Identification of spatial and temporal features of EEG

Specifically, this thesis extends the classic Support Vector Machine (SVM) algorithm to present a new hill- climbing method Relevant Subset Selection Using The

For the P300 data set (BCI competition III), for which a large number of trials are available, the sw-SVM proves to perform equivalently with respect to the ensemble SVM strategy

Through this fi rst simulation study, we illustrated how volume conduction and, to a lesser extend, reference electrode can affect the temporal resolution of EEG: by

The reduced model for a non-axisymmetric ellipsoid and the model of Busse ( 1968 ) for a spheroid agree within 0.3 %, leading to a mean differential rotation amplitude of the order of

In this paper, using a database with 23 healthy subjects we compare the effect on the letter accuracy (single-trial detection) provided by a linear support vector machine of

In this paper, we propose a Spatially Regularized CSP (SRCSP) which incorporates spatial information in a generic way. Contrary to beamforming, SRCSP does not require additional

In this section, we describe the different experiments performed to evalu- ate our method, and present the results obtained in discriminating between retinal images containing

Among the large panel of existing proximal algorithms [25, 26, 27], we consider the primal-dual M+LFBF algorithm recently proposed in [27], which is able to address general