Analyse discriminante matricielle descriptive. Application a l'\'etude de signaux EEG

We focus on the descriptive approach to linear discriminant analysis for matrix-variate data in the binary case. Under a separability assumption on row and column variability, the most discriminant linear combinations of rows and columns are determined by the singular value decomposition of the difference of the class-averages with the Mahalanobis metric in the row and column spaces. This approach provides data representations of data in two-dimensional or three-dimensional plots and singles out discriminant components. An application to electroencephalographic multi-sensor signals illustrates the relevance of the method.Comment: in French, Journ{\'e}es de statistique de la SFDS, Jun 2015, Lille, Franc

Analyse discriminante matricielle descriptive. Application a l'étude de signaux EEG

National audienceWe focus on the descriptive approach to linear discriminant analysis for matrix-variate data in the binary case. Under a separability assumption on row and column variability, the most discriminant linear combinations of rows and columns are determined by the singular value decomposition of the difference of the class-averages with the Mahalanobis metric in the row and column spaces. This approach provides data representations of data in two-dimensional or three-dimensional plots and singles out discriminant components. An application to electroencephalographic multi-sensor signals illustrates the relevance of the method.Nous nous intéressons à l'approche descriptive de l'analyse discriminante linéaire de données matricielles dans le cas binaire. Sous l'hypothèse de séparabilité de la variabilité des lignes de celle des colonnes, les combinaisons linéaires des lignes et des colonnes les plus discriminantes sont déterminées par la décomposition en valeurs singulières de la différence des moyennes des deux classes en munissant les espaces des lignes et des colonnes de la métrique de Mahalanobis. Cette approche permet d'obtenir des représentations des données dans des plans factoriels et de dégager des composantes discriminantes. Une application a des signaux d'électroencéphalographie multi-capteurs illustre la pertinence de la méthode

Brain-machine coadaptation for optimal interaction : application to P300-Speller

Les interfaces cerveau-machine (ICM) permettent de contrôler une machine directement à partir de l'activité cérébrale. Le P300-Speller, en particulier, pourrait offrir à des patients complètement paralysés, la possibilité de communiquer sans l'aide de la parole ou du geste. Nous avons cherché à améliorer cette communication en étudiant la coadaptation entre cerveau et machine. Nous avons d'abord montré que l'adaptation d'un utilisateur peut être partiellement perçue, en temps-réel, à travers les modulations de sa réponse électrophysiologique aux feedbacks de la machine. Nous avons ensuite proposé, testé et évalué les effets sur l'utilisateur de plusieurs approches permettant d'améliorer l'interaction, notamment : la correction automatique des erreurs, grâce à la reconnaissance en temps-réel des réponses aux feedbacks ; une stimulation dynamique permettant de diminuer le risque d'erreur tout en réduisant l'inconfort lié aux stimulations ; un processus automatique de décision adaptative, en fonction de l'état de vigilance du sujet. Nos résultats montrent la présence de réponses aux feedbacks spécifiques des erreurs et modulées par l'attention ainsi que par la surprise du sujet face au résultat de l'interaction. Par ailleurs, si l'efficacité de la correction automatique est variable d'un sujet à l'autre, le nouveau mode de stimulation comme la décision adaptative apparaissent comme très avantageux et leur utilisation a un effet positif sur la motivation. Dans la perspective d'études cliniques pour évaluer l'utilité des ICM pour la communication, ces travaux soulignent et quantifient l'intérêt de développer des interfaces capables de s'adapter à chaque utilisateurBrain-computer interfaces (BCI) aim at enabling the brain to directly control an artificial device. In particular, the P300-Speller could offer patients who cannot speak and neither move, to communicate again. This work consisted in improving this communication by implementing and studying a coadaptation between the brain and the machine. First, on the user side, we showed that adaptation is reflected in real-time by modulations of the electrophysiological responses to the feedbacks from the machine. Then, on the computer side, we proposed, tested and evaluated the effect on the user, of several approaches that endow the machine with adaptive behavior, namely: Automatic correction of errors, based on real-time recognition of feedback responses; Dynamic stimulation to increase spelling accuracy as well as to reduce the discomfort associated with the traditional row/column stimulation paradigm; Adaptive decision making for optimal stopping, depending on the attentional state of the user. Our results show the presence of feedback responses which are error specific and modulated by attention as well as user's surprise with respect to the outcome of the interaction. Besides, while the interest of automatic correction is highly subject-dependant, the new stimulation mode and the adaptive decision method proved clearly beneficial and their use had a significant positive impact on subject's motivation. In the perspective of clinical studies to assess the usefulness of ICM for communication, this work highlights and quantifies the importance of developing adaptive interfaces that are tailored to each every individua

Coadaptation cerveau machine pour une interaction optimale : application au P300-Speller

Brain-computer interfaces (BCI) aim at enabling the brain to directly control an artificial device. In particular, the P300-Speller could offer patients who cannot speak and neither move, to communicate again. This work consisted in improving this communication by implementing and studying a coadaptation between the brain and the machine. First, on the user side, we showed that adaptation is reflected in real-time by modulations of the electrophysiological responses to the feedbacks from the machine. Then, on the computer side, we proposed, tested and evaluated the effect on the user, of several approaches that endow the machine with adaptive behavior, namely: - - Automatic correction of errors, based on real-time recognition of feedback responses; - - Dynamic stimulation to increase spelling accuracy as well as to reduce the discomfort associated with the traditional row/column stimulation paradigm; - - Adaptive decision making for optimal stopping, depending on the attentional state of the user. Our results show the presence of feedback responses which are error specific and modulated by attention as well as user's surprise with respect to the outcome of the interaction. Besides, while the interest of automatic correction is highly subject-dependant, the new stimulation mode and the adaptive decision method proved clearly beneficial and their use had a significant positive impact on subject's motivation. In the perspective of clinical studies to assess the usefulness of ICM for communication, this work highlights and quantifies the importance of developing adaptive interfaces that are tailored to each every individual.Les interfaces cerveau-machine (ICM) permettent de contrôler une machine directement à partir de l'activité cérébrale. Le P300-Speller, en particulier, pourrait offrir à des patients complètement paralysés, la possibilité de communiquer sans l'aide de la parole ou du geste. Nous avons cherché à améliorer cette communication en étudiant la coadaptation entre cerveau et machine. Nous avons d'abord montré que l'adaptation d'un utilisateur peut être partiellement perçue, en temps-réel, à travers les modulations de sa réponse électrophysiologique aux feedbacks de la machine. Nous avons ensuite proposé, testé et évalué les effets sur l'utilisateur de plusieurs approches permettant d'améliorer l'interaction, notamment : - la correction automatique des erreurs, grâce à la reconnaissance en temps-réel des réponses aux feedbacks ; - une stimulation dynamique permettant de diminuer le risque d'erreur tout en réduisant l'inconfort lié aux stimulations ; - un processus automatique de décision adaptative, en fonction de l'état de vigilance du sujet. Nos résultats montrent la présence de réponses aux feedbacks spécifiques des erreurs et modulées par l'attention ainsi que par la surprise du sujet face au résultat de l'interaction. Par ailleurs, si l'efficacité de la correction automatique est variable d'un sujet à l'autre, le nouveau mode de stimulation comme la décision adaptative apparaissent comme très avantageux et leur utilisation a un effet positif sur la motivation. Dans la perspective d'études cliniques pour évaluer l'utilité des ICM pour la communication, ces travaux soulignent et quantifient l'intérêt de développer des interfaces capables de s'adapter à chaque utilisateur

Objective and Subjective Evaluation of Online Error Correction during P300-Based Spelling

Error potentials (ErrP) are alterations of EEG traces following the subject’s perception of erroneous feedbacks. They provide a way to recognize misinterpreted commands in brain-computer interfaces (BCI). However, this has been evaluated online in only a couple of studies and mostly with very few subjects. In this study, we implemented a P300-based BCI, including not only online error detection but also, for the first time, automatic correction. We evaluated it in 16 healthy volunteers. Whenever an error was detected, a new decision was made based on the second best guess of a probabilistic classifier. At the group level, correction did neither improve nor deteriorate spelling accuracy. However, automatic correction yielded a higher bit rate than a respelling strategy. Furthermore, the fine examination of interindividual differences in the efficiency of error correction and spelling clearly distinguished between two groups who differed according to individual specificity in ErrP detection. The high specificity group had larger evoked responses and made fewer errors which were corrected more efficiently, yielding a 4% improvement in spelling accuracy and a higher bit rate. Altogether, our results suggest that the more the subject is engaged into the task, the more useful and well accepted the automatic error correction

Adaptive training session for a P300 speller brain-computer interface

International audienceWith a brain-computer interface (BCI), it is nowadays possible to achieve a direct pathway between the brain and computers thanks to the analysis of some particular brain activities. The detection of even-related potentials, like the P300 in the oddball paradigm exploited in P300-speller, provides a way to create BCIs by assigning several detected ERP to a command. Due to the noise present in the electroencephalographic signal, the detection of an ERP and its different components requires efficient signal processing and machine learning techniques. As a consequence, a calibration session is needed for training the models, which can be a drawback if its duration is too long. Although the model depends on the subject, the goal is to provide a reliable model for the P300 detection over time. In this study, we propose a new method to evaluate the optimal number of symbols (i.e. the number of ERP that shall be detected given a determined target probability) that should be spelt during the calibration process. The goal is to provide a usable system with a minimum calibration duration and such that it can automatically switch between the training and online sessions. The method allows to adaptively adjust the number of training symbols to each subject. The evaluation has been tested on data recorded on 20 healthy subjects. This procedure lets drastically reduced the calibration session: height symbols during the training session reach an initialized system with an average accuracy of 80% after five epochs

Reducing Calibration Time for the P300 Brain-Computer Interface Speller

5 pagesNational audienceWith a Brain-Computer Interface (BCI), it is nowadays possible to achieve a direct pathway between the brain and computers thanks to the analysis of some particular brain activities. The detection of even-related potentials, like the P300, provides a way to create BCIs. The generation of the P300 wave is achieved with the oddball paradigm, which allows detecting targets selected by the user on a screen. The P300-Speller is based on this principle. The detection of the P300 requires efficient signal processing and machine learning techniques. Thus, a calibration step is needed for training the models. However, the duration of this calibration can be a drawback. We propose to evaluate the optimal number of characters that should be spelt in order to provide a working system with a minimum calibration duration. The evaluation has been tested on data recorded on 20 healthy subjects. It is possible to spell only seven symbols during the calibration to reach an initialized system with an average accuracy of at least 80%

Wood Debris Risk Analysis and Protection Scenarios of Lourdes City Using Iberwood Model

International audienceLarge wood (LW) accumulations can cause several damages, especially if the recruited wood is transported during floods down to urban areas, like Lourdes (France). One of the most serious problems concerning bridges and weirs all around the world, is the formation of LW accumulations, that might be responsible for the structure’s failure. However, the transport and deposition of LW during floods are very complex processes, which make predictions of these accumulations’ locations and potential impacts very challenging. Thus, reducing the LW-related risks is difficult. Thereby, we are conducting a study to assess the LW-related hazards by applying a numerical model of the Gave-de-Pau River. The software called Iber-Wood is a hydrodynamic model based on the 2D Saint Venant equations coupled to a wood dynamics module that simulates wood transport explicitly. It allows analyzing LW accumulations’ formation, depending on several input parameters. The aim of this study was to simulate wood transport during floods, in order to identify the potential risks created by the formation of LW accumulations in Lourdes’ city center. The paper first proposes a framework to calibrate the parameters of LW-supply (flux and size of logs) based on the typical dataset available in France. It then presents a preliminary study of the 2D hydrodynamic model showing its potential to identify preferential storage areas and bridges prone to the formation of LW accumulations. The calibration phase of the model is not finished but we can yet highlight some recommendations on LW-transport modelling from this early stage results

Wood Debris Risk Analysis and Protection Scenarios of Lourdes City Using Iberwood Model

International audienceLarge wood (LW) accumulations can cause several damages, especially if the recruited wood is transported during floods down to urban areas, like Lourdes (France). One of the most serious problems concerning bridges and weirs all around the world, is the formation of LW accumulations, that might be responsible for the structure’s failure. However, the transport and deposition of LW during floods are very complex processes, which make predictions of these accumulations’ locations and potential impacts very challenging. Thus, reducing the LW-related risks is difficult. Thereby, we are conducting a study to assess the LW-related hazards by applying a numerical model of the Gave-de-Pau River. The software called Iber-Wood is a hydrodynamic model based on the 2D Saint Venant equations coupled to a wood dynamics module that simulates wood transport explicitly. It allows analyzing LW accumulations’ formation, depending on several input parameters. The aim of this study was to simulate wood transport during floods, in order to identify the potential risks created by the formation of LW accumulations in Lourdes’ city center. The paper first proposes a framework to calibrate the parameters of LW-supply (flux and size of logs) based on the typical dataset available in France. It then presents a preliminary study of the 2D hydrodynamic model showing its potential to identify preferential storage areas and bridges prone to the formation of LW accumulations. The calibration phase of the model is not finished but we can yet highlight some recommendations on LW-transport modelling from this early stage results

Reducing Calibration Time for the P300 Brain-Computer Interface Speller

5 pagesNational audienceWith a Brain-Computer Interface (BCI), it is nowadays possible to achieve a direct pathway between the brain and computers thanks to the analysis of some particular brain activities. The detection of even-related potentials, like the P300, provides a way to create BCIs. The generation of the P300 wave is achieved with the oddball paradigm, which allows detecting targets selected by the user on a screen. The P300-Speller is based on this principle. The detection of the P300 requires efficient signal processing and machine learning techniques. Thus, a calibration step is needed for training the models. However, the duration of this calibration can be a drawback. We propose to evaluate the optimal number of characters that should be spelt in order to provide a working system with a minimum calibration duration. The evaluation has been tested on data recorded on 20 healthy subjects. It is possible to spell only seven symbols during the calibration to reach an initialized system with an average accuracy of at least 80%