Direct brain-computer communication through scalp recorded EEG signals

Scalp recorded electroencephalogram signals (EEG) reflect the combined synaptic and axonal activity of groups of neurons. In addition to their clinical applications, EEG signals can be used as support for direct brain-computer communication devices (Brain-Computer Interfaces BCIs). Indeed, during the performance of mental activities, EEG patterns that characterize them emerge. If actions executed by the BCI, are associated with classes of patterns resulting from mental activities that do not involve any physical effort, communication by means of thoughts is achieved. The subject operates the BCI by performing mental activities which are recognized by the BCI through comparison with recognition models that are set up during a training phase. In this thesis we consider a 2D object positioning application in a computer-rendered environment (CRE) that is operated with four mental activities (controlling MAs). BCI operation is asynchronous, namely the system is always active and reacts only when it recognizes any of the controlling MIAs. The BCI analyzes segments of EEG (EEG-trials) and executes actions on the CRE in accordance with a set of rules (action rules) adapted to the subject controlling skills. EEG signals have small amplitudes and are therefore sensitive to external electromagnetic perturbations. In addition, subject-generated artifacts (ocular and muscular) can hinder BCI operation and even lead to misleading conclusions regarding the real controlling skills of a subject. Thus, it is especially important to remove external perturbations and detect subject-generated artifacts. External perturbations are removed using established signal processing techniques and artifacts are detected through a singular event detection algorithm based on kernel methods. The detection parameters are calibrated at the beginning of each experimental session through an interactive procedure. Whenever an artifact is detected in an EEG-trial the BCI notifies the subject by executing a special action. Features that are relevant for the recognition of the controlling MIAs are extracted from EEG-trials (free of artifacts) through the statistical analysis of their time, frequency, and phase properties. Since a complete analysis covering all these aspects, would result in a very large number of features, various hypotheses on the nature of EEG are considered in order to reduce the number of needed features. Features are grouped into feature vectors that are used to build the recognition models using machine learning concepts. From a machine learning point of view, low dimensional feature vectors are preferred as they reduce the risk of over-fitting. Recognition models are built based on statistical learning theory and kernel methods. The advantage of these methods resides in their high recognition accuracy and flexibility. A particular requirement of BCI systems is to continuously adapt to possible EEG changes resulting from external factors or subject adaptation to th