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2023 Medical Technologies Congress, TIPTEKNO 2023 -- 10 November 2023 through 12 November 2023 -- 195703, Panic Disorder (PD) is a debilitating condition marked by sudden, intense fear episodes with physical symptoms. Swift and accurate PD detection is crucial for effective intervention. This study aimed to propose an optimal combination of spectral features of the Alpha band to detect PD. For this purpose, 21 PD-diagnosed individuals and 26 healthy controls attended a 5-minute eyes-closed resting state Electroencephalography (EEG) recording session. Welch method was applied to calculate the power spectral density of EEG signals and then the sum, average, maximum, relative power of alpha band, and individual alpha frequency (IAF) were extracted. Relief and nearest component analysis (NCA) methods were performed to select highly relevant features. The maximum average accuracy was reached when commonly selected features between two selection methods were used as inputs of classifiers. Adaboost classifier reached the highest average accuracy with $89.03 ±6.73% rate. © 2023 IEEE.

2023 Medical Technologies Congress, TIPTEKNO 2023 -- 10 November 2023 through 12 November 2023 -- 195703, Panic Disorder (PD) is a debilitating condition marked by sudden, intense fear episodes with physical symptoms. Swift and accurate PD detection is crucial for effective intervention. This study aimed to propose an optimal combination of spectral features of the Alpha band to detect PD. For this purpose, 21 PD-diagnosed individuals and 26 healthy controls attended a 5-minute eyes-closed resting state Electroencephalography (EEG) recording session. Welch method was applied to calculate the power spectral density of EEG signals and then the sum, average, maximum, relative power of alpha band, and individual alpha frequency (IAF) were extracted. Relief and nearest component analysis (NCA) methods were performed to select highly relevant features. The maximum average accuracy was reached when commonly selected features between two selection methods were used as inputs of classifiers. Adaboost classifier reached the highest average accuracy with $89.03 ±6.73% rate. © 2023 IEEE.

Differential diagnosis in epilepsy is sometimes challenging. Video-electroencephalography (V-EEG) is an essential tool in the diagnosis and management of epilepsy. The prolonged duration of V-EEG recording increases the diagnostic yield of a conventional V-EEG. The right length of monitoring for different indications is still to be established. We present a retrospective descriptive study with a sample of 50 patients with long-term V-EEG monitoring, with a mean age of 36.1 years, monitored from 2013 to 2019 at the Burgos University Hospital. The mean monitoring time was 3.6 days. Events were obtained in 76% of the patients, corresponding to epileptic seizures (ES) in 57.9% of them, with psychogenic non-epileptic seizures (PNES) in 39.5%, and with episodes of both pathologies in 2.6% of the patients. We found that the first event was highly representative, and it correlated with the rest of the events that would be recorded. Moreover, 92% of the first PNES had been captured at the end of the second day, and 89% of the first ES by the end of the third day. V-EEG for differential diagnosis between ES and PNES can be performed in hospitals without specialized epilepsy surgery units. For this indication, the duration of long-term V-EEG can be adjusted individually depending on the nature of the first event.

In this paper, we propose an interpretable electroencephalogram (EEG)-based solution for the diagnostics of major depressive disorder (MDD). The acquisition of EEG experimental data involved 32 MDD patients and 29 healthy controls. A feature matrix is constructed involving frequency decomposition of EEG data based on power spectrum density (PSD) using the Welch method. Those PSD features were selected, which were statistically significant. To improve interpretability, the best features are first selected from feature space via the non-dominated sorting genetic (NSGA-II) evolutionary algorithm. The best features are utilized for support vector machine (SVM), and k-nearest neighbors (k-NN) classifiers, and the results are then correlated with features to improve the interpretability. The results show that the features (gamma bands) extracted from the left temporal brain regions can distinguish MDD patients from control significantly. The proposed best solution by NSGA-II gives an average sensitivity of 93.3%, specificity of 93.4% and accuracy of 93.5%. The complete framework is published as open-source at https://github.com/ehw-fit/eeg-mdd., V tomto článku navrhujeme interpretovatelnou metodu pro diagnostiku velké depresivní poruchy (MDD) založenou na elektroencefalogramu (EEG). Experimentální data EEG byla získána od 32 pacientů s MDD a 29 zdravých jedinců. Byla zkonstruována matice příznaků zahrnující frekvenční dekompozici EEG dat na základě hustoty výkonového spektra (PSD) pomocí Welchovy metody. Byly vybrány ty příznaky PSD, které byly statisticky významné. Pro zlepšení interpretovatelnosti byly nejprve vybrány nejlepší příznaky z prostoru příznaků pomocí vícekriteriálního evolučního algoritmu NSGA-II. Nejlepší příznaky jsou využity pro klasifikaci pomocí algoritmů SVM a k-NN. Výsledky jsou následně korelovány s příznaky, aby se zlepšila interpretovatelnost. Výsledky ukazují, že příznaky (gama pásmo) extrahované z levé spánkové oblasti mozku dokáží významně odlišit pacienty s MDD od kontrolní skupiny. Navržené nejlepší řešení pomocí NSGA-II poskytuje průměrnou citlivost 93,3 %, specifičnost 93,4 % a přesnost 93,5 %. Kompletní systém je zveřejněn jako open-source na adrese https://github.com/ehw-fit/eeg-mdd.

Producción Científica, Automatic deep-learning models used for sleep scoring in children with obstructive sleep apnea (OSA) are perceived as black boxes, limiting their implementation in clinical settings. Accordingly, we aimed to develop an accurate and interpretable deep-learning model for sleep staging in children using single-channel electroencephalogram (EEG) recordings. We used EEG signals from the Childhood Adenotonsillectomy Trial (CHAT) dataset (n = 1637) and a clinical sleep database (n = 980). Three distinct deep-learning architectures were explored to automatically classify sleep stages from a single-channel EEG data. Gradient-weighted Class Activation Mapping (Grad-CAM), an explainable artificial intelligence (XAI) algorithm, was then applied to provide an interpretation of the singular EEG patterns contributing to each predicted sleep stage. Among the tested architectures, a standard convolutional neural network (CNN) demonstrated the highest performance for automated sleep stage detection in the CHAT test set (accuracy = 86.9% and five-class kappa = 0.827). Furthermore, the CNN-based estimation of total sleep time exhibited strong agreement in the clinical dataset (intra-class correlation coefficient = 0.772). Our XAI approach using Grad-CAM effectively highlighted the EEG features associated with each sleep stage, emphasizing their influence on the CNN's decision-making process in both datasets. Grad-CAM heatmaps also allowed to identify and analyze epochs within a recording with a highly likelihood to be misclassified, revealing mixed features from different sleep stages within these epochs. Finally, Grad-CAM heatmaps unveiled novel features contributing to sleep scoring using a single EEG channel. Consequently, integrating an explainable CNN-based deep-learning model in the clinical environment could enable automatic sleep staging in pediatric sleep apnea tests., Ministerio de Ciencia e Innovación- Agencia Estatal de Investigación- FEDER-EU y NextGenerationEU/PRTR (PID2020-115468RB-I00 y PDC2021-120775-I00), Sociedad Española de Neumología y Cirugía Torácica (SEPAR) (649/2018), CIBER -Consorcio Centro de Investigación Biomédica en Red- Instituto de Salud Carlos III (CB19/01/00012), Institutos Nacionales de Salud (HL083075, HL083129, UL1-RR-024134, UL1 RR024989), Instituto Nacional del Corazón, los Pulmones y la Sangre (R24 HL114473, 75N92019R002), Ministerio de Ciencia e Innovación - Agencia Estatal de Investigación- “Ramón y Cajal” grant (RYC2019-028566-I)

Around a third of the total population of Europe suffers from mental disorders. The use of electroencephalography (EEG) together with Machine Learning (ML) algorithms to diagnose mental disorders has recently been shown to be a prominent research area, as exposed by several reviews focused on the field. Nevertheless, previous to the application of ML algorithms, EEG data should be correctly preprocessed and prepared via Feature Engineering (FE). In fact, the choice of FE techniques can make the difference between an unusable ML model and a simple, effective model. In other words, it can be said that FE is crucial, especially when using complex, non-stationary data such as EEG. To this aim, in this paper we present a Systematic Mapping Study (SMS) focused on FE from EEG data used to identify mental disorders. Our SMS covers more than 900 papers, making it one of the most comprehensive to date, to the best of our knowledge. We gathered the mental disorder addressed, all the FE techniques used, and the Artificial Intelligence (AI) algorithm applied for classification from each paper. Our main contributions are: (i) we offer a starting point for new researchers on these topics, (ii) we extract the most used FE techniques to classify mental disorders, (iii) we show several graphical distributions of all used techniques, and (iv) we provide critical conclusions for detecting mental disorders. To provide a better overview of existing techniques, the FE process is divided into three parts: (i) signal transformation, (ii) feature extraction, and (iii) feature selection. Moreover, we classify and analyze the distribution of existing papers according to the mental disorder they treat, the FE processes used, and the ML techniques applied. As a result, we provide a valuable reference for the scientific community to identify which techniques have been proven and tested and where the gaps are located in the current state of the art.

Augmented Reality (AR) head-mounted display (HMD) provides users with an immersive virtual experience in the real world. The portability of this technology affords various information display options for construction workers that are not possible otherwise. The information delivered via an interactive user interface provides an innovative method to display complex building instructions, which is more intuitive and accessible compared with traditional paper documentations. However, there are still challenges hindering the practical usage of this technology at the construction jobsite. As a technical restriction, current AR HMD products have a limited field of view (FOV) compared to the human vision range. It leads to an uncertainty of how the obstructed view of display will affect construction workers' perception of hazards in their surrounding area. Similarly, the information displayed to workers requires rigorous testing and evaluation to make sure that it does not lead to information overload. Therefore, it is essential to comprehensively evaluate the impacts of using AR HMD from both perspectives of task performance and cognitive performance. This dissertation aims to bridge the gap in understanding the cognitive impacts of using AR HMD in construction assembly tasks. Specifically, it focuses on answering the following two questions: (1) How are task performance and cognitive skills affected by AR displays under complex working conditions? (2) How are moment-to-moment changes of mental workload captured and evaluated during construction assembly tasks? To answer these questions, this dissertation proposed two experiments. The first study tests two AR displays (conformal and tag-along) and paper instruction under complex working conditions, involving different framing scales and interference settings. Subjective responses are collected and analyzed to evaluate overall mental workload and situation awareness. The second study focuses on exploring an electroencephal

Producción Científica, Automatic deep-learning models used for sleep scoring in children with obstructive sleep apnea (OSA) are perceived as black boxes, limiting their implementation in clinical settings. Accordingly, we aimed to develop an accurate and interpretable deep-learning model for sleep staging in children using single-channel electroencephalogram (EEG) recordings. We used EEG signals from the Childhood Adenotonsillectomy Trial (CHAT) dataset (n = 1637) and a clinical sleep database (n = 980). Three distinct deep-learning architectures were explored to automatically classify sleep stages from a single-channel EEG data. Gradient-weighted Class Activation Mapping (Grad-CAM), an explainable artificial intelligence (XAI) algorithm, was then applied to provide an interpretation of the singular EEG patterns contributing to each predicted sleep stage. Among the tested architectures, a standard convolutional neural network (CNN) demonstrated the highest performance for automated sleep stage detection in the CHAT test set (accuracy = 86.9% and five-class kappa = 0.827). Furthermore, the CNN-based estimation of total sleep time exhibited strong agreement in the clinical dataset (intra-class correlation coefficient = 0.772). Our XAI approach using Grad-CAM effectively highlighted the EEG features associated with each sleep stage, emphasizing their influence on the CNN's decision-making process in both datasets. Grad-CAM heatmaps also allowed to identify and analyze epochs within a recording with a highly likelihood to be misclassified, revealing mixed features from different sleep stages within these epochs. Finally, Grad-CAM heatmaps unveiled novel features contributing to sleep scoring using a single EEG channel. Consequently, integrating an explainable CNN-based deep-learning model in the clinical environment could enable automatic sleep staging in pediatric sleep apnea tests., Ministerio de Ciencia e Innovación- Agencia Estatal de Investigación- FEDER-EU y NextGenerationEU/PRTR (PID2020-115468RB-I00 y PDC2021-120775-I00), Sociedad Española de Neumología y Cirugía Torácica (SEPAR) (649/2018), CIBER -Consorcio Centro de Investigación Biomédica en Red- Instituto de Salud Carlos III (CB19/01/00012), Institutos Nacionales de Salud (HL083075, HL083129, UL1-RR-024134, UL1 RR024989), Instituto Nacional del Corazón, los Pulmones y la Sangre (R24 HL114473, 75N92019R002), Ministerio de Ciencia e Innovación - Agencia Estatal de Investigación- “Ramón y Cajal” grant (RYC2019-028566-I)

The estimation of cognitive workload using electroencephalogram (EEG) is an emerging research area. However, due to poor spatial resolution issues, features obtained from EEG signals often lead to poor classification results. As a good generative model, the variational autoencoder (VAE) extracts the noise-free robust features from the latent space that lead to better classification performance. The spatial attention-based method [convolutional block attention module (CBAM)] can improve the spatial resolution of EEG signals. In this article, we propose an effective VAE-CBAM-based deep model for estimating cognitive states from topographical videos. Topographical videos of four different conditions [baseline (BL), low workload (LW), medium workload (MW), and high workload (HW)] of the mental arithmetic task are taken for the experiment. Initially, the VAE extracts localized features from input images (extracted from topographical video), and CBAM infers the spatial-channel-level's attention features from those localized features. Finally, the deep CNN-BLSTM model effectively learns those attention-based spatial features in a timely distributed manner to classify the cognitive state. For four-class and two-class classifications, the proposed model achieves 83.13% and 92.09% classification accuracy, respectively. The proposed model enhances the future research scope of attention-based studies in EEG applications.

Brain-computer interface (BCI) technology can realize dynamic room temperature adjustment based on individual real-time thermal sensation, which can provide the basis for future intelligent buildings. However, the generalization ability of previous thermal sensation discrimination model (TSDM) is limited, which is a serious obstacle to the application. In this paper, a general TSDM was developed by using convolutional neural network (CNN), which can be well applied to new subjects. In the study, the CNN-TSDM was established and evaluated based on the offline experimental data, and then the BCI closed-loop online room temperature control experiment was carried out based on this CNN-TSDM to further verify. The offline analysis results show that the recognition performance of CNN-TSDM in new subjects is significantly higher than that of typical shallow learning algorithms, and its area under the ROC curve (AUC) value reaches 0.789. In the online experiments of the two simulated environments, BCI using the CNN-TSDM dynamically controlled the air conditioning to improve the room temperature to the comfortable level according to the subjects' thermal sensation. The subjective score of subjects decreased from 3.1 to 3.0 for the hot uncomfortable to 1.1 and 1.2 for the cool comfortable (p < 0.001, p < 0.001). Moreover, in a hotter simulated experimental environment, BCI automatically controlled the air conditioner for longer cooling to obtain a same degree of thermal comfort. The total cooling time (p < 0.05) and the single cooling time (p < 0.05) of the air conditioner were significantly increased. This further confirmed the effectiveness and robustness of the general CNN-TSDM.

A brain computer interface (BCI) is a system which connects neural signals to a computer system. They have been used for controlling systems including robotics, on-screen computer control such as mouse movement, typing, and synthesizing audio signals. Invasive, or implanted, systems are often long-term medical solutions, or used for research where very clear signal is required. Non-invasive systems usually rely on exterior signals gathered through a headset using one or more electrode sensors. These signals are composed of sums of neuron activation potentials from brain activity and can be used to determine particular aspects of brain function. All BCIs rely on neural feedback of some form; the most effective systems combine multiple forms of feedback. The proposed BCI design will control audio playback through a connection to a non-invasive EEG sensor headset worn by the user. This design will take a conclusive step toward better integration of this technology for more effective control of systems in the future. It would be helpful to any consumer who wants to control media play back hands free, as well as patients unable to use hands for control. Its advantages include a low price point and simple implementation compared to medical and research grade equipment. In summary, the proposed BCI audio playback control system is a simple yet vital step toward a truly robust implementation of such a system on a larger scale.

In recent decades, the automatic recognition and interpretation of brain waves acquired by electroencephalographic (EEG) technologies have undergone remarkable growth, leading to a consequent rapid development of brain–computer interfaces (BCIs). EEG-based BCIs are non-invasive systems that allow communication between a human being and an external device interpreting brain activity directly. Thanks to the advances in neurotechnologies, and especially in the field of wearable devices, BCIs are now also employed outside medical and clinical applications. Within this context, this paper proposes a systematic review of EEG-based BCIs, focusing on one of the most promising paradigms based on motor imagery (MI) and limiting the analysis to applications that adopt wearable devices. This review aims to evaluate the maturity levels of these systems, both from the technological and computational points of view. The selection of papers has been performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), leading to 84 publications considered in the last ten years (from 2012 to 2022). Besides technological and computational aspects, this review also aims to systematically list experimental paradigms and available datasets in order to identify benchmarks and guidelines for the development of new applications and computational models.

In order to control a robot precisely, this essay highlights the growing need for better communication tools between humans and machines. The best method of communication between them is a brain-computer interface (BCI), particularly for the treatment of persons with disabilities and the completion of complex activities like surgery, rehabilitation, and procedures, among other things. The purpose of the survey was to measure public perceptions of and awareness of human-robot interaction (HRI) in the context of brain-computer interfacing (BCI).

In order to control a robot precisely, this essay highlights the growing need for better communication tools between humans and machines. The best method of communication between them is a brain-computer interface (BCI), particularly for the treatment of persons with disabilities and the completion of complex activities like surgery, rehabilitation, and procedures, among other things. The purpose of the survey was to measure public perceptions of and awareness of human-robot interaction (HRI) in the context of brain-computer interfacing (BCI).

Disorders of consciousness (DoC) happen frequently in various brain injuries. Their detection helps timely treatment for better survival outcomes of DoC patients. It is conventionally undertaken via clinical examinations, typically behavioural assessments. However, these neurological examinations consume significant resources of manpower and time, making continuous DoC monitoring practically infeasible. To address this issue, a computer-aided approach is proposed in this article for automated DoC detection through extracting knowledge from electroencephalogram (EEG) signals. It introduces a new connectivity measure: Power Spectral Density Difference (PSDD) incorporating with a recursive Cosine function (CPSDD). Then, the approach classifies brain-injured patients into DoC (i.e., positive) and wakefulness (i.e., negative) classes through an ensemble of support vector machines (EOSVM), which is a type of machine-learning methods. It is further applied to a dataset of 607 patients with brain injuries. Our classification results show that the EOSVM classifier with the new connectivity measure CPSDD has achieved the best classification performance among 12 connectivity measures. For a setting of 97% majority voting from all SVMs, the EOSVM has diagnosed, in high confidence, 35% of patients with the accuracy, sensitivity, and specificity of 98.21%, 100%, and 95.79%, respectively. Thus, the classifier EOSVM incorporating with the new connectivity measure CPSDD is a promising tool for automatic detection of DoC in brain injuries.

The wine sector is of extraordinary relevance in Spain, not only from an economic or environmental point of view, but also from a social and cultural point of view. In this research work for Gil Family Estates (a Spanish company in the wine sector), results of a market study with three approaches will be presented: Neuromarketing, qualitative and quantitative. The main objective of this research is based on the knowledge and understanding of what factors influence when tasting a wine depending on the type of bottle and, as a second part, analyze what type of labels are more attractive to consumers. In this way we will determine the disposition that people will have when buying Gil Family Estates wines. Through the use of neuromarketing techniques - galvanic skin response to record emotional arousal, eye tracking to identify where consumers are looking, and electroencephalography to interpret emotional reactions - combined with the qualitative research technique (in-depth interviews with all consumers), we will be able to know the natural and suggested memories. With this study, two variations of the Gil Family Estates (Amona) wine will be verified in terms of packaging perception and taste. In addition, it will be verified how successful or not the change of the labels of two other wines from the winery, Tridente and Atteca has been. The results indicate a favorable response to the changes made by the brand to improve its aesthetics and also, on the other hand, important knowledge regarding the comparison of one of its wines against another of the competition, El sector vinícola es de extraordinaria relevancia en España, no solo desde el punto de vista económico o medioambiental, sino también desde el social y cultural. En este trabajo de investigación para Gil Family Estates (empresa española del sector vinícola), se presentarán resultados de un estudio de mercado con tres enfoques: Neuromarketing, cualitativo y cuantitativo. El objetivo principal de esta investigación se basa en el conocimiento y comprensión de que factores influyen a la hora de catar un vino dependiendo del tipo de botella y, como segunda parte, analizar que tipo de etiquetas son mas atractivas para los consumidores. De esta manera determinaremos la disposición que tendrán las personas a la hora de comprar los vinos de Gil Family Estates. Mediante el uso de técnicas de neuromarketing -respuesta galvánica de la piel para registrar la excitación emocional, seguimiento ocular para identificar dónde miran los consumidores y electroencefalografía para interpretar las reacciones emocionales- combinadas con la técnica de investigación cualitativa (entrevistas en profundidad con todos los consumidores), podremos conocer los recuerdos naturales y sugeridos. Con este estudio se comprobará dos variaciones del vino de Gil Family Estates (Amona) en cuanto a percepción del envasado y gusto. Además, se comprobará lo acertado o no que han sido el cambio de las etiquetas de otros dos vinos de la bodega, Tridente y Atteca. Los resultados indican una respuesta favorable ante los cambios hechos por la marca para mejorar su estética y también por otra parte, conocimientos importantes en cuanto a la comparación de uno de sus vinos frente a otro de la competencia.

The purpose of this study is to localize the seizure onset zone of patients suffering from drug-resistant epilepsy. During the last two decades, multiple studies proposed the use of Independent Component Analysis (ICA) to analyze ictal electroencephalogram (EEG) recordings. This study aims at evaluating ICA potential with quantitative measurements. In particular, we address the challenging step where the components extracted by ICA of an ictal nature must be selected. We considered a cohort of 10 patients suffering from extratemporal lobe epilepsy who were rendered seizure-free after surgery. Different sets of pre-processing parameters were compared and component features were explored to help distinguish ictal components from others. Quantitative measurements were implemented to determine whether some of the components returned by ICA were located within the resection zone and thus likely to be ictal. Finally, an assistance to the component selection was proposed based on the implemented features. For every seizure, at least one component returned by ICA was localized within the resection zone, with the optimal pre-processing parameters. Three features were found to distinguish components localized within the resection zone: the dispersion of their active brain sources, the ictal rhythm power and the contribution to the EEG variance. Using the implemented component selection assistance based on the features, the probability that the first proposed component yields an accurate estimation reaches 51.43% (without assistance: 24.74%). The accuracy reaches 80% when considering the best result within the first five components. This study confirms the utility of ICA for ictal EEG analysis in extratemporal lobe epilepsy, and suggests relevant features to analyze the components returned by ICA. A component selection assistance is proposed to guide clinicians in their choice for ictal components.

Music is often used for emotion induction. ince the emotions felt when listening to it vary from person to person, customized music is required. Our previous work designed a music generation system that created personalized music based on participants' emotions predicted from EEG data. Although our system effectively induced emotions, unfortunately, it suffered from two problems. The first is that a long EEG recording is required to train emotion prediction models. In this paper, we trained models with a small amount of EEG data. We proposed emotion prediction with meta-learning and compared its performance with two other training methods. The second problem is that the generated music failed to consider the participants' emotions before they listened to music. We solved this challenge by constructing a system that adapted an iso principle that gradually changed the music from close to the participants' emotions to the target emotion. Our results showed that emotion prediction with meta-learning had the lowest RMSE among three methods (p < 0.016). Both a music generation system based on the iso principle and our conventional music generation system more effectively induced emotion than music generation that was not based on the emotions of the participants (p < 0.016)., identifier:2673-253X

The wine sector is of extraordinary relevance in Spain, not only from an economic or environmental point of view, but also from a social and cultural point of view. In this research work for Gil Family Estates (a Spanish company in the wine sector), results of a market study with three approaches will be presented: Neuromarketing, qualitative and quantitative. The main objective of this research is based on the knowledge and understanding of what factors influence when tasting a wine depending on the type of bottle and, as a second part, analyze what type of labels are more attractive to consumers. In this way we will determine the disposition that people will have when buying Gil Family Estates wines. Through the use of neuromarketing techniques - galvanic skin response to record emotional arousal, eye tracking to identify where consumers are looking, and electroencephalography to interpret emotional reactions - combined with the qualitative research technique (in-depth interviews with all consumers), we will be able to know the natural and suggested memories. With this study, two variations of the Gil Family Estates (Amona) wine will be verified in terms of packaging perception and taste. In addition, it will be verified how successful or not the change of the labels of two other wines from the winery, Tridente and Atteca has been. The results indicate a favorable response to the changes made by the brand to improve its aesthetics and also, on the other hand, important knowledge regarding the comparison of one of its wines against another of the competition, El sector vinícola es de extraordinaria relevancia en España, no solo desde el punto de vista económico o medioambiental, sino también desde el social y cultural. En este trabajo de investigación para Gil Family Estates (empresa española del sector vinícola), se presentarán resultados de un estudio de mercado con tres enfoques: Neuromarketing, cualitativo y cuantitativo. El objetivo principal de esta investigación se basa en el conocimiento y comprensión de que factores influyen a la hora de catar un vino dependiendo del tipo de botella y, como segunda parte, analizar que tipo de etiquetas son mas atractivas para los consumidores. De esta manera determinaremos la disposición que tendrán las personas a la hora de comprar los vinos de Gil Family Estates. Mediante el uso de técnicas de neuromarketing -respuesta galvánica de la piel para registrar la excitación emocional, seguimiento ocular para identificar dónde miran los consumidores y electroencefalografía para interpretar las reacciones emocionales- combinadas con la técnica de investigación cualitativa (entrevistas en profundidad con todos los consumidores), podremos conocer los recuerdos naturales y sugeridos. Con este estudio se comprobará dos variaciones del vino de Gil Family Estates (Amona) en cuanto a percepción del envasado y gusto. Además, se comprobará lo acertado o no que han sido el cambio de las etiquetas de otros dos vinos de la bodega, Tridente y Atteca. Los resultados indican una respuesta favorable ante los cambios hechos por la marca para mejorar su estética y también por otra parte, conocimientos importantes en cuanto a la comparación de uno de sus vinos frente a otro de la competencia.

Purpose: In this paper, a new automated procedure based on deep learning methods for schizophrenia diagnosis is presented. Methods: To this aim, electroencephalogram signals obtained using a 32-channel helmet are prominently used to analyze high temporal resolution information from the brain. By these means, the data collected is employed to evaluate the class likelihoods using a neuronal network based on radial basis functions and a fuzzy means algorithm. Results: The results obtained with real datasets validate the high accuracy of the proposed classification method. Thus, effectively characterizing the changes in EEG signals acquired from schizophrenia patients and healthy volunteers. More specifically, values of accuracy better than 93% has been obtained in the present research. Additionally, a comparative study with other approaches based on well-knows machine learning methods shows that the proposed method provides better results than recently proposed algorithms in schizophrenia detection. Conclusion: The proposed method can be used as a diagnostic tool in the detection of the schizophrenia, helping for early diagnosis and treatment., QC 20230614

Music is often used for emotion induction. ince the emotions felt when listening to it vary from person to person, customized music is required. Our previous work designed a music generation system that created personalized music based on participants' emotions predicted from EEG data. Although our system effectively induced emotions, unfortunately, it suffered from two problems. The first is that a long EEG recording is required to train emotion prediction models. In this paper, we trained models with a small amount of EEG data. We proposed emotion prediction with meta-learning and compared its performance with two other training methods. The second problem is that the generated music failed to consider the participants' emotions before they listened to music. We solved this challenge by constructing a system that adapted an iso principle that gradually changed the music from close to the participants' emotions to the target emotion. Our results showed that emotion prediction with meta-learning had the lowest RMSE among three methods (p < 0.016). Both a music generation system based on the iso principle and our conventional music generation system more effectively induced emotion than music generation that was not based on the emotions of the participants (p < 0.016).

Music is often used for emotion induction. ince the emotions felt when listening to it vary from person to person, customized music is required. Our previous work designed a music generation system that created personalized music based on participants' emotions predicted from EEG data. Although our system effectively induced emotions, unfortunately, it suffered from two problems. The first is that a long EEG recording is required to train emotion prediction models. In this paper, we trained models with a small amount of EEG data. We proposed emotion prediction with meta-learning and compared its performance with two other training methods. The second problem is that the generated music failed to consider the participants' emotions before they listened to music. We solved this challenge by constructing a system that adapted an iso principle that gradually changed the music from close to the participants' emotions to the target emotion. Our results showed that emotion prediction with meta-learning had the lowest RMSE among three methods (p < 0.016). Both a music generation system based on the iso principle and our conventional music generation system more effectively induced emotion than music generation that was not based on the emotions of the participants (p < 0.016)., journal article

Objective. Clinical diagnosis of epilepsy relies partially on identifying interictal epileptiform discharges (IEDs) in scalp electroencephalograms (EEGs). This process is expert-biased, tedious, and can delay the diagnosis procedure. Beyond automatically detecting IEDs, there are far fewer studies on automated methods to differentiate epileptic EEGs (potentially without IEDs) from normal EEGs. In addition, the diagnosis of epilepsy based on a single EEG tends to be low. Consequently, there is a strong need for automated systems for EEG interpretation. Traditionally, epilepsy diagnosis relies heavily on IEDs. However, since not all epileptic EEGs exhibit IEDs, it is essential to explore IED-independent EEG measures for epilepsy diagnosis. The main objective is to develop an automated system for detecting epileptic EEGs, both with or without IEDs. In order to detect epileptic EEGs without IEDs, it is crucial to include EEG features in the algorithm that are not directly related to IEDs. Approach. In this study, we explore the background characteristics of interictal EEG for automated and more reliable diagnosis of epilepsy. Specifically, we investigate features based on univariate temporal measures (UTMs), spectral, wavelet, Stockwell, connectivity, and graph metrics of EEGs, besides patient-related information (age and vigilance state). The evaluation is performed on a sizeable cohort of routine scalp EEGs (685 epileptic EEGs and 1229 normal EEGs) from five centers across Singapore, USA, and India. Main results. In comparison with the current literature, we obtained an improved Leave-One-Subject-Out (LOSO) cross-validation (CV) area under the curve (AUC) of 0.871 (Balanced Accuracy (BAC) of 80.9%) with a combination of three features (IED rate, and Daubechies and Morlet wavelets) for the classification of EEGs with IEDs vs. normal EEGs. The IED-independent feature UTM achieved a LOSO CV AUC of 0.809 (BAC of 74.4%). The inclusion of IED-independent features also helps, Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Signal Processing Systems

The majority of studies evaluating the effectiveness of branded CSR campaigns are concentrated and base their conclusions on data collection through self-reporting questionnaires. Although such studies provide insights for evaluating the effectiveness of CSR communication methods, analysing the message that is communicated, the communication channel used and the explicit brain responses of those for whom the message is intended, they lack the ability to fully encapsulate the problem of communicating environmental messages by not taking into consideration what the recipients' implicit brain reactions are presenting. Therefore, this study aims to investigate the effectiveness of CSR video communications relating to environmental issues through the lens of the recipients' implicit self, by employing neuroscience-based assessments. For the examination of implicit brain perception, an electroencephalogram (EEG) was used, and the collected data was analysed through three indicators identified as the most influential indicators on human behaviour. These three indicators are emotional valence, the level of brain engagement and cognitive load. The study is conducted on individuals from the millennial generation in Thessaloniki, Greece, whose implicit brain responses to seven branded commercial videos are recorded. The seven videos were a part of CSR campaigns addressing environmental issues. Simultaneously, the self-reporting results from the participants were gathered for a comparison between the explicit and implicit brain responses. One of the key findings of the study is that the explicit and implicit brain responses differ to the extent that the CSR video communications' brain friendliness has to be taken into account in the future, to ensure success. The results of the study provide an insight for the future creation process, conceptualisation, design and content of the effective CSR communication, in regard to environmental issues.

Objective. Code-modulated visual evoked potentials (c-VEP) have been consolidated in recent years as robust control signals capable of providing non-invasive brain–computer interfaces (BCIs) for reliable, high-speed communication. Their usefulness for communication and control purposes has been reflected in an exponential increase of related articles in the last decade. The aim of this review is to provide a comprehensive overview of the literature to gain understanding of the existing research on c-VEP-based BCIs, since its inception (1984) until today (2021), as well as to identify promising future research lines. Approach. The literature review was conducted according to the Preferred Reporting Items for Systematic reviews and Meta-Analysis guidelines. After assessing the eligibility of journal manuscripts, conferences, book chapters and non-indexed documents, a total of 70 studies were included. A comprehensive analysis of the main characteristics and design choices of c-VEP-based BCIs was discussed, including stimulation paradigms, signal processing, modeling responses, applications, etc. Main results. The literature review showed that state-of-the-art c-VEP-based BCIs are able to provide an accurate control of the system with a large number of commands, high selection speeds and even without calibration. In general, a lack of validation in real setups was observed, especially regarding the validation with disabled populations. Future work should be focused toward developing self-paced c-VEP-based portable BCIs applied in real-world environments that could exploit the unique benefits of c-VEP paradigms. Some aspects such as asynchrony, unsupervised training, or code optimization still require further research and development. Significance. Despite the growing popularity of c-VEP-based BCIs, to the best of our knowledge, this is the first literature review on the topic. In addition to providing a joint discussion of the advances in the field, some future lines o, PID2020-115468RB-I00, RTC2019-007350-1, 0702_MIGRAINEE_2_E

[ES] Las interfaces cerebro-computador pueden ser de gran ayuda para aquellas personas que sufren de alguna discapacidad motora severa, permitiendo así mejorar su calidad de vida. Por ello, este trabajo se basa en la realización de una comparación entre diferentes paradigmas utilizados en las interfaces cerebro-computador, los cuales en este caso son P300, Steady-State Visual Evoked Potentials y Motor Imagery. Asimismo, el objetivo principal del trabajo se basa en la implementación de un speller con el paradigma que mejores resultados obtenga, tanto a nivel de usuario como de rendimiento, tras la realizar la comparación entre ellos. Por último, la eficacia del speller se va a comprobar mediante la ejecución de una serie de pruebas en distintos usuarios, para así observar su funcionamiento independientemente del sujeto que se encuentre utilizándolo., [EN] Brain-computer interfaces can be of great help for those who suffer from a severe motor disability, thus improving their quality of life. Therefore, this work is based on a comparison between different paradigms used in brain-computer interfaces, which in this case are P300, Steady-State Visual Evoked Potentials and Motor Imagery. Likewise, the main objective of the work is based on the implementation of a speller with the paradigm that obtains the best results, both at the user and performance level, after comparing them. Finally, the effectiveness of the speller will be verified by running a series of tests on different users, in order to observe its operation regardless of the subject who is using it.

[ES] Las interfaces cerebro-computador pueden ser de gran ayuda para aquellas personas que sufren de alguna discapacidad motora severa, permitiendo así mejorar su calidad de vida. Por ello, este trabajo se basa en la realización de una comparación entre diferentes paradigmas utilizados en las interfaces cerebro-computador, los cuales en este caso son P300, Steady-State Visual Evoked Potentials y Motor Imagery. Asimismo, el objetivo principal del trabajo se basa en la implementación de un speller con el paradigma que mejores resultados obtenga, tanto a nivel de usuario como de rendimiento, tras la realizar la comparación entre ellos. Por último, la eficacia del speller se va a comprobar mediante la ejecución de una serie de pruebas en distintos usuarios, para así observar su funcionamiento independientemente del sujeto que se encuentre utilizándolo., [EN] Brain-computer interfaces can be of great help for those who suffer from a severe motor disability, thus improving their quality of life. Therefore, this work is based on a comparison between different paradigms used in brain-computer interfaces, which in this case are P300, Steady-State Visual Evoked Potentials and Motor Imagery. Likewise, the main objective of the work is based on the implementation of a speller with the paradigm that obtains the best results, both at the user and performance level, after comparing them. Finally, the effectiveness of the speller will be verified by running a series of tests on different users, in order to observe its operation regardless of the subject who is using it.

[ES] Las interfaces cerebro-computador pueden ser de gran ayuda para aquellas personas que sufren de alguna discapacidad motora severa, permitiendo así mejorar su calidad de vida. Por ello, este trabajo se basa en la realización de una comparación entre diferentes paradigmas utilizados en las interfaces cerebro-computador, los cuales en este caso son P300, Steady-State Visual Evoked Potentials y Motor Imagery. Asimismo, el objetivo principal del trabajo se basa en la implementación de un speller con el paradigma que mejores resultados obtenga, tanto a nivel de usuario como de rendimiento, tras la realizar la comparación entre ellos. Por último, la eficacia del speller se va a comprobar mediante la ejecución de una serie de pruebas en distintos usuarios, para así observar su funcionamiento independientemente del sujeto que se encuentre utilizándolo., [EN] Brain-computer interfaces can be of great help for those who suffer from a severe motor disability, thus improving their quality of life. Therefore, this work is based on a comparison between different paradigms used in brain-computer interfaces, which in this case are P300, Steady-State Visual Evoked Potentials and Motor Imagery. Likewise, the main objective of the work is based on the implementation of a speller with the paradigm that obtains the best results, both at the user and performance level, after comparing them. Finally, the effectiveness of the speller will be verified by running a series of tests on different users, in order to observe its operation regardless of the subject who is using it.

The Obstructive Sleep Apnoea Hypopnoea Syndrome (OSAH) means "cessation of breath" during the sleep hours and the sufferers often experience related changes in the electrical activity of the brain and heart. This paper describes the application of adaptive neurofuzzy inference system (ANFIS) model for automatic detection of alterations in the human electroencephalogram (EEG) activities during hypopnoea episodes. Decision making was performed in two stages: feature extraction by computation of wavelet coefficients and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. The EEG signals (pre and during hypopnoea) from three electrodes (C3, C4 and O2) were used as input patterns of the three ANFIS classifiers. To improve diagnostic accuracy, the fourth ANFIS classifier (combining ANFIS) was trained using the outputs of the three ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on detecting any possible changes in the human EEG activity due to hypopnoea (mild case of cessation of breath) occurrences were drawn through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in detecting changes in the human EEG activity clue to hypopnoea episodes. (C) 2009 Elsevier Inc. All rights reserved.

The Obstructive Sleep Apnoea Hypopnoea Syndrome (OSAH) means "cessation of breath" during the sleep hours and the sufferers often experience related changes in the electrical activity of the brain and heart. This paper describes the application of adaptive neurofuzzy inference system (ANFIS) model for automatic detection of alterations in the human electroencephalogram (EEG) activities during hypopnoea episodes. Decision making was performed in two stages: feature extraction by computation of wavelet coefficients and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. The EEG signals (pre and during hypopnoea) from three electrodes (C3, C4 and O2) were used as input patterns of the three ANFIS classifiers. To improve diagnostic accuracy, the fourth ANFIS classifier (combining ANFIS) was trained using the outputs of the three ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on detecting any possible changes in the human EEG activity due to hypopnoea (mild case of cessation of breath) occurrences were drawn through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in detecting changes in the human EEG activity clue to hypopnoea episodes. (C) 2009 Elsevier Inc. All rights reserved.

This paper presents the application of least squares support vector machines (LS-SVMs) for automatic detection of alterations in the human electroencephalogram (EEG) activities during hypopnoea episodes. The obstructive sleep apnoea hypopnoea syndrome (OSAH) means "cessation of breath" during the sleep hours and the sufferers often experience related changes in the electrical activity of the brain and heart. Decision making was performed in two stages: feature extraction by computation of autoregressive (AR) coefficients and classification by the LS-SVMs. The EEG signals (pre and during hypopnoea) from three electrodes (C3, C4 and O2) were used as input patterns of the LS-SVMs. The performance of the LS-SVMs was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed LS-SVM has potential in detecting changes in the human EEG activity due to hypopnoea episodes. (C) 2009 Elsevier Ltd. All rights reserved.

Mental illness refers to mental disorder that causes mild to severe disturbances in thoughts and behavior, resulting in the inability to cope with ordinary demands and daily life routines.  Among the wide spectrum of mental health conditions, depression was found to have the highest prevalence globally.  The current practice of depression detection depends on screening tools which are either physician-administered or self-administered and behavioral observations. Both methods are widely used but relies on subjective interpretation.  Thus, the method may lack the reliability and accuracy of detecting depression which may lead to incorrect diagnosis of medications. Many mental illness patients still suffer from their mental conditions as well as the side effects of medications during the treatment process.  Physiological measurement methods, such as electroencephalogram (EEG) measurements were found to be able to evaluate the condition of patients suffering from mental illness through recorded brain waves.  The paper sought to investigate the relationship between brainwaves and depression and to propose an alternative method to further evaluate the condition of patients who suffer from depression in a more accurate and effective way. Subjects underwent an initial screening to evaluate and classify their mental health condition based on the score obtained from the screening tools - Patient Health Questionnaire (PHQ)-9.  Subjects also underwent an EEG experiment with a given video-watching stimulus to evaluate their brain activity.  The study proves that increased depression severity subjects have shown distinctly lower Alpha waves at almost all electrode channels and comparatively lower Beta and Theta in the frontal region. This shows that there is a relationship between mental illness and brainwaves activities.  Despite that, the findings from the study showed that there were no strong association found between mean EEG amplitud

In this paper, we present a method to improve emotion recognition based on the fusion of local cortical activations and dynamic functional network patterns. We estimate the cortical activations using power spectral density (PSD) with the Burg autoregressive model. On the other hand, we estimate the functional connectivity networks by utilizing the phase locking value (PLV). The results of cortical activations and connectivity networks show different patterns across three emotions at all frequency bands. Similarly, the results of fusion significantly improve the classification rate in terms of accuracy, sensitivity, specificity and the area under the receiver operator characteristics curve (AROC), p < 0:05. The average improvement with fusion in all evaluation metrics are 6.84% and 4.1% when compared to PSD and PLV alone, respectively. The results clearly demonstrate the advantage of fusion of cortical activations with dynamic functional networks for developing human-computer interaction system in real-world applications.

[ES] La interfaz cerebro-ordenador es el camino para conectar es sistema neuromuscular y el mundo externo. El presente TFG consiste en la adquisición, procesado y clasificación de señales de electroencefalograma (EEG) de imaginación de movimientos motores (MI). La imaginación de movimientos motores es un estado cognitivo fruto de la representación interna de una acción motora específica sin actividad muscular. En primer lugar, se adquieren señales de EEG multicanal de 18 sujetos diferentes mientras la imaginación de tareas afectivas y neutrales. La tarea afectiva consiste en imaginarse apretar un cactus con la mano derecha varias veces, sintiendo una emoción desagradable. En la condición neutral, la tarea es imaginarse apretar una pelota normal o una pelota de masaje. Tras la adquisición de las señales de EEG se lleva a cabo el procesamiento de estas con MATLAB y EEGLAB. Para estudiar el incremento o disminución de la actividad neuronal se emplea la Perturbación en el Espectro Relacionada con Eventos (ERSP). Finalmente, se extraen las características que permitan clasificar las señales mediante un Análisis Discriminante Lineal de contracción (sLDA). Esta clasificación se hace separadamente para cada sujeto y para evaluar su resultado se emplea la precisión., [EN] Brain-Computer Interfaces is an alternative pathway to connect the brain with the outside world besides the neuro-muscular system. The current project consists of the acquisition, processing and classification of the signal of the motor imagery electroencephalogram (EEG). Motor imagery is a dynamic cognitive state during which the representation of a specific motor action is internally created without any muscular activity. First, 18 subjects are instructed to imagine affective, and neutral motor tasks while multichannel EEG is recorded simultaneously. In the affective MI condition, they had to imagine squeezing a cactus with their right hand several times, eliciting an unpleasant emotion. In the neutral condition their task was to imagine squeezing a ball. After the acquisition, the signal is processed with MATLAB and EEGLAB. To study the increase or decrease of neural activity Event-related Spectral Perturbation (ERSP) is calculated. Finally, the study and extraction of the features of the signal is done for the posterior Shrinkage Linear Discriminant Analysis (sLDA) classification. The classification is done separately for each subject and, to evaluate it, it is used the accuracy

Recognition and continuous monitoring of human emotions is a key problem that is spread across multiple research disciplines like electrical engineering, computer science, neuroscience, and cognitive science. Instead of the widely used method of approaching this problem from the perspective of computer vision i.e. by tracking user's facial expressions, we take a multi-modal approach. This approach utilizes various bio-sensing modalities in addition to computer vision for assessing and classifying human affects. In the process, we address various limitations—hardware and software—of such bio-sensing modalities. We evaluate the developed framework on real-world applications ranging from watching emotional multimedia content (such as videos) to driving an automobile. Our holistic framework contributes toward (1) the development of a novel multi-modal bio-sensing headset that is capable of recording, monitoring, and tracking various bio-signals in real-time, (2) the development of algorithms that utilize signal processing as well as deep learning tools for various bio-sensor and vision-based modalities for emotion recognition, (3) the utilization of such algorithms in a wide range of applications namely emotion classification, studying emotion elicitation, and monitoring driver's awareness, and (4) the performance comparison of various sensor modalities for the above applications when they are used individually or in fusion with each other. Although the above multi-modal sensor platform has been evaluated in this thesis dissertation on affective computing applications only, it is generalizable. Thus, this platform can work in various applications in the field of human-computer interaction that requires the use of bio-sensing modalities.

[ES] La interfaz cerebro-ordenador es el camino para conectar es sistema neuromuscular y el mundo externo. El presente TFG consiste en la adquisición, procesado y clasificación de señales de electroencefalograma (EEG) de imaginación de movimientos motores (MI). La imaginación de movimientos motores es un estado cognitivo fruto de la representación interna de una acción motora específica sin actividad muscular. En primer lugar, se adquieren señales de EEG multicanal de 18 sujetos diferentes mientras la imaginación de tareas afectivas y neutrales. La tarea afectiva consiste en imaginarse apretar un cactus con la mano derecha varias veces, sintiendo una emoción desagradable. En la condición neutral, la tarea es imaginarse apretar una pelota normal o una pelota de masaje. Tras la adquisición de las señales de EEG se lleva a cabo el procesamiento de estas con MATLAB y EEGLAB. Para estudiar el incremento o disminución de la actividad neuronal se emplea la Perturbación en el Espectro Relacionada con Eventos (ERSP). Finalmente, se extraen las características que permitan clasificar las señales mediante un Análisis Discriminante Lineal de contracción (sLDA). Esta clasificación se hace separadamente para cada sujeto y para evaluar su resultado se emplea la precisión., [EN] Brain-Computer Interfaces is an alternative pathway to connect the brain with the outside world besides the neuro-muscular system. The current project consists of the acquisition, processing and classification of the signal of the motor imagery electroencephalogram (EEG). Motor imagery is a dynamic cognitive state during which the representation of a specific motor action is internally created without any muscular activity. First, 18 subjects are instructed to imagine affective, and neutral motor tasks while multichannel EEG is recorded simultaneously. In the affective MI condition, they had to imagine squeezing a cactus with their right hand several times, eliciting an unpleasant emotion. In the neutral condition their task was to imagine squeezing a ball. After the acquisition, the signal is processed with MATLAB and EEGLAB. To study the increase or decrease of neural activity Event-related Spectral Perturbation (ERSP) is calculated. Finally, the study and extraction of the features of the signal is done for the posterior Shrinkage Linear Discriminant Analysis (sLDA) classification. The classification is done separately for each subject and, to evaluate it, it is used the accuracy

[ES] La interfaz cerebro-ordenador es el camino para conectar es sistema neuromuscular y el mundo externo. El presente TFG consiste en la adquisición, procesado y clasificación de señales de electroencefalograma (EEG) de imaginación de movimientos motores (MI). La imaginación de movimientos motores es un estado cognitivo fruto de la representación interna de una acción motora específica sin actividad muscular. En primer lugar, se adquieren señales de EEG multicanal de 18 sujetos diferentes mientras la imaginación de tareas afectivas y neutrales. La tarea afectiva consiste en imaginarse apretar un cactus con la mano derecha varias veces, sintiendo una emoción desagradable. En la condición neutral, la tarea es imaginarse apretar una pelota normal o una pelota de masaje. Tras la adquisición de las señales de EEG se lleva a cabo el procesamiento de estas con MATLAB y EEGLAB. Para estudiar el incremento o disminución de la actividad neuronal se emplea la Perturbación en el Espectro Relacionada con Eventos (ERSP). Finalmente, se extraen las características que permitan clasificar las señales mediante un Análisis Discriminante Lineal de contracción (sLDA). Esta clasificación se hace separadamente para cada sujeto y para evaluar su resultado se emplea la precisión., [EN] Brain-Computer Interfaces is an alternative pathway to connect the brain with the outside world besides the neuro-muscular system. The current project consists of the acquisition, processing and classification of the signal of the motor imagery electroencephalogram (EEG). Motor imagery is a dynamic cognitive state during which the representation of a specific motor action is internally created without any muscular activity. First, 18 subjects are instructed to imagine affective, and neutral motor tasks while multichannel EEG is recorded simultaneously. In the affective MI condition, they had to imagine squeezing a cactus with their right hand several times, eliciting an unpleasant emotion. In the neutral condition their task was to imagine squeezing a ball. After the acquisition, the signal is processed with MATLAB and EEGLAB. To study the increase or decrease of neural activity Event-related Spectral Perturbation (ERSP) is calculated. Finally, the study and extraction of the features of the signal is done for the posterior Shrinkage Linear Discriminant Analysis (sLDA) classification. The classification is done separately for each subject and, to evaluate it, it is used the accuracy

Playing games always been interest in child and adolescent. Technology specially internet given new dimension to gaming and over the decade gaming over internet attracted major percentage of player. Excessive gaming when not controlled also lead to the behavioral dysfunction along with physiological issues. World Health Organization (WHO) has added Internet Gaming Disorder (IGD) as potential mental disorder recently in Diagnostic and Statistical Manual for Mental Disorders (DSM-5) as research field to include in main manual. Research in this field gained interest, but about diagnostic criteria debate is still on in research group. For diagnosis of IGD, not only subjective symptoms but its underlying neurobiology needs to be included. Major behavioral addiction shares its symptoms with different addiction, which can be seen into IGD and Alcohol Use Disorder (AUD) also. In this paper, we aim to identify potential biomarkers to explore relationships between AUD and IGD symptoms using EEG signals recordings. Noise and other artifacts are removed during pre-processing and bands splitted in intermediate steps of proposed method. Spectral features are potential indicator in EEG data, for all five EEG bands power spectral density is computed and visible difference found in mean power on most of the channels for all bands. Across theta and beta bands, mean absolute power is greatly reduced in the frontal and prefrontal brain region. The results indicate symptoms are matching with impulsive behavior. Based on statistical analysis across we found similarities in EEG features in terms of emotional imbalance, high arousal, slow response inhibition and our study indicate Alcohol Use Disorder and IGD are correlated.

hronization (epilepsy) [11], degenerates (dementias) or simply projects genetic preconditions. In this paper, a brief study is carried out on certain patterns that are derived from electroencephalographic signals, taking into account Reny Entropy, as a measure of disorder. These patterns allow differentiating a patient considered healthy from one with a neurological condition. The results achieved contribute to the early diagnosis of certain neurological conditions. In addition, the use of some mathematical techniques that facilitate the interpretation of the results by clinical specialists., El cerebro humano es un sistema complejo que progresivamente envejece, alcanza una sincronización generalizada (epilepsia) [11], se degenera (demencias) o simplemente proyecta precondicionamientos genéticos. En el presente trabajo se realiza un breve estudio acerca de ciertos patrones que se derivan de las señales electroencefalográficas, teniendo en cuenta la Entropía de Renyi, como una medida del desorden. Dichos patrones permiten diferenciar a un paciente considerado sano de uno con una afección neurológica. Los resultados conseguidos contribuyen al diagnóstico precoz de ciertas afecciones neurológicas. Además, se emplean algunas técnicas matemáticas que facilitan la interpretación de los resultados por los especialistas clínicos.

Recognition and continuous monitoring of human emotions is a key problem that is spread across multiple research disciplines like electrical engineering, computer science, neuroscience, and cognitive science. Instead of the widely used method of approaching this problem from the perspective of computer vision i.e. by tracking user's facial expressions, we take a multi-modal approach. This approach utilizes various bio-sensing modalities in addition to computer vision for assessing and classifying human affects. In the process, we address various limitations—hardware and software—of such bio-sensing modalities. We evaluate the developed framework on real-world applications ranging from watching emotional multimedia content (such as videos) to driving an automobile. Our holistic framework contributes toward (1) the development of a novel multi-modal bio-sensing headset that is capable of recording, monitoring, and tracking various bio-signals in real-time, (2) the development of algorithms that utilize signal processing as well as deep learning tools for various bio-sensor and vision-based modalities for emotion recognition, (3) the utilization of such algorithms in a wide range of applications namely emotion classification, studying emotion elicitation, and monitoring driver's awareness, and (4) the performance comparison of various sensor modalities for the above applications when they are used individually or in fusion with each other. Although the above multi-modal sensor platform has been evaluated in this thesis dissertation on affective computing applications only, it is generalizable. Thus, this platform can work in various applications in the field of human-computer interaction that requires the use of bio-sensing modalities.

In this study, we propose the authentication of individuals using electroencephalograms (EEGs) evoked by the application of invisible visual stimuli. In our previous study, we introduced a wavelet transform, which is a time-frequency analysis method, and applied it to extract features, including time information, to enable more accurate discrimination between individuals. An equal error rate (EER) of 9.4 % was achieved using Euclidean distance matching. In this paper, we introduce a machine learning-based approach in order to further improve the verification performance. An EER of 8.1 % is achieved by the proposed method after training the constituent neural networks using ensemble learning with 30 networks.

In this study, we propose the authentication of individuals using electroencephalograms (EEGs) evoked by the application of invisible visual stimuli. In our previous study, we introduced a wavelet transform, which is a time-frequency analysis method, and applied it to extract features, including time information, to enable more accurate discrimination between individuals. An equal error rate (EER) of 9.4 % was achieved using Euclidean distance matching. In this paper, we introduce a machine learning-based approach in order to further improve the verification performance. An EER of 8.1 % is achieved by the proposed method after training the constituent neural networks using ensemble learning with 30 networks.

hronization (epilepsy) [11], degenerates (dementias) or simply projects genetic preconditions. In this paper, a brief study is carried out on certain patterns that are derived from electroencephalographic signals, taking into account Reny Entropy, as a measure of disorder. These patterns allow differentiating a patient considered healthy from one with a neurological condition. The results achieved contribute to the early diagnosis of certain neurological conditions. In addition, the use of some mathematical techniques that facilitate the interpretation of the results by clinical specialists., El cerebro humano es un sistema complejo que progresivamente envejece, alcanza una sincronización generalizada (epilepsia) [11], se degenera (demencias) o simplemente proyecta precondicionamientos genéticos. En el presente trabajo se realiza un breve estudio acerca de ciertos patrones que se derivan de las señales electroencefalográficas, teniendo en cuenta la Entropía de Renyi, como una medida del desorden. Dichos patrones permiten diferenciar a un paciente considerado sano de uno con una afección neurológica. Los resultados conseguidos contribuyen al diagnóstico precoz de ciertas afecciones neurológicas. Además, se emplean algunas técnicas matemáticas que facilitan la interpretación de los resultados por los especialistas clínicos.

Playing games always been interest in child and adolescent. Technology specially internet given new dimension to gaming and over the decade gaming over internet attracted major percentage of player. Excessive gaming when not controlled also lead to the behavioral dysfunction along with physiological issues. World Health Organization (WHO) has added Internet Gaming Disorder (IGD) as potential mental disorder recently in Diagnostic and Statistical Manual for Mental Disorders (DSM-5) as research field to include in main manual. Research in this field gained interest, but about diagnostic criteria debate is still on in research group. For diagnosis of IGD, not only subjective symptoms but its underlying neurobiology needs to be included. Major behavioral addiction shares its symptoms with different addiction, which can be seen into IGD and Alcohol Use Disorder (AUD) also. In this paper, we aim to identify potential biomarkers to explore relationships between AUD and IGD symptoms using EEG signals recordings. Noise and other artifacts are removed during pre-processing and bands splitted in intermediate steps of proposed method. Spectral features are potential indicator in EEG data, for all five EEG bands power spectral density is computed and visible difference found in mean power on most of the channels for all bands. Across theta and beta bands, mean absolute power is greatly reduced in the frontal and prefrontal brain region. The results indicate symptoms are matching with impulsive behavior. Based on statistical analysis across we found similarities in EEG features in terms of emotional imbalance, high arousal, slow response inhibition and our study indicate Alcohol Use Disorder and IGD are correlated.

Objectives: Develop an effective and intuitive Graphical User Interface (GUI) for a Brain–Computer Interface (BCI) system, that achieves high classification accuracy and Information Transfer Rates (ITRs), while using a simple classification technique. Objectives also include the development of an output device, that is capable of real time execution of the selected commands. Methods: A region based T9 BCI system with familiar face presentation cues capable of eliciting strong P300 responses was developed. Electroencephalogram (EEG) signals were collected from the Oz, POz, CPz and Cz electrode locations on the scalp and subsequently filtered, averaged and used to extract two features. These feature sets were classified using the Nearest Neighbour Approach (NNA). To complement the developed BCI system, a ‘drone prototype’ capable of simulating six different movements, each over a range of eight distinct selectable distances, was also developed. This was achieved through the construction of a body with 4 movable legs, capable of tilting the main body forward, backward, up and down, as well as a pointer capable of turning left and right. Results: From ten participants, with normal or corrected to normal vision, an average accuracy of 91.3 ± 4.8% and an ITR of 2.2 ± 1.1 commands/minute (12.2 ± 6.0 bits/minute) was achieved. Conclusion: The proposed system was shown to elicit strong P300 responses. When compared to similar P300 BCI systems, which utilise a variety of more complex classifiers, competitive accuracy and ITR results were achieved, implying the superiority of the proposed GUI. Significance: This study supports the hypothesis that more research, time and care should be taken when developing GUIs for BCI systems.

Mental illness refers to mental disorder that causes mild to severe disturbances in thoughts and behavior, resulting in the inability to cope with ordinary demands and daily life routines.  Among the wide spectrum of mental health conditions, depression was found to have the highest prevalence globally.  The current practice of depression detection depends on screening tools which are either physician-administered or self-administered and behavioral observations. Both methods are widely used but relies on subjective interpretation.  Thus, the method may lack the reliability and accuracy of detecting depression which may lead to incorrect diagnosis of medications. Many mental illness patients still suffer from their mental conditions as well as the side effects of medications during the treatment process.  Physiological measurement methods, such as electroencephalogram (EEG) measurements were found to be able to evaluate the condition of patients suffering from mental illness through recorded brain waves.  The paper sought to investigate the relationship between brainwaves and depression and to propose an alternative method to further evaluate the condition of patients who suffer from depression in a more accurate and effective way. Subjects underwent an initial screening to evaluate and classify their mental health condition based on the score obtained from the screening tools - Patient Health Questionnaire (PHQ)-9.  Subjects also underwent an EEG experiment with a given video-watching stimulus to evaluate their brain activity.  The study proves that increased depression severity subjects have shown distinctly lower Alpha waves at almost all electrode channels and comparatively lower Beta and Theta in the frontal region. This shows that there is a relationship between mental illness and brainwaves activities.  Despite that, the findings from the study showed that there were no strong association found between mean EEG amplitud

Electroencephalogram (EEG)-based BrainComputer Interface (BCI) technology needs efficient algorithms to find distinct EEG patterns/features to realize applications with distinct high-dimensional control signals. This paper proposes a novel feature extraction methodology for separating EEG patterns associated right hand motor imagery performed towards left and right directions. The most discriminative subject-specific feature set is chosen based on Fisher's ratio of absolute phase values of EEG in 6 low frequency sub bands. Using this, the proposed BCI system is capable of providing better classification results than state-ofthe-art methodology with fixed channels, fusing absolute phase and spatial features from selected subject-specific discriminative channels. Experimental analysis shows that though parietal lobe is vital in providing distinguishable features, the channel set that provide maximum accuracy, is highly subject-specific. Hence, subject-specific BCI that can decode finer parameters of imagined movement are feasible and further research to understand the activations elicited in parietal lobe can contribute towards robust BCI systems. © 2019 IEEE.

[ES] El presente proyecto está enfocado en el empleo de una interfaz cerebro-computador (BCI- Brain Computer Interface), para el control de una aplicación, utilizando el amplificador EEG Enobio de 8 canales. Este sistema utiliza las señales del electroencefalograma (EGG) generadas por el cerebro humano y permite que una persona pueda interactuar con su entorno sin necesidad de mover ningún músculo. Los sistemas BCI pueden basarse en numerosos paradigmas: P300, Motor Imaginary Pero para esta aplicación se ha elegido el paradigma SSVEP (Steady State Visual-Evoked Potentials), que se considera como una respuesta cortical continua provocada por estímulos visuales repetitivos (RVS). Es decir, los SSVEP contienen la misma frecuencia fundamental que el RVS al que esté mirando el usuario. Por tanto, este tipo de aplicaciones están destinadas principalmente a personas con problemas de movilidad reducida. Para llevarlo a cabo, el proyecto se divide en dos partes; Por un lado, se ha utilizado RVS con los estímulos configurados en la pantalla de un computador. Por otra parte usando el entorno LabVIEW y la DAQ USB-6008, una matriz de diodos LEDs han actuado como RVS. Con ambos programas se analizan que estímulos presentan unos resultados más fiables. Por otro lado, se ha diseñado e implementado una aplicación para el control, utilizando como RVS los LEDs. De esta manera, se ha desarrollado una aplicación en LabVIEW, que, mediante la comunicación con OpenViBE, extrae la frecuencia del SSVEP, y en función de esta, actuará encendiendo otro determinado LED, previamente definido., [EN] The present project is focused on the use of a brain-computer interface (BCI), for the control of an application, using the Enobio 8-channel EEG amplifier. This system uses the signals of the electroencephalogram (EGG) generated by the human brain and allows a person to interact with their environment without moving any muscle. BCI systems can be based on numerous paradigms: P300, Motor Imaginary... For this application we have chosen the SSVEP (Steady State Visual-Evoked Potentials) paradigm, which is considered as a continuous cortical response caused by repetitive visual stimuli (RVS). That is, SSVEPs contain the same fundamental frequency as the RVS that the user is looking at. Therefore, this type of applications are mainly intended for people with reduced mobility problems.To carry it out, the project is divided into two parts; On the one hand, RVS has been used with the stimuli configured on the screen of a computer. On the other hand, using the LabVIEW environment and the USB-6008 DAQ, a matrix of LED diodes has acted as RVS. With both programs, it is analyzed which stimuli present the most reliable results.On the other hand, an application for control has been designed and implemented, using the LEDs as RVS. In this way, an application has been developed in LabVIEW, which, through communication with OpenViBE, extracts the frequency of the SSVEP, and depending on this, it will act by igniting another determined LED, previously defined.

Brain-computer interface (BCI) technology shows potential for application to motor rehabilitation therapies that use neural plasticity to restore motor function and improve quality of life of stroke survivors. However, it is often difficult for BCI systems to provide the variety of control commands necessary for multi-task real-time control of soft robot naturally. In this study, a novel multimodal human-machine interface system (mHMI) is developed using combinations of electrooculography (EOG), electroencephalography (EEG), and electromyogram (EMG) to generate numerous control instructions. Moreover, we also explore subject acceptance of an affordable wearable soft robot to move basic hand actions during robot-assisted movement. Six healthy subjects separately perform left and right hand motor imagery, looking-left and looking-right eye movements, and different hand gestures in different modes to control a soft robot in a variety of actions. The results indicate that the number of mHMI control instructions is significantly greater than achievable with any individual mode. Furthermore, the mHMI can achieve an average classification accuracy of 93.83% with the average information transfer rate of 47.41 bits/min, which is entirely equivalent to a control speed of 17 actions per minute. The study is expected to construct a more user-friendly mHMI for real-time control of soft robot to help healthy or disabled persons perform basic hand movements in friendly and convenient way.

Human identification by using Electroencephalogram is becoming promising field and reliable to improve security systems. It is difficult to acquire EEG at a certain mental condition always such as concentration or relaxation. This paper represents a simple model to identify individuals and finding most effective primary color by using features of EEG by means of color stimuli. A comparison between primary and secondary colors for identification has also been made. Standard additive primary colors blue, green, red and one secondary color yellow were selected for experiment. Four neural networks were built by extracting various features of EEG in the domain of time and frequency. All artificial neural networks showed satisfactory performance with minimum mean square error for identification. Among the four selected colors blue color based ANN showed minimum mean square error of 6.238×10-08.