Non-invasive classification of cortical activities for Brain Computer Interface: A variable selection approach

We propose to carry out a classification method for electro-encephalographic signals (EEG), using the activities of cortical sources estimated with an EEG inverse problem. To overcome the difficulties caused by the high number of sources (approximately 10000), we use a multivariate variable selection algorithm: the zero norm Support Vector Machine (L0-SVM). This technique allows to extract a small subset of sources, which are the most useful to allow for the discrimination of the mental states. The whole approach is applied to an asynchronous Brain Computer Interface (BCI) experiment from our lab. It outperforms a method based on the direct measurement of EEG electrodes' activities.