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1. The early negative bias of social semantics: evidence from behavioral and ERP studies.

Author

Fan X, Xu Q, Liu J, Xing H, Ning L, Chen Q, and Yang Y

Subjects
Male, Humans, Female, Evoked Potentials, Brain, Existentialism, Semantics, Electroencephalography
Abstract

Background: Compared to nonsocial information, the human brain is more highly sensitive to social information. As a kind of typical social semantic information, the words describing person traits differ from the nonsocial semantic information describing inanimate objects in many ways. It remains to be seen whether the processing of trait words has a valence asymmetric and whether it differs from the processing of nonsocial semantic information in terms of behavioral responses and neural temporal processes., Method: Taking person and object names as priming stimuli and adjective words only used for describing humans or objects as target stimuli, the present study aimed to investigate the processing characteristics of social and nonsocial semantic information by recording both behavioral and ERP data., Results: Behavioral results showed that the response times for negative words were significantly slower than those for positive words whether for social or nonsocial semantic information. The accuracy rates of negative words were significantly lower than those of positive words when the targets were social words which is contrary to the nonsocial words. The ERP results indicated that there was a negative bias effect on the processing of both types of information during the whole time course of brain neural activity; that is, the P2, N400, and LPP amplitudes elicited by negative words were larger than those elicited by positive words; However, the negative bias effect of social semantic information started at the early perceptual stage which was significantly earlier than the onset of negative bias of nonsocial semantic information, and was significantly affected by the prime type. In addition, there was a significant semantic conflict N400 effect only for nonsocial semantic information., Conclusions: Overall, the present study revealed the existence of an early negative bias of social information and provided evidence for the specificity of social information., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Published
2023
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2. Optimizing Motor Intention Detection With Deep Learning: Towards Management of Intraoperative Awareness

Author

Laurent Bougrain, Oleksii Avilov, Anton Popov, Sébastien Rimbert, National Technical University of Ukraine 'Kyiv Polytechnic Institute' [Kiev], Analysis and modeling of neural systems by a system neuroscience approach (NEUROSYS), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Complex Systems, Artificial Intelligence & Robotics (LORIA - AIS), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL), Oleksii Avilov was supported by scholarship from the French Embassy to Ukraine while working on this topic at the NEUROSYS team at LORIA (Université de Lorraine/CNRS/Inria), Nancy, France. Experiments presented in this paper were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (https://www.grid5000.fr)., Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), and Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)

Subjects
0209 industrial biotechnology, Computer science, Biomedical Engineering, motor imagery AAGA: accidental awareness during general anesthesia, 02 engineering and technology, Intention, Electroencephalography, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], Intraoperative Awareness, 020901 industrial engineering & automation, Motor imagery, median nerve stimulation, Deep Learning, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], intraoperative awareness during general anesthesia, 0202 electrical engineering, electronic engineering, information engineering, medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Functional electrical stimulation, Humans, Brain-computer interface (BCI), Artificial neural network, medicine.diagnostic_test, Median nerve stimulation, business.industry, electroencephalogram (EEG), Deep learning, [SCCO.NEUR]Cognitive science/Neuroscience, Pattern recognition, Linear discriminant analysis, Median nerve, medicine.anatomical_structure, machine learning, Frontal lobe, Brain-Computer Interfaces, Imagination, 020201 artificial intelligence & image processing, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithms, Motor cortex
Abstract

International audience; Objective: This article shows the interest in deep learning techniques to detect motor imagery (MI) from raw electroencephalographic (EEG) signals when a functional electrical stimulation is added or not. Impacts of electrode montages and bandwidth are also reported. The perspective of this work is to improve the detection of intraoperative awareness during general anesthesia. Methods: Various architectures of EEGNet were investigated to optimize MI detection. They have been compared to the state-of-the-art classifiers in Brain-Computer Interfaces (based on Riemannian geometry, linear discriminant analysis), and other deep learning architectures (deep convolution network, shallow convolutional network). EEG data were measured from 22 participants performing motor imagery with and without median nerve stimulation. Results: The proposed architecture of EEGNet reaches the best classification accuracy (83.2%) and false-positive rate (FPR 19.0%) for a setup with only six electrodes over the motor cortex and frontal lobe and for an extended 4-38 Hz EEG frequency range while the subject is being stimulated via a median nerve. Configurations with a larger number of electrodes result in higher accuracy (94.5%) and FPR (6.1%) for 128 electrodes (and respectively 88.0% and 12.9% for 13 electrodes).Conclusion: The present work demonstrates that using an extended EEG frequency band and a modified EEGNet deep neural network increases the accuracy of MI detection when used with as few as 6 electrodes which include frontal channels. Significance: The proposed method contributes to the development of Brain-Computer Interface systems based on MI detection from EEG.

Published
2021

3. Binaural Beats through the auditory pathway: from brainstem to connectivity patterns

Author

Hector D. Orozco Perez, Guillaume Dumas, Alexandre Lehmann, International Laboratory for Brain, Music and Sound Research (BRAMS), McMaster University [Hamilton, Ontario], Génétique humaine et fonctions cognitives - Human Genetics and Cognitive Functions (GHFC (UMR_3571 / U-Pasteur_1)), Institut Pasteur [Paris]-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Université Sorbonne Paris Cité (USPC), McGill University = Université McGill [Montréal, Canada], Centre for Research on Brain, Language and Music (CRBLM), The authors of this paper would like to thank Pierre Rainville and Bérangère Houze for sharing the E - SAS scales, Mihaela Felezeu for all the help and support during data acquisition and our participants for volunteering their time to perform the experiment, Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Institut Pasteur [Paris], McGill University = Université McGill [Montréal, Canada]-Université de Montréal (UdeM), Center for Complex Systems and Brain Sciences, Florida Atlantic University [Boca Raton], GD was financially supported by the Institut Pasteur., Institut Pasteur [Paris] (IP)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Université McGill -Université de Montréal (UdeM), and McGill University

Subjects
Auditory Pathways, Brain activity and meditation, media_common.quotation_subject, binaural beats, brain entrainment, Stimulus (physiology), Monaural, 050105 experimental psychology, law.invention, 03 medical and health sciences, 0302 clinical medicine, law, Perception, Humans, 0501 psychology and cognitive sciences, Single-Blind Method, EEG, 10. No inequality, media_common, Auditory Cortex, Brainwave entrainment, Binaural beats, General Neuroscience, 05 social sciences, brain connectivity, Monaural beats, Electroencephalography, General Medicine, Frequency following response, Acoustic Stimulation, Cognition and Behavior, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Psychology, Neuroscience, Binaural recording, Auditory illusion, 030217 neurology & neurosurgery, Research Article: New Research, Brain Stem
Abstract

Visual Abstract, Binaural beating is a perceptual auditory illusion occurring when presenting two neighboring frequencies to each ear separately. Several controversial claims have been attributed to binaural beats regarding their ability to entrain human brain activity and mood, in both the scientific literature and the marketing realm. Here, we sought to address those questions in a robust fashion using a single-blind, active-controlled protocol. To do so, we compared the effects of binaural beats with a control beat stimulation (monaural beats, known to entrain brain activity but not mood) across four distinct levels in the human auditory pathway: subcortical and cortical entrainment, scalp-level functional connectivity and self-reports. Both stimuli elicited standard subcortical responses at the pure tone frequencies of the stimulus [i.e., frequency following response (FFR)], and entrained the cortex at the beat frequency [i.e., auditory steady state response (ASSR)]. Furthermore, functional connectivity patterns were modulated differentially by both kinds of stimuli, with binaural beats being the only one eliciting cross-frequency activity. Despite this, we did not find any mood modulation related to our experimental manipulation. Our results provide evidence that binaural beats elicit cross frequency connectivity patterns, but weakly entrain the cortex when compared with monaural beat stimuli. Whether binaural beats have an impact on cognitive performance or other mood measurements remains to be seen and can be further investigated within the proposed methodological framework.

Published
2019