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

Interfaces cerveau-ordinateur et jeux sérieux : adaptation aux patients schizophrènes

National audienceBrain-computer interfaces (BCI) can be used as a rehabilitation tool for a subject with disabilities. Their use for compensation of severe motor disabilities has already been evaluated. Here we propose to use a BCI for rehabilitation purposes in the framework of mental handicap caused by schizophrenia. We plan to use serious games for patient rehabilitation through a reduction of associated psychiatric disorders. We focus on the necessity of adapting the BCI to this specific situation and on the importance of involving the patient early in the process, i.e. in the development stage, in order to maximize performance and facilitate future rehabilitation.Les interfaces cerveau-ordinateur (BCI) peuvent être utilisées comme outil de rééducation pour un sujet en situation de handicap. Leur utilisation dans le cadre d’un handicap moteur a déjà fait ses preuves. Nous proposons ici de les utiliser dans le cadre d’un handicap psychique : la schizophrénie, au travers de jeux sérieux pour la rééducation des troubles psychiques associés. Nous soulignons l’importance d’adapter les BCI utilisées au handicap mais aussi l’importance d’impliquer le patient dès le début du développement des interfaces afin de maximiser ses performances et ainsi faciliter sa réadaptation

Leaving the lab: a portable and quickly tunable BCI

Although many systems for palliative communication based on non-invasive BCIs have been developped during the last few years, very few projects aim at leaving the research labs and hospitals for helping patients at home. Jon Wolpaw's team at the Wadworth Center1 has developped a portable BCI that has now been used for more than one year on a daily basis by 5 people suffering from ALS. This experiment shows that highly handicaped people greatly benefit from such BCIs that they tend to use during long periods -- between 5 an 8 hours a day -- for communicating with their loved ones, for surfing the web, or reading and writing emails. We also aim at being able to leave the lab with this now mature technology for screening handicaped people at home. This would allow checking easily if a patient can use efficiently a BCI without requiring him to come to the hospital or to a specialized laboratory. From the hardware point of view, this home-screening requires a BCI setup that can be used in any situation: portable, fully autonomous and battery powered. From the software point of view, the machine learning techniques that adapt the BCI to the individual must provide a "good" result within a few seconds rather than an "optimal" result after several minutes or hours of processing

Interfaces cerveau-ordinateur et rééducation fonctionnelle: étude de cas chez un patient hémiparésique

National audienceLes interfaces cerveau-ordinateur (BCIs: Brain-Computer Interfaces) utilisent l'activité cérébrale d'un individu pour dialoguer avec un ordinateur. L'aide à la communication (outil d'épellation, interface domotique) et la récupération du mouvement (contrôle d'une prothèse ou d'un robot) sont les applications les plus fréquentes des BCIs dans le domaine de l'assistance. L'étude de cas présentée dans cet article montre que les BCIs peuvent également être utilisées dans une approche thérapeutique par neurofeedback pour la rééducation ou la récupération fonctionnelle. Nous décrivons, dans un premier temps, les utilisations thérapeutiques connues des interfaces cerveau-ordinateur. Puis nous présentons une expérience clinique durant laquelle une BCI a été utilisée comme outil d'aide à la rééducation motrice par un patient atteint d'une hémiparésie du côté droit

Focusing on human factors while designing a BMI room

International audienceThe research in Brain Machine Interfaces (BMIs), although in rapid expansion, must still be considered at the experimental level since no widely available BMI system exists for helping people with motor disabilities in everyday life. Transferring BMI applications from laboratories to dedicated clinical services - and later to patient homes - implies, first of all, the specification of perfectly adapted experimental conditions including all the human factors. Our paper surveys various criteria that must be taken into account while designing a room dedicated to BMI experimentation from the ergonomic point of view, as well as adapted experimental protocols. This related work emphasizes the need and the complexity of a global and multidisciplinary approach which places human factors at the centre of the concerns

Classification des potentiels évoqués par corrélation de Pearson dans une interface cerveau-ordinateur

National audienceDans cette communication, nous décrivons et évaluons les performances d'une technique d'apprentissage des coefficients d'un classifieur linéaire utilisé dans une interface cerveau-ordinateur. Les signaux de l'électroencéphalogramme d'un individu sont analysés au moyen de cette technique afin de mettre en évidence les réponses de ce dernier à des stimuli visuels. Le traitement et la classification des signaux sont utilisés afin d'implanter un système de communication palliative permettant à l'individu d'épeler des mots. Les performances de la méthode de classification ont été évaluées par une expérimentation sur huit personnes

Classification of evoked potentials by Pearson's correlation in a brain-computer interface

International audienceIn this paper, we describe and evaluate the performance of a linear classifier learning technique for use in a brain-computer interface. Electroencephalogram (EEG) signals acquired from individual subjets are analyzed with this technique in order to detect responses to visual stimuli. Signal processing and classification are used for implementing a palliative communication system which allows the individual to spell words. Performance with this technique is evaluated on data collected from eight individuals

fMRI-based neurofeedback strategies and the way forward to treating phasic psychiatric symptoms

Auditory verbal hallucinations (AVH) are the perfect illustration of phasic symptoms in psychiatric disorders. For some patients and in some situations, AVH cannot be relieved by standard therapeutic approaches. More advanced treatments are needed, among which neurofeedback, and more specifically fMRI-based neurofeedback, has been considered. This paper discusses the different possibilities to approach neurofeedback in the specific context of phasic symptoms, by highlighting the strengths and weaknesses of the available neurofeedback options. It concludes with the added value of the recently introduced information-based neurofeedback. Although requiring an online fMRI signal classifier, which can be quite complex to implement, this neurofeedback strategy opens a door toward an alternative treatment option for complex phasic symptomatology

A comparison of classification techniques for the P300 speller

International audienceThis study assesses the relative performance characteristics of five established classification techniques on data collected using the P300 Speller paradigm, originally described by Farwell and Donchin (1988 Electroenceph. Clin. Neurophysiol. 70 510). Four linear methods: Pearson's correlation method (PCM), Fisher's linear discriminant (FLD), stepwise linear discriminant analysis (SWLDA) and a linear support vector machine (LSVM); and one nonlinear method: Gaussian kernel support vector machine (GSVM), are compared for classifying offline data from eight users. The relative performance of the classifiers is evaluated, along with the practical concerns regarding the implementation of the respective methods. The results indicate that while all methods attained acceptable performance levels, SWLDA and FLD provide the best overall performance and implementation characteristics for practical classification of P300 Speller data