We study a brain computer interface (BCI) to control a prosthesis with thought. The aim of the BCI is to decode the movement desired by the subject from electroencephalographic (EEG) signals. The core of the BCI is a classification algorithm characterized by the choice of signals descriptors and decision rules. The purpose of this thesis is to develop an accurate BCI system, able to improve its performance during its use and to adapt to the user evolutions without requiring multiple learning sessions. We combine two ways to achieve this. The first one is to increase the precision of the decision system by looking for relevant descriptors for the classification. The second one is to include a feedback to the user on the system decision : the idea is to estimate the error of the BCI from evoked brain potentials, reflecting the emotional state of the patient correlated to the success or failure of the decision taken by the BCI, and to correct the decision system of the BCI accordingly. The main contributions are : we have proposed a method to optimize the feature space based on wavelets for multi-channel EEG signals ; we quantified theoretically the performances of the complete system improved by the detector ; a simulator of the corrected and looped system has been developed to observe the behavior of the overall system and to compare different strategies to update the learning set ; the complete system has been implemented and works online in real conditions.
