Méthodes d'analyse et de débruitage multicanaux à partir d'ondelettes pour améliorer la détection de potentiels évoqués sans moyennage : application aux interfaces cerveau-ordinateur. (Wavelet-based semblance methods to enhance single-trial ERP detection)

Brain-Computer Interfaces (BCI) are control and communication systems which were initially developed to assist people with motor disabilities. The main idea behind BCI is to translate the brain activity into commands for a computer application or other devices, such as a spelling system or a robotic limb. Nowadays, BCI systems are becoming very popular because of its potential to be applied in other domains, where the target audience correspond to healthy people, as for example in computer games. A popular non-invasive technique to record brain signals is the electroencephalography (EEG), because it is easy to use, safe for users and it is not bulky or expensive, compared to other recordings techniques. The EEG records the voltage uctuations of the brain (brain waves) triggered by di erent stimuli or mental states. Event-Related potentials (ERPs) are one of the most used signals for BCI that can be recorded using EEG. Despite the BCI popularity, it is generally di cult to work with brain signals, because the hardware used to record them also records noise and artifacts (i.e. body signals from the subject or environmental signals), and because the brain components amplitudes are very small compared to the whole ongoing EEG activity. Moreover, ERP responses have di erent latencies and amplitudes for each subject (or even for the same subject), causing the use of large time-windows including irrelevant information to perform the analysis. As a consequence it is necessary to use intelligent techniques to recognize ERPs. For these reasons, a commonly used technique was conceived to improve the signal-to-noise ratio of brain signals : averaging several single-trial brain signals obtained under the same conditions. This technique reduces the noise and artifacts, naturally improving the system's performance, at the cost of slowing the whole process due to the extra repetitions required to function. Another problem related with the average of repetitions occurs when there are latency jitter or phase artifacts between single trials, which can cause the elimination of transient characteristics. In this thesis, new techniques based on wavelet theory are developed to improve the performance of ERP-based BCI systems using similarity measures between signals. One technique presented in this thesis denoises the signals in the wavelet domain simultaneously for all channels, i.e. not independently, using the phase information provided by all signals. This method has been compared to classic thresholding techniques based on wavelets on single-trials ERP detection. We have also extended this approach combining the phase and the amplitude information provided by the signals to obtain a correlation between target and non-target signals. This measure allows to localize automatically the ERP in time, for each user, in order to reduce the time-window to analyze, to remove useless features and make the analysis easier. A speci c time-window selection can be done per channel using target and non-target averages, or the same time-window can be selected for all channels using target and non-target grand averages. The combination of our two algorithms for denoising the signals and select the time-window has been compared to xDAWN which is an e cient algorithm to enhance and detect ERPs. The algorithms presented in this thesis are evaluated and compared on single-trial detection using a large dataset for the P300 speller containing 22 rst-time users provided by the neuroimaging laboratory of the Metropolitan autonomous university of Mexico. A second database including patients provided by the multimedia signal processing group of EPFL has been blindly used for validation using the parameters selected for the rst database. The results show that the generated models exhibit signi cant improvements with respect to conventional methods.