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5. Large scale investigation of the effect of gender on mu rhythm suppression in motor imagery brain-computer interfaces.

Author

von Groll, Valentina Gamboa, Leeuwis, Nikki, Rimbert, Sébastien, Roc, Aline, Pillette, Léa, Lotte, Fabien, and Alimardani, Maryam

Subjects
MOTOR imagery (Cognition), BRAIN-computer interfaces, SAMPLE size (Statistics), GENDER, RHYTHM
Abstract

The utmost issue in Motor Imagery Brain-Computer Interfaces (MI-BCI) is the BCI poor performance known as 'BCI inefficiency'. Although past research has attempted to find a solution by investigating factors influencing users' MI-BCI performance, the issue persists. One of the factors that has been studied in relation to MI-BCI performance is gender. Research regarding the influence of gender on a user's ability to control MI-BCIs remains inconclusive, mainly due to the small sample size and unbalanced gender distribution in past studies. To address these issues and obtain reliable results, this study combined four MI-BCI datasets into one large dataset with 248 subjects and equal gender distribution. The datasets included EEG signals from healthy subjects from both gender groups who had executed a right- vs. left-hand motor imagery task following the Graz protocol. The analysis consisted of extracting the Mu Suppression Index from C3 and C4 electrodes and comparing the values between female and male participants. Unlike some of the previous findings which reported an advantage for female BCI users in modulating mu rhythm activity, our results did not show any significant difference between the Mu Suppression Index of both groups, indicating that gender may not be a predictive factor for BCI performance. [ABSTRACT FROM AUTHOR]

Published
2024
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6. DUPE MIBCI: Database with User’s Profile and EEG signals for Motor Imagery Brain Computer Interface research

Author

Dreyer, Pauline, Roc, Aline, Rimbert, Sébastien, Lotte, Fabien, Pillette, Léa, Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), 3D interaction with virtual environments using body and mind (Hybrid), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-RÉALITÉ VIRTUELLE, HUMAINS VIRTUELS, INTERACTIONS ET ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), BCI Society, and European Project: 714567 ,H2020 Pilier ERC,BrainConquest(2017)

Subjects
Database, [SCCO]Cognitive science, [INFO]Computer Science [cs], EEG, BCI, User profile
Abstract

International audience; We present and share a large database containing electroencephalographic signals from 87 human participants, collected during a single day of brain-computer interface (BCI) experiments, organized into 3 datasets (A, B, and C) that were all recorded using the same protocol: right and left hand motor imagery (MI). Each session contains 240 trials (120 per class), which in total represents more than 20,800 trials, or approximately 70 hours of recording time.It includes the performance of the associated BCI users, detailed information about the demographics, personality profile as well as some cognitive traits and the experimental instructions and codes (executed in the open-source platform OpenViBE).Such database could prove useful for various studies, including but not limited to: 1) studying the relationships between BCI users' profiles and their BCI performances, 2) studying how EEG signals properties varies for different users' profiles and MI tasks, 3) using the large number of participants to design cross-user BCI machine learning algorithms or 4) incorporating users' profile information into the design of EEG signal classification algorithms.

Published
2023

7. Does Gender Matter in Motor Imagery BCIs?

Author

Alimardani, Maryam, Gamboa von Groll, Valentina, Leeuwis, Nikki, Rimbert, Sébastien, Roc, Aline, Pillette, Léa, Lotte, Fabien, Tilburg University [Tilburg], Netspar, Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), 3D interaction with virtual environments using body and mind (Hybrid), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-RÉALITÉ VIRTUELLE, HUMAINS VIRTUELS, INTERACTIONS ET ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), BCI society, and European Project: 714567 ,H2020 Pilier ERC,BrainConquest(2017)

Subjects
[SCCO]Cognitive science, brain-computer interface, EEG, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC]
Abstract

International audience

Published
2023
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8. A large-scale study on the general public to assess and model the acceptability of BCIs dedicated to motor rehabilitation after stroke

Author

Grevet, Elise, Forge, Killyam, Tadiello, Sébastien, Izac, Margaux, Amadieu, Franck, Brunel, Lionel, Pillette, Léa, Py, Jacques, Gasq, David, Jeunet-Kelway, Camille, Institut de Neurosciences cognitives et intégratives d'Aquitaine (INCIA), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-SFR Bordeaux Neurosciences-Centre National de la Recherche Scientifique (CNRS), Cognition, Langues, Langage, Ergonomie (CLLE), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT), Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Cognition, Langues, Langage, Ergonomie (CLLE-LTC), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Dynamique des capacités humaines et des conduites de santé (EPSYLON), Université Paul-Valéry - Montpellier 3 (UPVM), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Centre Hospitalier Universitaire de Toulouse (CHU Toulouse), Toulouse Neuro Imaging Center (ToNIC), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Toulouse Mind & Brain Institut (TMBI), Université de Toulouse (UT)-Université Toulouse - Jean Jaurès (UT2J), BCI Society, and ANR-20-CE38-0008,ABCIS,Une Meilleure Acceptabilité pour une Meilleure Efficience des Procédures de Réhabilitation post-AVC basées sur les Interfaces Cerveau-Ordinateur (ICO)(2020)

Subjects
[SCCO.NEUR]Cognitive science/Neuroscience, [SDV.NEU.SC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Cognitive Sciences
Abstract

International audience; Introduction:While several meta-analyses have demonstrated the relevance of BCIs for improving motor recovery after stroke, these technologies are still barely used in clinical practice. We hypothesise that optimising the acceptability of BCIs could be used as a lever to increase the efficacy of BCI-based motor rehabilitation procedures, and thereby their clinical use. Indeed, a better acceptability will enable a reduction of anxiety levels and an enhancement of motivation and engagement in the procedure. Both will in turn allow for more cognitive resources to be allocated to the task, thus favouring learning and, ultimately, motor recovery. To the best of our knowledge, so far only one study has assessed the relevance of a BCI-based stroke rehabilitation procedure using acceptability measures among their primary criteria of efficacy. Most often, acceptability is only considered as an attribute of user satisfaction, itself being a dimension of user experience. Yet, given its potential impact on BCI use, it seems important to study acceptability as an integral component. Thus, our objective is to rely on validated models depicted in other literature in order to design a first thorough model of acceptability specifically dedicated to BCI-based procedures for motor rehabilitation after stroke. Material, Methods and Results: We designed a model of BCI acceptability based on the technology acceptance model 3 (TAM3) and on the unified theory of acceptance and use of technology 2 (UTAUT2) that assess acceptability in terms of perceived usefulness (PU), perceived ease of use (PEOU) and behavioural intention (BI). Then, we created a questionnaire based on our model in order to estimate its validity and to quantify the influence that each factor had on BCIacceptability. We collected a data sample of 753 respondents representative of the general public in France. We targeted the general public for two main reasons. (i) Due to the high prevalence of stroke, many of us are concerned, more or less directly, by stroke. (ii) Patients’ acceptability is likely to be influenced by the opinions and attitudes of their close relatives (who are part of the general public). Descriptive analyses: Our results suggest that BCIs are associated with high levels of acceptability: PU (8.28/10 ±1.57), PEOU (7.17/10 ±1.57), BI (8.23/10 ±1.69). Validity analyses: Cronbach’s α coefficient analyses revealed a satisfactory internal consistency of our questionnaire, i.e., the items used to measure each factor are mostly consistent and not too redundant (12/17 factors in [0.72;0.95], 4/17 in [0.50;0.62] and 1 = 0.97). Furthermore, a confirmatory factor analysis showed that the structure of the model is adequate. Quantification analyses: Regression analyses based on random forest algorithm revealed that BI is mainly driven by the PU of the system and by the perceived benefits on risk ratio associated with the technology. PU itself is mainly determined by the scientific relevance of BCIs and by PEOU. The main determinants of PEOU are ease of learning and playfulness.Discussion: Our results suggest that beyond the subjective norm (i.e., perceived opinions of our close ones), several factors impact significantly BCI acceptability. Given the weight of the scientific relevance and benefits/risk ratio, it is of utmost importance to clearly inform the population. In addition, the impact of playfulness and ease of learning should encourage us to adapt the rehabilitation procedures to each patient. In order to refine this model, additional data should be collected, in particular with i) patients and caregivers; ii) persons from different cultures; and iii) in different contexts, i.e. for other use cases.

Published
2023

9. What brain patterns should we reinforce during BCI training procedures targeting motor imagery abilities? An experimental study on athletes

Author

Izac, Margaux, Rossignol, Eléa, Pierrieau, Emeline, Pillette, Léa, Rienzo, Franck, Guillaud, Etienne, Guillot, Aymeric, Michelet, Thomas, N'Kaoua, Bernard, Jeunet-Kelway, Camille, Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Neurosciences cognitives et intégratives d'Aquitaine (INCIA), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-SFR Bordeaux Neurosciences-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Laboratoire Interuniversitaire de Biologie de la Motricité (LIBM ), Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])

Subjects
[SCCO]Cognitive science, Brain-Computer Interface, Event-Related Desynchronisation, [SCCO.NEUR]Cognitive science/Neuroscience, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Expertise, Motor Imagery, Neurofeedback, Sport
Abstract

International audience; Introduction:Motor imagery (MI) can be defined as a “dynamic state during which one simulates an action mentally without any body movement” [1]. The aim of MI is to optimise learning (e.g., in athletic training) or re-learning (e.g., in motor rehabilitation after stroke) by mastering the technique of new motor skills, but also through attentional focus [2] thanks to brain plasticity mechanisms. Indeed, similarities exist between MI and motor execution with regards to the solicitation of certain brain networks and regions, including premotor, parietal, and somatosensory regions [3]. Current BCI protocols targeting MI consist in positively reinforcing the maximum modulation of sensorimotor rhythms (SMRs) from baseline levels. This suggests that we consider that the growing expertise in the MI task will be associated with a higher desynchronisation of neurons in the sensorimotor cortices [4]. Yet, experiments investigating the neural efficiency hypothesis have shown that experts happen to have a reduced modulation of neural activity in comparison to novices [5], which can be attributed to a more efficient resource distribution. This efficiency would take form of reinforced temporal and spatial stability during MI tasks [6,7]. Thus, our questions are as follows: Q1. Does expertise modify the brain patterns associated with a MI task? Q2. If so, are those modifications elicited exclusively during MI of mastered movements? Or do these modifications reflect the acquisition of a generic skill (whatever the imagined movement)? Q3. Is maximising the percentage of desynchronisation a relevant objective? If not, what metrics of performance should be used in order to optimise the training of users/patients to self-regulate their brain activity? In order to investigate those different questions, we will recruit athletes who can be considered as an expert population in MI because of their frequent mental training use (to prevent overtraining or during rehabilitation but also as warm-up routines and rehearsal technique). Material and Methods:We will recruit 48 participants who will be divided into three groups: “basketball experts” (G1exp), “dance experts” (G2exp) and “novices” (G3) [16 participants per group, 8 men, 8 women]. All participants will perform 20 MI trials lasting 10s each for all four of the following movements: a simple reaching action (T1 – for which all participants are experts), a complex novel drawing task (T2 – for which none are expert), a basketball free throw (T3 – expertise of G1exp only) and a short pre-defined dancing choreography (T4 – expertise of G2exp only). EEG activity will be recorded during trials. This paradigm will enable us to compare MI-related brain patterns between experts and novices (G1exp/G2exp vs. G3) (Q1). It will also enable us to assess the extent to which the potential modifications of brain patterns due to expertise are specific to expert movements or generic (for G1exp and G2exp: T3 vs. T4; for all, control: T1 vs. T2) (Q2). From those results, we will investigate which performance metrics seem the most relevant to use in MI-based BCI/NF paradigms (Q3).Discussion:This study will enable us to acquire new knowledge regarding the neural efficiency hypothesis. If our results comfort this hypothesis, it will be important to identify, implement and evaluate new metrics of performance to guide BCI/NF users during their training (i.e., instead of ERD%). Following the neural efficiency hypothesis, we expect experts to show brain patterns that are more spatially and temporally stable than those of novices [6,7]. In other terms, in experts, we expect the modulations of brain activity during MI to be circumscribed to sensorimotor cortices (provided that they perform kinaesthetic MI) and to be highly stable across trials in terms of location and frequency. In addition, we hypothesise that those modulations will represent a general skill. In other terms, we expect the same patterns to be elicited for experts when doing MI of a mastered technique from their discipline, but also when doing MI of a novel movement of similar nature (in our case a different physical activity). Indeed, we believe that a transfer of neural efficiency exists. However, this phenomenon might only happen to a certain extent and not be identifiable for MI of a complex novel task or of a simple everyday life action. To our knowledge, this last hypothesis hasn’t been tested in the current literature and will therefore require an exploratory approach.References:[1] Guillot, A. and Collet, C. (2008). Construction of the Motor Imagery Integrative Model in Sport: A review and theoretical investigation of motor imagery use. IRSEP 1, 31–44[2] Budnik-Przybylska, D. et al. (2021). Neural Oscillation During Mental Imagery in Sport: An Olympic Sailor Case Study. Frontiers in Human Neuroscience 15, 669422.[3] Hardwick, R. M. et al. (2018). Neural Correlates of Action: Comparing Meta-Analyses of Imagery, Observation, and Execution. Neuroscience & Biobehavioral Reviews 94, 31‑44.[4] Ono, T. et al. (2013). Daily Training with Realistic Visual Feedback Improves Reproducibility of Event-Related Desynchronisation Following Hand Motor Imagery. Clinical Neurophysiology 124(9), 1779 86.[5] Li, L. and Smith, D. M. (2021). Neural Efficiency in Athletes: A Systematic Review. Frontiers in Behavioral Neuroscience 15.[6] Del Percio, C. et al. (2009). “Neural Efficiency” of Athletes’ Brain for Upright Standing: A High-Resolution EEG Study. Brain Research Bulletin 79(3), 193‑200.[7] Kraeutner, S. N. et al. (2018). Experience Modulates Motor Imagery-Based Brain Activity. EJN 47, no. 10: 1221–29.

Published
2023

10. Acceptability of BCI-based procedures for motor rehabilitation after stroke: A questionnaire study among patients

Author

Grevet, Elise, Izac, Margaux, Pillette, Léa, Py, Jacques, Amadieu, F., Gasq, D., Jeunet-Kelway, Camille, Institut de Neurosciences cognitives et intégratives d'Aquitaine (INCIA), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-SFR Bordeaux Neurosciences-Centre National de la Recherche Scientifique (CNRS), 3D interaction with virtual environments using body and mind (Hybrid), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-RÉALITÉ VIRTUELLE, HUMAINS VIRTUELS, INTERACTIONS ET ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Cognition, Langues, Langage, Ergonomie (CLLE), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT), Imagerie cérébrale et handicaps neurologiques (ICHN), Institut des sciences du cerveau de Toulouse. (ISCT), Université de Toulouse (UT)-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre Hospitalier Universitaire de Toulouse (CHU Toulouse), CORTICO, and ANR-20-CE38-0008,ABCIS,Une Meilleure Acceptabilité pour une Meilleure Efficience des Procédures de Réhabilitation post-AVC basées sur les Interfaces Cerveau-Ordinateur (ICO)(2020)

Subjects
[SCCO]Cognitive science, Acceptability, Questionnaire, [SDV]Life Sciences [q-bio], Stroke Rehabilitation, Neurofeedback, Model
Abstract

International audience; Introduction: Stroke leaves around 40% of surviving patients dependent in their activities, notably due to severe motor disabilities[1]. BCIs have been shown to favour motor recovery after stroke [2], but this efficiency has not reached yet the level required to achieve a clinical usage. We hypothesise that improving BCI acceptability, notably by personalising BCI-based rehabilitation procedures to each patient, will reduce anxiety and favour engagement in the rehabilitation process, thereby increasing the efficiency of those procedures. To test this hypothesis, we need to understand how to adapt BCI procedures to each patient depending on their profile. Thus, we constructed a model of BCI acceptability based on the literature [3], adapted it in a questionnaire, and distributed the latter to post-stroke patients (N=140). Methods: The questionnaire consisted of i) 3 target factors used as a proxy of BCI acceptability, namely the perceived usefulness (PU), perceived ease of use (PEoU) intention to use (IU) and ii) 23 explanatory factors that could influence acceptability. First, k-mean clustering analyses were performed to identify different profiles of patients. Then, for each cluster, elastic net regressions were used to identify the explanatory factors that predicted PU, PEoU and IU the best, i.e., to identify the factors that are the most important to personalise for each patient. Results: Five clusters (c1 to c5) were identified. The regression analyses indicated that the following factors had to be considered: (c1 & c5) "scientific relevance" & "ease of learning"; (c2) "benefits/risks ratio", "ease of learning", "visual aesthetic" & "result demonstrability"; (c3) "scientific relevance" & "benefits/risks ratio";(c4) none. Perspectives: We will use those results in a clinical study to personalise the BCI procedures to each patient. We expect lower anxiety and better motivation, acceptability and motor recovery with this personalised setting than with a standard one. Sources [1] Inserm, 2019 [2] Cervera, María A., et al. "Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis."

Published
2023

11. Single trial classification of the level of presence in VR

Author

Savalle, Emile, Pillette, Léa, Argelaguet Sanz, Ferran, Lécuyer, Anatole, Won, Kyung-Ho, Macé, Marc J.-M., 3D interaction with virtual environments using body and mind (Hybrid), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-RÉALITÉ VIRTUELLE, HUMAINS VIRTUELS, INTERACTIONS ET ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)

Subjects
Brain-Computer Interface, Réalité virtuelle, Immersion, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Virtual reality, [INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR], Interface cerveau-ordinateur
Abstract

National audience; Virtual reality (VR) relies heavily on the sense of presence - the feeling of being physically present in a virtual environment (VE). Currently, presence is mainly assessed through subjective questionnaires, which cannot be completed while users experience VR as it would disrupt their immersion. To address this limitation, we propose to explore the feasibility of using electroencephalography (EEG) to monitor users' level of presence and classify it based on the reaction to oddball stimuli.

Published
2023

12. A questionnaire to identify the links between athletes’ profiles and cognitive training practice: Heading for personalised neurofeedback procedures

Author

Izac, Margaux, Rossignol, Eléa, Pillette, Léa, Guillot, Aymeric, Guillaud, Etienne, Rienzo, Franck Di, Michelet, Thomas, N’kaoua, Bernard, Jeunet-Kelway, Camille, Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Neurosciences cognitives et intégratives d'Aquitaine (INCIA), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-SFR Bordeaux Neurosciences-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), 3D interaction with virtual environments using body and mind (Hybrid), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-RÉALITÉ VIRTUELLE, HUMAINS VIRTUELS, INTERACTIONS ET ROBOTIQUE (IRISA-D5), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Laboratoire Interuniversitaire de Biologie de la Motricité (LIBM ), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), Marie-Constance Corsi, Sylvain Chevallier, Camille Jeunet-Kelway, and Raphaëlle Roy

Subjects
[SCCO]Cognitive science, Brain-Computer Interface, [SCCO.NEUR]Cognitive science/Neuroscience, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Motor Imagery, Neurofeedback, Sport
Abstract

International audience; Motor imagery (MI) is widely used in sports, in particular for motor learning and anxiety management [1]. Indeed, athletes consistently report using MI for rehearsing technique, maintaining arousal or enhancing self-confidence [2]. However, optimal gains are limited by the fact that they receive no feedback and cannot assess their performance. This can have detrimental effects on their motivation to diligently practice MI. Therefore, using Neurofeedback (NF) appears to be an adapted solution, allowing athletes to know if they are correctly performing the task. This addition also makes it possible to objectify performance and quantify progression throughout the sessions. In fact, three recent meta-analysis testify that NF improves the ability to self-regulate brain activity and sport performance [3, 4, 5]. The number of papers using MI-NF protocols has increased in the past few years. However, their efficiency has been shown to depend on various factors such as expertise in the imagined task [6], personality traits [7], MI ability [8] or NF acceptability [9]. Some of these factors can be assessed using validated questionnaires [10,11,12] but items are generally focused on questioning the current use of MI or thoughts on NF. An issue, as we know that practice can sometimes differ from real needs and expectations. To our knowledge, no previous work has investigated sports field actors’ preferences with the aim of designing specific NF tools, perfectly fitted to their profile, goals and expectations. Therefore, based on pre-existing questionnaires, we will build and broadcast an online questionnaire for athletes where personality, MI use, NF acceptability and NF preferences will be assessed. This method will enable us to personalise MI-NF trainings to fit each athlete’s needs. A longitudinal study will then require that athletes follow a personalised procedure to investigate how it impacts their MI and NF performances as well as their acceptability levels.[1] Guillot, A., & Collet, C. (2008). Construction of the Motor Imagery Integrative Model in Sport : A review and theoretical investigation of motor imagery use. International Review of Sport and Exercise Psychology, 1.[2] Munroe, K. J., Giacobbi Jr., P. R., Hall, C., & Weinberg, R. (2000). The Four Ws of Imagery Use : Where, When, Why, and What. Sport Psychologist, 14(2), 119.[3] Mirifar, A., Beckmann, J., & Ehrlenspiel, F. (2017). Neurofeedback as supplementary training for optimizing athletes’ performance: A systematic review with implications for future research. Neuroscience & Biobehavioral Reviews, 75, 419 432. [4] Xiang, M.-Q., Hou, X.-H., Liao, B.-G., Liao, J.-W., & Hu, M. (2018). The effect of neurofeedback training for sport performance in athletes: A meta-analysis. Psychology of Sport and Exercise, 36, 114 122.[5] Gong, A., Gu, F., Nan, W., Qu, Y., Jiang, C., & Fu, Y. (2021). A Review of Neurofeedback Training for Improving Sport Performance From the Perspective of User Experience. Frontiers in Neuroscience, 15.[6] Kraeutner, S. N., McWhinney, S. R., Solomon, J. P., Dithurbide, L., & Boe, S. G. (2018). Experience modulates motor imagery-based brain activity. European Journal of Neuroscience, 47(10), 1221 1229.[7] Jeunet, C., N’Kaoua, B., Subramanian, S., Hachet, M., & Lotte, F. (2015). Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns. PLOS ONE, 10(12), e0143962.[8] Guillot, A., Collet, C., Nguyen, V. A., Malouin, F., Richards, C., & Doyon, J. (2008). Functional neuroanatomical networks associated with expertise in motor imagery. NeuroImage. [9] Morone, G., Pisotta, I., Pichiorri, F., Kleih, S., Paolucci, S., Molinari, M., Cincotti, F., Kübler, A., & Mattia, D. (2015). Proof of principle of a brain-computer interface approach to support poststroke arm rehabilitation in hospitalized patients: Design, acceptability, and usability. Archives of Physical Medicine and Rehabilitation, 96(3 Suppl), S71-78. [10] Hall, C. R., Rodgers, W. M., & Barr, K. A. (1990). The Use of Imagery by Athletes in Selected Sports. The Sport Psychologist, 4(1), 1 10.[11] Plaisant, O., Courtois, R., Réveillère, C., Mendelsohn, G. A., & John, O. P. (2010). Validation par analyse factorielle du Big Five Inventory français (BFI-Fr). Analyse convergente avec le NEO-PI-R. Annales Médico-psychologiques, revue psychiatrique, 168(2), 97 106. [12] Grevet, E., Forge, K., Tadiello, S., Izac, M., Amadieu, F., Brunel, L., Pillette, L., Py, J., Gasq, D., & Jeunet-Kelway, C. (2023). Modeling the acceptability of BCIs for motor rehabilitation after stroke: A large scale study on the general public. Frontiers in Neuroergonomics, 3.

Published
2023

13. Which sensations should you imagine?

Author

Le Jeune, François, Driessens, Léa, Savalle, Emile, Pillette, Léa, 3D interaction with virtual environments using body and mind (Hybrid), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-RÉALITÉ VIRTUELLE, HUMAINS VIRTUELS, INTERACTIONS ET ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Institut National de Recherche en Informatique et en Automatique (Inria), and Ce travail est financé par le CNRS et Inria Bretagne Atlantique.

Subjects
Brain-Computer Interfaces, [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Instructions, Sensations
Abstract

International audience; During motor imagery-based brain computer interfaces (MI-BCI) training, users are most often instructed to perform kinesthetic motor imagery, i.e., imagine the sensations related to a movement [1]. However, there is a great variety of sensations associated with motor imagery that can either be interoceptive or exteroceptive [2]. Interoceptive sensations related to movement arise from within the body, such as muscle contraction, whereas exteroceptive sensations are sensed through the skin, such as touch or vibration. Among those different sensations, we do not know which to advise MI-BCI users to imagine [3].Thus, our experiment aims at studying the influence of imagining sensations on neurophysiological activity and user experience. It will consist in a two hours session during which participants (thirty expected) will be equipped with an electroencephalographic headset while performing five MI tasks: interoceptive, exteroceptive and both, with either a unique or multiple exteroceptive sensations, i.e., pressure and vibration (Fig. 1). After all MI conditions, participants will be asked to fill a user-experience questionnaire [4]. The kinesthetic and visual imagery abilities of participants will also be considered in our analyses [5].Based on previous results, we expect that imagining both exteroceptive and interoceptive sensations leads to a stronger desynchronization over the sensorimotor cortex [6] than imagining interoceptive sensations only [7, 8], in turn stronger than imagining exteroceptive sensations only. Imagining several exteroceptive sensations should also enable a stronger desynchronization than imagining only one [9]. We also expect a correlation between participants’ kinesthetic and visual imagery abilities, their neurophysiological results [10, 11] and their preferences.Our results should provide useful insights on which instructions to give to participants during MI-BCI user training and potentially how to adapt them to the participants’ profile.

Published
2023

14. Are Brain-Computer Interfaces and Neurofeedback acceptable?

Author

Pillette, Léa, Grevet, Elise, Dussard, Claire, Amadieu, Franck, Gasq, David, Pierrieau, Emeline, Py, Jacques, Si-Mohammed, Hakim, George, Nathalie, Jeunet, Camille, Pillette, Léa, Institut de Neurosciences cognitives et intégratives d'Aquitaine (INCIA), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-SFR Bordeaux Neurosciences-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Cognition, Langues, Langage, Ergonomie (CLLE), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Toulouse - Jean Jaurès (UT2J)-Centre National de la Recherche Scientifique (CNRS), INSERM, Univ. Toulouse 3, Université de Sherbrooke (UdeS), Université Toulouse - Jean Jaurès (UT2J), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-SFR Bordeaux Neurosciences-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Toulouse Mind & Brain Institut (TMBI), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT), and Université Toulouse III - Paul Sabatier (UT3)

Subjects
[SCCO]Cognitive science, Acceptability, Brain-Computer Interfaces, [SCCO] Cognitive science, Neurofeedback
Abstract

International audience; The adoption of a technology can be defined as the decision made by a person or a society to accept and use it. It is based on two phases during which the willingness to use that technology is assessed. The first phase corresponds to the evaluation of the acceptability of the technology and takes place before the use while the second phase, the technology acceptance, comes afterwards. Assessing the acceptability of BCIs/NF is of utmost importance as it could provide i) insights on the propensity of people to use these technologies and ii) leads to facilitate their adoption.We reviewed the literature dealing with the acceptability of BCIs/NF and using validated questionnaires and models. Through a reproducible search method, we retrieved a list of 55 main references of acceptability questionnaires and models. Then, we searched Scopus, PubMed, and Web of Science to identify the articles in the fields of BCIs/NF that cited these references. We included in our review all the articles reporting experimental results on the acceptability of BCIs/NF that were based on one or several validated acceptability questionnaire(s).Four articles were included in our review. Two of them assessed the acceptability while the others assessed the acceptance of the BCIs. While there are too few articles included to conclude on the acceptability of those technologies, the results are encouraging. BCIs were perceived as useful at 78% by motor impaired individuals who used BCIs for pain management and at 84% by neurotypical individuals who used BCIs to browse an instruction manual. The perceived ease of use varied from 30% to 80%, which could be related to differences in the protocols. Among other factors, the presence of social support appeared to influence the acceptability.

Published
2022

15. Unveiling the role of beta activity in motor motivation: an EEG study of effort using neurofeedback and pupillometry

Author

Pierrieau, Emeline, Pillette, Léa, Dussard, Claire, George, Nathalie, Jeunet, Camille, Institut de Neurosciences cognitives et intégratives d'Aquitaine (INCIA), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-SFR Bordeaux Neurosciences-Centre National de la Recherche Scientifique (CNRS), 3D interaction with virtual environments using body and mind (Hybrid), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-RÉALITÉ VIRTUELLE, HUMAINS VIRTUELS, INTERACTIONS ET ROBOTIQUE (IRISA-D5), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SCCO.NEUR]Cognitive science/Neuroscience, [SDV.NEU.NB]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Neurobiology, Effort, Beta power, EEG, Neurofeedback, Motor vigor
Abstract

International audience; A progressive desynchronization of beta-band power (β-desync; 13-30 Hz) has been consistently observed before movement initiation. Although mounting evidence demonstrate a significant relationship between this β-desync and subsequent motor vigor, a neurophysiological foundation to explain this finding is still lacking. In the present study, we proposed that this association might come from changes in motor motivation and perceived effort. This hypothesis will be tested by recording the signal from electroencephalography and using it as neurofeedback to decrease or increase β-desync in two distinct sessions, in an intra-subject and double-blinded design. First, participants will be screened based on their ability to up- and down-regulate their β-desync. The selected participants will then perform the experimental test, in which each trial comprised a neurofeedback phase directly followed by an effort phase. The effort will consist in squeezing a dynamometer with the right hand during an allocated time of 10 seconds. Participants will be asked to apply enough force to cross a defined threshold, and that the more time the exerted force would exceed the threshold, the more money they would earn. Subjective perception of effort will be evaluated afterwards using an analog scale. Because effort exertion is associated with increased pupil dilation, which is itself modulated by noradrenergic activity, we will further investigate the influence of noradrenergic activity on changes in β-desync due to neurofeedback and effort exertion. Besides the contribution to unraveling the neurophysiological and functional bases of beta activity, the present work is of special interest to the study of Parkinson’s disease by helping to better understand the association between beta activity and motor symptoms.

Published
2022

16. Can feedback transparency improve Motor-Imagery BCI performance?

Author

Dussard, Claire, Pillette, Léa, Jeunet, Camille, George, Nathalie, Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, Inserm, CNRS, APHP, Hôpital de la Pitié Salpêtrière, Paris, France, Institut de Neurosciences cognitives et intégratives d'Aquitaine (INCIA), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-SFR Bordeaux Neurosciences-Centre National de la Recherche Scientifique (CNRS), and Dussard, Claire

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, feedback transparency, [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, agency, neurofeedback, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], embodiment
Abstract

International audience; Motor Imagery-based BCI (MI-BCI) performances remain highly variable, with up to 30% of users unable to achieve control. Operating a BCI system requires users to learn to modulate their neural activity. This is a complex task, thus the training procedure is crucial. Our study draws on task-feedback transparency to improve the feedback given during training. MI-BCI protocols often feature abstract feedback (e.g., visual gauge) unrelated to the imagined movement. Transparent feedback, directly related to the task, may reinforce agency-the ability to self-attribute actions, such as feedback movements-and embodiment, facilitating kinesthetic MI. This could lessen complexity in BCI control and ultimately improve BCI performance.We tested this hypothesis in a beta-based MI neurofeedback task with electroencephalography (EEG). Twenty-three subjects participated in a single MIneurofeedback session that consisted in imagining clenching their right hand rhythmically. Feedback was based on online laplacian-filtered 8-30Hz power on C3 electrode computed with OpenViBE. There were 3 feedback conditions of expected increasing transparency: pendulum; virtual hand; virtual hand with additional motor illusion induced by vibrations to the participant's hand. The amplitude of the pendulum and virtual hand movements was proportional to beta desynchronisation. Each participant performed two runs of five 24s-trials per condition. Agency, embodiment, and user experience questionnaires were included. Repeated-measures ANOVA showed better BCI performance when using the virtual hand than the pendulum. However, participants had lower performance in the visuotactile condition compared to the virtual hand-only feedback. Agency showed a similar pattern.Feeling of success in the task did not vary with feedback conditions, but the virtual hand feedback felt easier to use. BCI performance correlated positively with agency for all conditions and with embodiment in conditions with a virtual hand.Thus, visual transparency—but not tactile feedback addition—increased BCI performance and this was associated with better agency and embodiment.

Published
2022

18. Long-Term BCI Training of a Tetraplegic User: Adaptive Riemannian Classifiers and User Training

Author

Benaroch, Camille, Sadatnejad, Khadijeh, Roc, Aline, Appriou, Aurélien, Monseigne, Thibaut, Pramij, Smeety, Mladenović, Jelena, Pillette, Léa, Jeunet, Camille, Lotte, Fabien, Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Cognition, Langues, Langage, Ergonomie (CLLE), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT), The European Research Council with project BrainConquest (grant ERC-2016-STG-714567) and the French National Research Agency with project REBEL (grant ANR-15-CE23-0013-01) supported this work., ANR-15-CE23-0013,REBEL,Redéfinir les Interfaces Cerveau-Ordinateur pour permettre à leurs utilisateurs d'en maitriser le contrôle(2015), European Project: 714567 ,H2020 Pilier ERC,BrainConquest(2017), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, and Centre National de la Recherche Scientifique (CNRS)-École pratique des hautes études (EPHE)

Subjects
User experience, Riemannian classification, Learning metrics, [SCCO.NEUR]Cognitive science/Neuroscience, Tetraplegic or quadriplegic people, Electroencaphlography, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Adaptive classification, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [SCCO.PSYC]Cognitive science/Psychology, [INFO.INFO-ET]Computer Science [cs]/Emerging Technologies [cs.ET], [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Brain computer interface, User training
Abstract

International audience; While often presented as promising assistive technologies for motor-impaired users, electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) remain barely used outside laboratories due to low reliability in real-life conditions. There is thus a need to design long-term reliable BCIs that can be used outside-of-the-lab by end-users, e.g., severely motor-impaired ones. Therefore, we propose and evaluate the design of a multi-class Mental Task (MT)-based BCI for longitudinal training (20 sessions over 3 months) of a tetraplegic user for the CYBATHLON BCI series 2019. In this BCI championship, tetraplegic pilots are mentally driving a virtual car in a racing video game. We aimed at combining a progressive user MT-BCI training with a newly designed machine learning pipeline based on adaptive Riemannian classifiers shown to be promising for real-life applications. We followed a two step training process: the first 11 sessions served to train the user to control a 2-class MT-BCIby performing either two cognitive tasks (REST and MENTAL SUBTRACTION) or two motor-imagery tasks (LEFT-HAND and RIGHT-HAND). The second training step (9 remaining sessions) applied an adaptive, session-independent Riemannian classifier that combined all 4 MT classes used before. Moreover, as our Riemannian classifier was incrementally updated in an unsupervised way it would capture both within and between-session non-stationarity. Experimental evidences confirm the effectiveness of this approach. Namely, the classification accuracy improved by about 30% at the end of the training compared to initial sessions. We also studied the neural correlates of this performance improvement. Using a newly proposed BCI user learning metric, we could show our user learned to improve his BCI control by producing EEG signals matching increasingly more the BCI classifier training data distribution, rather than by improving his EEG class discrimination. However, the resulting improvementwas effective only on synchronous (cue-based) BCI and it did not translate into improved CYBATHLON BCI game performances. For the sake of overcoming this in the future, we unveil possible reasons for these limited gaming performances and identify a number of promising future research directions. Importantly, we also report on the evolution of the user’s neurophysiological patterns and user experience throughout the BCI training and competition.

Published
2021
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19. Redefining and Adapting Feedback for Mental-Imagery based Brain-Computer Interface User Training to the Learners’ Traits and States

Author

Pillette, Léa, Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Bordeaux, Fabien Lotte, Bernard N'Kaoua, Lotte, Fabien, N'Kaoua, Bernard, Mattia, Donatella, Pelachaud, Catherine, Lécuyer, Anatole, Coyle, Damien, and STAR, ABES

Subjects
Post-stroke motor rehabilitation, Interface Cerveau-Ordinateur, Brain-Computer Interface, Réhabilitation motrice post-AVC, Mental imagery, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], Imagerie mentale, Modalité de feedback, Modalities of feedback, Feedback, Emotional Feedback and social presence, Feedback émotionnel et présence sociale, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], Attention, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC]
Abstract

Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) present new opportunities to interact with digital technologies, such as neuroprostheses or videogames, only by performing mental imagery tasks, such as imagining an object rotating. The recognition of the command for the system is based on the analysis of the brain activity of the user. The users must learn to produce brain activity patterns that are recognizable by the system in order to control BCIs. However, current training protocols do not enable 10 to 30% of persons to acquire the skills required to use BCIs. The lack of robustness of BCIs limit the development of the technology outside of research laboratories. This thesis aims at investigating how the feedback provided throughout the training can be improved and adapted to the traits and states of the users. First, we investigate the role that feedback is currently given in MI-BCI applications and training protocols. We also analyse the theories and experimental contributions discussing its role and usefulness. Then, we review the different feedback that have been used to train MI-BCI users. We focus on three main characteristics of feedback, i.e., its content, its modality of presentation and finally its timing. For each of these characteristics, we reviewed the literature to assess which types of feedback have been tested and what is their impact on the training. We also analysed which traits or states of the learners were shown to influence BCI training outcome. Based on these reviews of the literature, we hypothesised that different characteristics of feedback could be leveraged to improve the training of the learners depending on either traits or states. We reported the results of our experimental contributions for each of the characteristics of feedback. Finally, we presented different recommendations and challenges regarding each characteristic of feedback. Potential solutions were proposed to meet these recommendations in the future., Les interfaces cerveau-ordinateur basées sur l’imagerie mentale (MI-BCIs) offrent de nouvelles possibilités d’interaction avec les technologies numériques, telles que les neuroprothèses ou les jeux vidéo, uniquement en effectuant des tâches d’imagerie mentale, telles qu’imaginer d’un objet en rotation. La reconnaissance de la commande envoyée au système par l’utilisateur repose sur l’analyse de l’activité cérébrale de ce dernier. Les utilisateurs doivent apprendre à produire des patterns d’activité cérébrale reconnaissables par le système afin de contrôler les MI-BCIs. Cependant, les protocoles de formation actuels ne permettent pas à 10 à 30 % des personnes d’acquérir les compétences nécessaires pour utiliser les MI-BCIs. Ce manque de fiabilité des BCIs limite le développement de la technologie en dehors des laboratoires de recherche. Cette thèse a pour objectif d’examiner comment le feedback fourni tout au longde la formation peut être amélioré et adapté aux traits et aux états des utilisateurs. Dans un premier temps, nous examinons le rôle qui est actuellement donné au feedback dans les applications et les protocoles d’entraînement à l’utilisation des MI-BCIs. Nous analysons également les théories et les contributions expérimentales discutant de son rôle et de son utilité dans le processus d’apprentissage de contrôle de correlats neurophysiologiques. Ensuite, nous fournissons une analyse de l’utilité de différents feedback pour l’entraînement à l’utilisation des MI-BCIs. Nous nous concentrons sur trois caractéristiques principales du feedback, i.e., son contenu, sa modalité de présentation et enfin sa dimension temporelle. Pour chacune de ces caractéristiques, nous avons examiné la littérature afin d’évaluer quels types de feedback ont été testés et quel impact ils semblent avoir sur l’entraînement. Nous avons également analysé quels traits ou états des apprenants influaient sur les résultats de cet entraînement. En nous basant sur ces analyses de la littérature, nous avons émis l’hypothèse que différentes caractéristiques du feedback pourraient être exploitées afin d’améliorer l’entraînement en fonction des traits ou états des apprenants. Nous rapportons les résultats de nos contributions expérimentales pour chacune des caractéristiques du feedback. Enfin, nous présentons différentes recommandations et défis concernant chaque caractéristique du feedback. Des solutions potentielles sont proposées pour à l’avenir surmonter ces défis et répondre à ces recommandations.

Published
2019

20. Inria Research & Development for the Cybathlon BCI series

Author

Lotte, Fabien, Clerc, Maureen, Appriou, Aurélien, Audino, Amandine, Benaroch, Camille, Giacalone, Pierre, Jeunet, Camille, Mladenović, Jelena, Monseigne, Thibaut, Papadopoulo, Théodore, Pillette, Léa, Roc, Aline, Sadatnejad, Khadijeh, Turi, Federica, Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Computational Imaging of the Central Nervous System (ATHENA), Inria Sophia Antipolis - Méditerranée (CRISAM), Cognition, Langues, Langage, Ergonomie (CLLE), Centre National de la Recherche Scientifique (CNRS)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Toulouse - Jean Jaurès (UT2J), ANR-15-CE23-0013,REBEL,Redéfinir les Interfaces Cerveau-Ordinateur pour permettre à leurs utilisateurs d'en maitriser le contrôle(2015), European Project: 714567 ,H2020 Pilier ERC,BrainConquest(2017), Lotte, Fabien, Interactions humain-machine, objets connectés, contenus numériques, données massives et connaissance - Redéfinir les Interfaces Cerveau-Ordinateur pour permettre à leurs utilisateurs d'en maitriser le contrôle - - REBEL2015 - ANR-15-CE23-0013 - AAPG2015 - VALID, Boosting Brain-Computer Communication with high Quality User Training - BrainConquest - - H2020 Pilier ERC2017-07-01 - 2022-06-30 - 714567 - VALID, Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, École Pratique des Hautes Études (EPHE), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS)-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), and Université de Toulouse (UT)

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [SCCO]Cognitive science, [INFO.INFO-BT] Computer Science [cs]/Biotechnology, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG], [SCCO] Cognitive science, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-BT]Computer Science [cs]/Biotechnology, ComputingMilieux_MISCELLANEOUS, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience

Published
2019

21. Impact of MI-BCI feedback for post-stroke and neurotypical people

Author

Pillette, Léa, Glize, Bertrand, N'Kaoua, Bernard, Joseph, Pierre-Alain, Jeunet, Camille, Lotte, Fabien, Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire Handicap Activité Cognition et Système Nerveux (HACS), Université de Bordeaux (UB), Service de Médecine Physique et Réadaptation, CHU Bordeaux [Bordeaux], Cognition, Langues, Langage, Ergonomie (CLLE-LTC), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), ANR-15-CE23-0013,REBEL,Redéfinir les Interfaces Cerveau-Ordinateur pour permettre à leurs utilisateurs d'en maitriser le contrôle(2015), European Project: 714567 ,H2020 Pilier ERC,BrainConquest(2017), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université Toulouse - Jean Jaurès (UT2J)-Centre National de la Recherche Scientifique (CNRS), and Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)

Subjects
[INFO.INFO-CY]Computer Science [cs]/Computers and Society [cs.CY], [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.PSYC]Cognitive science/Psychology, [SCCO.COMP]Cognitive science/Computer science, [INFO.EIAH]Computer Science [cs]/Technology for Human Learning, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2019

22. Do experimenters have an influence on MI-BCI user training?

Author

Roc, Aline, Pillette, Léa, N'Kaoua, Bernard, Lotte, Fabien, Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Bordeaux (UB), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), ANR-15-CE23-0013,REBEL,Redéfinir les Interfaces Cerveau-Ordinateur pour permettre à leurs utilisateurs d'en maitriser le contrôle(2015), European Project: 714567 ,H2020 Pilier ERC,BrainConquest(2017), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2019

23. BCPy, an open-source python platform for offline EEG signals decoding and analysis

Author

Appriou, Aurélien, Pillette, Léa, Cichocki, Andrzej, Lotte, Fabien, Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), RIKEN Center for Brain Science [Wako] (RIKEN CBS), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), Skolkovo Institute of Science and Technology [Moscow] (Skoltech), Japanese Society for the Promotion of Science and the European Research Council (grant ERC-2016-STG-714567), and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest

Subjects
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-SE]Computer Science [cs]/Software Engineering [cs.SE], ComputingMilieux_MISCELLANEOUS, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience

Published
2018

24. Classification of attention types in EEG signals

Author

Pillette, Léa, Appriou, Aurélien, Cichocki, Andrzej, N'Kaoua, Bernard, Lotte, Fabien, Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), RIKEN Center for Brain Science [Wako] (RIKEN CBS), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), Skolkovo Institute of Science and Technology [Moscow] (Skoltech), Université de Bordeaux (UB), and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest

Subjects
[SCCO]Cognitive science, ACM: D.: Software/D.2: SOFTWARE ENGINEERING/D.2.2: Design Tools and Techniques/D.2.2.12: User interfaces, [SCCO.NEUR]Cognitive science/Neuroscience, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], ComputingMilieux_MISCELLANEOUS, ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.6: Learning
Abstract

International audience

Published
2018

25. Multi-Session Influence of Two Modalities of Feedback and Their Order of Presentation on MI-BCI User Training.

Author

Pillette, Léa, N'Kaoua, Bernard, Sabau, Romain, Glize, Bertrand, and Lotte, Fabien

Subjects
BRAIN-computer interfaces, DIGITAL technology, MOTOR imagery (Cognition), NEUROPROSTHESES, VIBROTACTILE stimulation
Abstract

By performing motor-imagery tasks, for example, imagining hand movements, Motor-Imagery based Brain-Computer Interfaces (MI-BCIs) users can control digital technologies, for example, neuroprosthesis, using their brain activity only. MI-BCI users need to train, usually using a unimodal visual feedback, to produce brain activity patterns that are recognizable by the system. The literature indicates that multimodal vibrotactile and visual feedback is more effective than unimodal visual feedback, at least for short term training. However, the multi-session influence of such multimodal feedback on MI-BCI user training remained unknown, so did the influence of the order of presentation of the feedback modalities. In our experiment, 16 participants trained to control a MI-BCI during five sessions with a realistic visual feedback and five others with both a realistic visual feedback and a vibrotactile one. training benefits from a multimodal feedback, in terms of performances and self-reported mindfulness. There is also a significant influence of the order presentation of the modality. Participants who started training with a visual feedback had higher performances than those who started training with a multimodal feedback. We recommend taking into account the order of presentation for future experiments assessing the influence of several modalities of feedback. [ABSTRACT FROM AUTHOR]

Published
2021
Full Text
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26. BioPyC, an Open-Source Python Toolbox for Offline Electroencephalographic and Physiological Signals Classification.

Author

Appriou, Aurélien, Pillette, Léa, Trocellier, David, Dutartre, Dan, Cichocki, Andrzej, and Lotte, Fabien

Subjects
*SIGNAL classification, *ELECTROENCEPHALOGRAPHY, *BIOMEDICAL signal processing, *PYTHON programming language, *SIGNAL processing, *BRAIN-computer interfaces, *HEART beat
Abstract

Research on brain–computer interfaces (BCIs) has become more democratic in recent decades, and experiments using electroencephalography (EEG)-based BCIs has dramatically increased. The variety of protocol designs and the growing interest in physiological computing require parallel improvements in processing and classification of both EEG signals and bio signals, such as electrodermal activity (EDA), heart rate (HR) or breathing. If some EEG-based analysis tools are already available for online BCIs with a number of online BCI platforms (e.g., BCI2000 or OpenViBE), it remains crucial to perform offline analyses in order to design, select, tune, validate and test algorithms before using them online. Moreover, studying and comparing those algorithms usually requires expertise in programming, signal processing and machine learning, whereas numerous BCI researchers come from other backgrounds with limited or no training in such skills. Finally, existing BCI toolboxes are focused on EEG and other brain signals but usually do not include processing tools for other bio signals. Therefore, in this paper, we describe BioPyC, a free, open-source and easy-to-use Python platform for offline EEG and biosignal processing and classification. Based on an intuitive and well-guided graphical interface, four main modules allow the user to follow the standard steps of the BCI process without any programming skills: (1) reading different neurophysiological signal data formats, (2) filtering and representing EEG and bio signals, (3) classifying them, and (4) visualizing and performing statistical tests on the results. We illustrate BioPyC use on four studies, namely classifying mental tasks, the cognitive workload, emotions and attention states from EEG signals. [ABSTRACT FROM AUTHOR]

Published
2021
Full Text
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27. Experimenters Influence on Mental-Imagery based Brain-Computer Interface User Training

Author

Léa Pillette, Fabien Lotte, Aline Roc, Bernard N'Kaoua, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Popular interaction with 3d content (Potioc), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Handicap Activité Cognition Santé [Bordeaux] (HACS), Université de Bordeaux (UB)-Institut National de Recherche en Informatique et en Automatique (Inria)-CHU Bordeaux [Bordeaux]-Institut National de la Santé et de la Recherche Médicale (INSERM), ANR-15-CE23-0013,REBEL,Redéfinir les Interfaces Cerveau-Ordinateur pour permettre à leurs utilisateurs d'en maitriser le contrôle(2015), European Project: 714567 ,H2020 Pilier ERC,BrainConquest(2017), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Pillette, Léa, Interactions humain-machine, objets connectés, contenus numériques, données massives et connaissance - Redéfinir les Interfaces Cerveau-Ordinateur pour permettre à leurs utilisateurs d'en maitriser le contrôle - - REBEL2015 - ANR-15-CE23-0013 - AAPG2015 - VALID, and Boosting Brain-Computer Communication with high Quality User Training - BrainConquest - - H2020 Pilier ERC2017-07-01 - 2022-06-30 - 714567 - VALID

Subjects
Social psychology (sociology), Experimenter influence, Brain activity and meditation, Control (management), Human Factors and Ergonomics, Context (language use), Mental imagery, 050105 experimental psychology, Session (web analytics), Education, 03 medical and health sciences, [SCCO]Cognitive science, 0302 clinical medicine, Motor imagery, [INFO.EIAH] Computer Science [cs]/Technology for Human Learning, 0501 psychology and cognitive sciences, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], Brain–computer interface, User training, 05 social sciences, General Engineering, Gender, [SCCO] Cognitive science, Human-Computer Interaction, Hardware and Architecture, Brain-Computer Interfaces, [INFO.EIAH]Computer Science [cs]/Technology for Human Learning, [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], Psychology, 030217 neurology & neurosurgery, Software, Cognitive psychology, Mental image
Abstract

International audience; Context Motor Imagery based Brain-Computer Interfaces (MI-BCIs) enable their users to interact with digital technologies, e.g., neuroprosthesis, by performing motor imagery tasks only, e.g., imagining hand movements, while their brain activity is recorded. To control MI-BCIs, users must train to control their brain activity. During such training, experimenters have a fundamental role, e.g., they motivate participants. However, their influence had never been formally assessed for MI-BCI user training. In other fields, e.g., social psychology, experimenters’ gender was found to influence experimental outcomes, e.g., behavioural or neurophysiological measures.ObjectiveOur aim was to evaluate if the experimenters’ gender influenced MI-BCI user training outcomes, i.e., performances and user-experience.MethodsWe performed an experiment involving 6 experimenters (3 women) each training 5 women and 5 men (60 participants) to perform right versus left hand MI-BCI tasks over one session. We then studied the training outcomes, i.e., MI-BCI performances and user-experience, according to the experimenters' and subjects' gender.ResultsA significant interaction between experimenters’ and participants' gender was found on the evolution of trial-wise performances. Another interaction was found between participants’ tension and experimenters’ gender on the average performances.ConclusionExperimenters’ gender could influence MI-BCI performances depending on participants’ gender and tension.SignificanceExperimenters’ influence on MI-BCI user training outcomes should be better controlled, assessed and reported to further benefit from it while preventing any bias.

Published
2021
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28. Toward understanding the influence of the experimenter on BCI performance

Author

Aline Roc, Léa Pillette, Fabien Lotte, Popular interaction with 3d content (Potioc), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Pillette, Léa, and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest

Subjects
[SCCO]Cognitive science, [SHS.STAT]Humanities and Social Sciences/Methods and statistics, ACM: D.: Software/D.2: SOFTWARE ENGINEERING/D.2.2: Design Tools and Techniques/D.2.2.12: User interfaces, [SHS.STAT] Humanities and Social Sciences/Methods and statistics, [SCCO] Cognitive science, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], ACM: D.: Software/D.2: SOFTWARE ENGINEERING/D.2.2: Design Tools and Techniques, ComputingMilieux_MISCELLANEOUS, ACM: D.: Software/D.2: SOFTWARE ENGINEERING/D.2.10: Design/D.2.10.0: Methodologies
Abstract

International audience

Published
2018

29. Influence of feedback transparency on motor imagery neurofeedback performance: the contribution of agency.

Author

Dussard C, Pillette L, Dumas C, Pierrieau E, Hugueville L, Lau B, Jeunet-Kelway C, and George N

Subjects
Humans, Male, Female, Young Adult, Adult, Neurofeedback methods, Neurofeedback physiology, Imagination physiology, Electroencephalography methods, Psychomotor Performance physiology
Abstract

Objective. Neurofeedback (NF) is a cognitive training procedure based on real-time feedback (FB) of a participant's brain activity that they must learn to self-regulate. A classical visual FB delivered in a NF task is a filling gauge reflecting a measure of brain activity. This abstract visual FB is not transparently linked-from the subject's perspective-to the task performed (e.g., motor imagery (MI)). This may decrease the sense of agency, that is, the participants' reported control over FB. Here, we assessed the influence of FB transparency on NF performance and the role of agency in this relationship. Approach. Participants performed a NF task using MI to regulate brain activity measured using electroencephalography. In separate blocks, participants experienced three different conditions designed to vary transparency: FB was presented as either (1) a swinging pendulum, (2) a clenching virtual hand, (3) a clenching virtual hand combined with a motor illusion induced by tendon vibration. We measured self-reported agency and user experience after each NF block. Main results . We found that FB transparency influences NF performance. Transparent visual FB provided by the virtual hand resulted in significantly better NF performance than the abstract FB of the pendulum. Surprisingly, adding a motor illusion to the virtual hand significantly decreased performance relative to the virtual hand alone. When introduced in incremental linear mixed effect models, self-reported agency was significantly associated with NF performance and it captured the variance related to the effect of FB transparency on NF performance. Significance . Our results highlight the relevance of transparent FB in relation to the sense of agency. This is likely an important consideration in designing FB to improve NF performance and learning outcomes., (Creative Commons Attribution license.)

Published
2024
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30. Towards electrophysiological measurement of presence in virtual reality through auditory oddball stimuli.

Author

Savalle E, Pillette L, Won K, Argelaguet F, Lécuyer A, and J-M Macé M

Subjects
Humans, Male, Female, Adult, Young Adult, Event-Related Potentials, P300 physiology, Algorithms, Attention physiology, Evoked Potentials, Auditory physiology, Virtual Reality, Electroencephalography methods, Acoustic Stimulation methods
Abstract

Objective. Presence is an important aspect of user experience in virtual reality (VR). It corresponds to the illusion of being physically located in a virtual environment (VE). This feeling is usually measured through questionnaires that disrupt presence, are subjective and do not allow for real-time measurement. Electroencephalography (EEG), which measures brain activity, is increasingly used to monitor the state of users, especially while immersed in VR. Approach. In this paper, we present a way of evaluating presence, through the measure of the attention dedicated to the real environment via an EEG oddball paradigm. Using breaks in presence, this experimental protocol constitutes an ecological method for the study of presence, as different levels of presence are experienced in an identical VE. Main results. Through analysing the EEG data of 18 participants, a significant increase in the neurophysiological reaction to the oddball, i.e. the P300 amplitude, was found in low presence condition compared to high presence condition. This amplitude was significantly correlated with the self-reported measure of presence. Using Riemannian geometry to perform single-trial classification, we present a classification algorithm with 79% accuracy in detecting between two presence conditions. Significance. Taken together our results promote the use of EEG and oddball stimuli to monitor presence offline or in real-time without interrupting the user in the VE., (© 2024 IOP Publishing Ltd.)

Published
2024
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31. Large scale investigation of the effect of gender on mu rhythm suppression in motor imagery brain-computer interfaces.

Author

von Groll VG, Leeuwis N, Rimbert S, Roc A, Pillette L, Lotte F, and Alimardani M

Abstract

The utmost issue in Motor Imagery Brain-Computer Interfaces (MI-BCI) is the BCI poor performance known as 'BCI inefficiency'. Although past research has attempted to find a solution by investigating factors influencing users' MI-BCI performance, the issue persists. One of the factors that has been studied in relation to MI-BCI performance is gender. Research regarding the influence of gender on a user's ability to control MI-BCIs remains inconclusive, mainly due to the small sample size and unbalanced gender distribution in past studies. To address these issues and obtain reliable results, this study combined four MI-BCI datasets into one large dataset with 248 subjects and equal gender distribution. The datasets included EEG signals from healthy subjects from both gender groups who had executed a right- vs. left-hand motor imagery task following the Graz protocol. The analysis consisted of extracting the Mu Suppression Index from C3 and C4 electrodes and comparing the values between female and male participants. Unlike some of the previous findings which reported an advantage for female BCI users in modulating mu rhythm activity, our results did not show any significant difference between the Mu Suppression Index of both groups, indicating that gender may not be a predictive factor for BCI performance., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.)

Published
2024
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32. Modeling the acceptability of BCIs for motor rehabilitation after stroke: A large scale study on the general public.

Author

Grevet E, Forge K, Tadiello S, Izac M, Amadieu F, Brunel L, Pillette L, Py J, Gasq D, and Jeunet-Kelway C

Abstract

Introduction: Strokes leave around 40% of survivors dependent in their activities of daily living, notably due to severe motor disabilities. Brain-computer interfaces (BCIs) have been shown to be efficiency for improving motor recovery after stroke, but this efficiency is still far from the level required to achieve the clinical breakthrough expected by both clinicians and patients. While technical levers of improvement have been identified (e.g., sensors and signal processing), fully optimized BCIs are pointless if patients and clinicians cannot or do not want to use them. We hypothesize that improving BCI acceptability will reduce patients' anxiety levels, while increasing their motivation and engagement in the procedure, thereby favoring learning, ultimately, and motor recovery. In other terms, acceptability could be used as a lever to improve BCI efficiency. Yet, studies on BCI based on acceptability/acceptance literature are missing. Thus, our goal was to model BCI acceptability in the context of motor rehabilitation after stroke, and to identify its determinants., Methods: The main outcomes of this paper are the following: i) we designed the first model of acceptability of BCIs for motor rehabilitation after stroke, ii) we created a questionnaire to assess acceptability based on that model and distributed it on a sample representative of the general public in France ( N = 753, this high response rate strengthens the reliability of our results), iii) we validated the structure of this model and iv) quantified the impact of the different factors on this population., Results: Results show that BCIs are associated with high levels of acceptability in the context of motor rehabilitation after stroke and that the intention to use them in that context is mainly driven by the perceived usefulness of the system. In addition, providing people with clear information regarding BCI functioning and scientific relevance had a positive influence on acceptability factors and behavioral intention ., Discussion: With this paper we propose a basis (model) and a methodology that could be adapted in the future in order to study and compare the results obtained with: i) different stakeholders, i.e., patients and caregivers; ii) different populations of different cultures around the world; and iii) different targets, i.e., other clinical and non-clinical BCI applications., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Grevet, Forge, Tadiello, Izac, Amadieu, Brunel, Pillette, Py, Gasq and Jeunet-Kelway.)

Published
2023
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33. Long-Term BCI Training of a Tetraplegic User: Adaptive Riemannian Classifiers and User Training.

Author

Benaroch C, Sadatnejad K, Roc A, Appriou A, Monseigne T, Pramij S, Mladenovic J, Pillette L, Jeunet C, and Lotte F

Abstract

While often presented as promising assistive technologies for motor-impaired users, electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) remain barely used outside laboratories due to low reliability in real-life conditions. There is thus a need to design long-term reliable BCIs that can be used outside-of-the-lab by end-users, e.g., severely motor-impaired ones. Therefore, we propose and evaluate the design of a multi-class Mental Task (MT)-based BCI for longitudinal training (20 sessions over 3 months) of a tetraplegic user for the CYBATHLON BCI series 2019. In this BCI championship, tetraplegic pilots are mentally driving a virtual car in a racing video game. We aimed at combining a progressive user MT-BCI training with a newly designed machine learning pipeline based on adaptive Riemannian classifiers shown to be promising for real-life applications. We followed a two step training process: the first 11 sessions served to train the user to control a 2-class MT-BCI by performing either two cognitive tasks (REST and MENTAL SUBTRACTION) or two motor-imagery tasks (LEFT-HAND and RIGHT-HAND). The second training step (9 remaining sessions) applied an adaptive, session-independent Riemannian classifier that combined all 4 MT classes used before. Moreover, as our Riemannian classifier was incrementally updated in an unsupervised way it would capture both within and between-session non-stationarity. Experimental evidences confirm the effectiveness of this approach. Namely, the classification accuracy improved by about 30% at the end of the training compared to initial sessions. We also studied the neural correlates of this performance improvement. Using a newly proposed BCI user learning metric, we could show our user learned to improve his BCI control by producing EEG signals matching increasingly more the BCI classifier training data distribution, rather than by improving his EEG class discrimination. However, the resulting improvement was effective only on synchronous (cue-based) BCI and it did not translate into improved CYBATHLON BCI game performances. For the sake of overcoming this in the future, we unveil possible reasons for these limited gaming performances and identify a number of promising future research directions. Importantly, we also report on the evolution of the user's neurophysiological patterns and user experience throughout the BCI training and competition., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Benaroch, Sadatnejad, Roc, Appriou, Monseigne, Pramij, Mladenovic, Pillette, Jeunet and Lotte.)

Published
2021
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