SSVEP-based BCIs: study of classifier stability over time and effects of human learning on classification accuracy

International audienceBrain-computer interfaces (BCI) based on steady-state visual evoked potentials (SSVEP) enable a user to control an application by focusing his/her attention on visual stimuli blinking at specific frequencies. This technique of interaction can enable people suffering from severe motor disabilities to improve their quality of life through regaining a partial autonomy. According to literature, each usage session of a SSVEP-based BCI integrates a calibration phase aimed in particular at computing classifier's parameters. Our objective is to evaluate if the same parameters could be used during several sessions, in order to avoid performing systematically a calibration phase, which is very restrictive for the user. To do so, we analyze stability of classification results over time. On the other hand, the data acquired during our experiments were used to study the possible effects of human learning on interface performance and to confirm or not the state of the art knowledge on this subject. According to literature, SSVEP-based BCIs work well from the first use and their performances do not improve with subject's experience

Human expert supervised selection of time-frequency intervals in EEG signals for brain–computer interfacing

International audienceIn the context of brain–computer interfacing based on motor imagery, we propose a method allowing a human expert to supervise the selection of user-specific time-frequency features computed from EEG signals. Indeed, in the current state of BCI research, there is always at least one expert involved in the first stages of any experimentation. On one hand, such experts really appreciate keeping a certain level of control on the tuning of user-specific parameters. On the other hand, we will show that their knowledge is extremely valuable for selecting a sparse set of significant time-frequency features. The expert selects these features through a visual analysis of curves highlighting differences between electroencephalographic activities recorded during the execution of various motor imagery tasks. We compare our method to the basic common spatial patterns approach and to two fully-automatic feature extraction methods, using dataset 2A of BCI competition IV. Our method (mean accuracy m = 83.71 ± 14.6 std) outperforms the best competing method (m = 79.48 ± 12.41 std) for 6 of the 9 subjects

Pre-stimulus EEG engagement ratio predicts inattentional deafness to auditory alarms in realistic flight simulator

Accidents analyses and research conducted in simulated or real flight conditions indicated that inattentional deafness (ID) to auditory alarms could take place in the cockpit [1, 3]. Previous findings indicated that single trial event related potentials analyses over the electrophysiological signals could be used to detect ID [6]. However, a more relevant approach to improve flight safety would be to predict the occurrence of this phenomenon, thus paving the way to the design of “adaptive cockpit”. To that end, a relevant approach is to measure shift in neural oscillations as a measure of atten- tion [2]. In the present study, we propose to predict ID in a flight simulator by assessing the engagement ratio computed from pre-stimulus EEG signals. This ratio aggregates three main frequency bands (alpha, beta, theta) [5] and has been shown to be related to episodes of ID [4]

Hybrid BCI Coupling EEG and EMG for Severe Motor Disabilities

AbstractIn this paper, we are studying hybrid Brain-Computer Interfaces (BCI) coupling joystick data, electroencephalogram (EEG – electrical activity of the brain) and electromyogram (EMG – electrical activity of muscles) activities for severe motor disabilities. We are focusing our study on muscular activity as a control modality to interact with an application. We present our data processing and classification technique to detect right and left hand movements. EMG modality is well adapted for DMD patients, because less strength is needed to detect movements in contrast to conventional interfaces like joysticks. Across virtual reality tools, we believe that users will be more able to understand how to interact with such kind of interactive systems. This first part of our study report some very good results concerning the detection of hand movements, according to muscular channel, on healthy subjects

Monitoring pilot’s cognitive fatigue with engagement features in simulated and actual flight conditions using an hybrid fNIRS-EEG passive BCI

There is growing interest for implementing tools to monitor cognitive performance in naturalistic environments. Recent technological progress has allowed the development of new generations of brain imaging systems such as dry electrodes electroencephalography (EEG) and functional near infrared spec- troscopy (fNIRS) to investigate cortical activity in a variety of human tasks out of the laboratory. These highly portable brain imaging devices offer interesting prospects to implement passive brain computer interfaces (pBCI) and neuroadaptive technology. We developed a fNIRS-EEG based pBCI to monitor cognitive fatigue using engagement related features (EEG engagement ratio and wavelet coherence fNIRS based metrics). This mental state is known to impair cognitive performance and can jeopardize flight safety. In this preliminary study, four participants were asked to perform four identical traffic patterns along with a secondary auditory task in a flight simulator and in an actual light aircraft. The two first traffic patterns were considered as the low cognitive fatigue class, whereas the two last traffic patterns were considered as the high cognitive fatigue class. As expected, the pilots missed more auditory targets in the second part than in the first part of the experiment. Classification accuracy reached 87.2% in the flight simulator condition and 87.6% in the actual flight conditions when combining the two modalities. This study demonstrates that fNIRS and EEG-based pBCIs can monitor mental states in operational and noisy environments

Interface cerveau-machine hybride pour pallier le handicap causé par la myopathie de Duchenne

Brain-machine interfaces (BMI) have been considered since many years as the most promising approach to the palliation of severe motor handicap. This thesis describes a hybrid brain-machine interface, designed specifically for patients suffering from Duchenne muscular dystrophy. Our hybrid BMI uses signals recorded by electroencephalography (EEG), electromyography (EMG), and joystick sensors. Signal processing enables the BCI to detect a movement or movement intent at different levels of the motor command chain. Joysticks are used as long as the patient is able to activate them, then when motricity deteriorates with the disease evolution, the hybrid BMI takes EMG signals into account and finally EEG signals.We have developed an original method for processing EEG signals, allowing the system to select features that a human expert considers as the most discriminant. Performance has been assessed on a data set used as a reference in the BMI community, as well as on data that we have recorded from healthy subjects in our laboratory. Our hybrid BMI controls the trajectory of a moving object – either real or virtual – through three actions, corresponding to a movement or an intent of movement of the right hand, the left hand, or both hands simultaneously. An additional degree of freedom can be considered by integrating the detection of attempted feet movements.La palliation du handicap moteur est la principale application actuelle des interfaces cerveau-machine (ICM). Cette thèse décrit une interface cerveau-machine hybride, conçue spécifiquement pour des patients souffrant de myopathie de Duchenne. Notre ICM hybride exploite les signaux issus de capteurs électroencéphalographiques (EEG), électromyographiques (EMG), et de joysticks. Leur traitement nous permet de détecter un mouvement ou une intention de mouvement à différents niveaux de la commande motrice. Les signaux joysticks sont utilisés tant que le patient est capable de les activer, puis à mesure que la motricité se dégrade avec l’évolution de la maladie, l’ICM hybride prend en compte les signaux EMG et enfin les signaux EEG.Nous avons développé une méthode originale de traitement des signaux EEG, qui permet à un expert humain de sélectionner les valeurs caractéristiques qui lui semblent les plus discriminantes. Les performances de cette méthode ont été évaluées sur une base de données qui sert de référence dans la communauté BCI, ainsi que sur des données que nous avons enregistrées sur des sujets sains. Notre ICM hybride permet le contrôle de trajectoire d’un mobile à partir de trois actions, correspondant à un mouvement ou une intention de mouvement de la main droite, de la main gauche, et des deux mains simultanément. Un degré de liberté supplémentaire peut être envisagé en intégrant la détection d’une intention de mouvementdes pieds

Hybrid brain-machine interface to palliate handicap caused by Duchenne muscular dystrophy

La palliation du handicap moteur est la principale application actuelle des interfaces cerveau-machine (ICM). Cette thèse décrit une interface cerveau-machine hybride, conçue spécifiquement pour des patients souffrant de myopathie de Duchenne. Notre ICM hybride exploite les signaux issus de capteurs électroencéphalographiques (EEG), électromyographiques (EMG), et de joysticks. Leur traitement nous permet de détecter un mouvement ou une intention de mouvement à différents niveaux de la commande motrice. Les signaux joysticks sont utilisés tant que le patient est capable de les activer, puis à mesure que la motricité se dégrade avec l’évolution de la maladie, l’ICM hybride prend en compte les signaux EMG et enfin les signaux EEG. Nous avons développé une méthode originale de traitement des signaux EEG, qui permet à un expert humain de sélectionner les valeurs caractéristiques qui lui semblent les plus discriminantes. Les performances de cette méthode ont été évaluées sur une base de données qui sert de référence dans la communauté ICM, ainsi que sur des données que nous avons enregistrées sur des sujets sains. Notre ICM hybride permet le contrôle de trajectoire d’un mobile à partir de trois actions, correspondant à un mouvement ou une intention de mouvement de la main droite, de la main gauche, et des deux mains simultanément. Un degré de liberté supplémentaire peut être envisagé en intégrant la détection d’une intention de mouvement des pieds.Brain-machine interfaces (BMI) have been considered since many years as the most promising approach to the palliation of severe motor handicap. This thesis describes a hybrid brain-machine interface, designed specifically for patients suffering from Duchenne muscular dystrophy. Our hybrid BMI uses signals recorded by electroencephalography (EEG), electromyography (EMG), and joystick sensors. Signal processing enables the hybrid BMI to detect a movement or movement intent at different levels of the motor command chain. Joysticks are used as long as the patient is able to activate them, then when motricity deteriorates with the disease evolution, the hybrid BMI takes EMG signals into account and finally EEG signals. We have developed an original method for processing EEG signals, allowing the system to select features that a human expert considers as the most discriminant. Performance has been assessed on a data set used as a reference in the BMI community, as well as on data that we have recorded from healthy subjects in our laboratory. Our hybrid BMI controls the trajectory of a moving object – either real or virtual – through three actions, corresponding to a movement or an intent of movement of the right hand, the left hand, or both hands simultaneously. An additional degree of freedom can be considered by integrating the detection of attempted feet movements

BCI exploitant les SSVEP : étude de la stabilité du classifieur dans le temps et des effets de l’apprentissage humain sur les performances de classification

National audienceLes interfaces cerveau-ordinateur (ou BCI pour Brain-Computer Interfaces) exploitant les potentiels évoqués visuels stables (ou SSVEP pour Steady-State Visual Evoked Potentials) permettent le contrôle d'une application par l'intermédiaire de stimuli visuels clignotant a différentes fréquences. Cette modalité d'interaction pourrait permettre à des personnes souffrant d'un handicap moteur sévère d'améliorer leur qualité de vie en regagnant une autonomie partielle. D'après les travaux relatés dans la littérature, chaque session d'utilisation d'une BCI-SSVEP intègre une phase de calibrage visant notamment a régler les paramètres d'un classifieur. Notre objectif est d'évaluer si les mêmes paramètres peuvent être utilisés pendant plusieurs sessions, ce qui éviterait d'introduire systématiquement une phase de calibrage, très contraignante pour l'utilisateur. Pour ce faire, nous analysons la stabilité dans le temps des résultats de classification. D'autre part, les données acquises sont mises a profit pour étudier dans une secondé étude les effets de l'apprentissage humain sur les performances de l'interface et de confirmer ou non l'état de l'art à ce sujet. D'après la littérature, les BCI-SSVEP fonctionnent correctement dès la première utilisation et leurs performances ne s'améliorent pas avec l'expérience de l'utilisateur

Supervision of time-frequency features selection in EEG signals by a human expert for brain–computer interfacing based on motor imagery

International audienceIn the context of brain–computer interfacing based on motor imagery, we propose a method which allows an expert to select manually time-frequency features. This selection is performed specifically for each subject, by analysing a set of curves that emphasize differences of brain activity recorded from electroencephalographic signals during the execution of various motor imagery tasks. We will show that expert knowledge is very valuable to supervise the selection of a sparse set of significant time-frequency features. Features selection is performed through a graphical user interface to allow an easy access to experts with no specific programming skills. In this paper, we compare our method with three fully-automatic features selection methods, using dataset 2A of BCI competition IV. Results are better for five of the nine subjects compared to the best competing method

Sélection par un expert humain des intervalles temps-fréquence dans le signal EEG pour les interfaces cerveau–ordinateur

National audienceDans le cadre des interfaces cerveau-ordinateur utilisant l'imagination motrice, nous proposons une méthode permettant a un expert humain de superviser, spécifiquement pour chaque utilisateur, la sélection d'intervalles temps-fréquence à partir des signaux EEG. En effet, en l'état actuel de la recherche sur les BCI, on trouve au moins un expert impliqué dans les premières étapes de l'expérimentation. D'une part, de tels experts apprécient de conserver un certain niveau de contrôle pour la sélection des paramètres spécifiques a l'utilisateur. D'autre part, nous verrons que leurs connaissances sont grandement profitables pour la sélection d'un ensemble parcimonieux d'intervalles temps-fréquence pertinents. Les experts sélectionnent ces attributs au travers d'une analyse visuelle d'un ensemble de courbes qui met en évidence les différences au sein des signaux EEG enregistrés pendant l'imagination de diverses tâches motrices. Nous comparons notre méthode à une approche CSP (Common Spatial Pattern) basique et à deux méthodes d'extraction de valeurs caractéristiques entièrement automatiques, en utilisant le jeu de données 2A de la compétition BCI IV. La méthode proposée (taux de bonne classification moyen m = 83.71 ± 14.6 σ) donne de meilleurs résultats pour 6 sujets sur 9 comparée à la méthode automatique la plus performante (m = 79.48 ± 12.41 σ)