Section: New Results

Brain Computer Interfaces

Combining ERD and ERS features to create a system-paced BCI

Participants : Maureen Clerc, Joan Fruitet, Théodore Papadopoulo, Eoin Thomas.

An important factor in the usability of a brain computer interface (BCI) is the setup and calibration time required for the interface to function accurately. Recently, brain-switches based on the rebound following motor imagery of a single limb effector have been investigated as basic BCIs due to their good performance with limited electrodes, and brief training session requirements. In this work, a BCI is proposed which expands the methodology of brain-switches to design an interface composed of multiple brain-buttons. The algorithm is designed as a system paced interface which can recognise 2 intentional-control tasks and a no-control state based on the activity during and following motor imagery in only 3 electroencephalogram channels. An online experiment was performed over 6 subjects to validate the algorithm, and the results show that a working BCI can be trained from a single calibration session and that the post motor imagery features are both informative and robust over multiple sessions.

This work has been published in [24] .

An analysis of performance evaluation for motor-imagery based BCI

Participants : Maureen Clerc, Matthew Dyson [Laboratoire de Neurosciences Cognitives, Marseille] , Eoin Thomas.

In recent years, numerous brain–computer interfaces (BCIs) based on motor-imagery have been proposed which incorporate features such as adaptive classification, error detection and correction, fusion with auxiliary signals and shared control capabilities. Due to the added complexity of such algorithms, the evaluation strategy and metrics used for analysis must be carefully chosen to accurately represent the performance of the BCI. In this work, metrics are reviewed and contrasted using both simulated examples and experimental data. Furthermore, a review of the recent literature is presented to determine how BCIs are evaluated, in particular, focusing on the relationship between how the data are used relative to the BCI subcomponent under investigation. From the analysis performed in this study, valuable guidelines are presented regarding the choice of metrics and evaluation strategy dependent upon any chosen BCI paradigm.

This work has been published in [23] .

Bandit algorithms for faster task selection in BCI

Participants : Maureen Clerc, Aurélien Emmanuel, Joan Fruitet [former Athena PhD student] , Alexandra Carpentier [Sequel Project-Team, Inria Lille] , Rémi Munos [Sequel Project-Team, Inria Lille] .

Brain–computer interfaces (BCIs) based on sensorimotor rhythms use a variety of motor tasks, such as imagining moving the right or left hand, the feet or the tongue. Finding the tasks that yield best performance, specifically to each user, is a time-consuming preliminary phase to a BCI experiment. This study presents a new adaptive procedure to automatically select (online) the most promising motor task for an asynchronous brain-controlled button. We have developed for this purpose an adaptive algorithm UCB-classif based on the stochastic bandit theory and design an EEG experiment to test our method. We compare (offline) the adaptive algorithm to a naïve selection strategy which uses uniformly distributed samples from each task. We also run the adaptive algorithm online to fully validate the approach. By not wasting time on inefficient tasks, and focusing on the most promising ones, this algorithm results in a faster task selection and a more efficient use of the BCI training session. More precisely, the offline analysis reveals that the use of this algorithm can reduce the time needed to select the most appropriate task by almost half without loss in precision, or alternatively, allow us to investigate twice the number of tasks within a similar time span. Online tests confirm that the method leads to an optimal task selection. This study is the first one to optimize the task selection phase by an adaptive procedure. By increasing the number of tasks that can be tested in a given time span, the proposed method could contribute to reducing 'BCI illiteracy'.

This work has been published in [17] .

Enhancing visuospatial attention performance with brain-computer interfaces

Participants : Thomas Brochier [Institut des Neurosciences de La Timone, Marseille] , Maureen Clerc, Romain Trachel.

Brain-Computer Interfaces (BCI) can provide innovative solutions beyond the medical domain. In human research, visuospatial attention is often assessed from shifts in head or gaze orientation. However in some critical situations, these behavioral features can be dissociated from covert attention processes and brain features may indicate more reliably the spatial focus of attention. In this context, we investigate whether EEG signals could be used to enhance the behavioral performance of human subjects in a visuospatial attention task. Our results demonstrate that a BCI protocol based on adaptive or warning displays can be developed to shorten the reaction time and improve the accuracy of responses to complex visual targets. We performed offline and online tests demonstrating the validity of this type of approach.

This work was presented at conferences in the HCI community [35] and in the Neural Engineering community [34] .

Verbal communication through brain computer interfaces

Participants : Maureen Clerc, Dieter Devlaminck, Claude Desnuelle [CHU de Nice l'Archet] , Violaine Guy [CHU de Nice l'Archet] , Manu Maby [Centre de Recherche Neurologique de Lyon] , Jérémie Mattout [Centre de Recherche Neurologique de Lyon] , Théodore Papadopoulo.

Brain Computer Interfaces (BCI) provide a way of communicating directly from brain activity, bypassing muscular control. We report some recent advances in a BCI communication system called the P300 speller, which is a virtual brain-operated keyboard. This system relies on electroencephalographic activity time-locked to the flashing of the desired letters. It requires calibration of the system, but very little training from the user. Clinical tests are being conducted on a target population of patients suffering from Amyotrophic Lateral Sclerosis, in order to confirm the usability of the P300 speller for reliable communication.

This work has been published in [26] . It is also the object of an intensive clinical study on 20 patients which we are currently conducting at Nice University Hospital.