In BCI (brain - computer interface) systems, brain signals must be processed to identify distinct activities that convey different mental states. We propose a new technique for the classification of electroencephalographic (EEG) steady-state visual evoked potential (SSVEP) activity for non-invasive BCI. The proposed method is based on a convolutional neural network that includes a Fourier transform between hidden layers in order to switch from the time domain to the frequency domain analysis in the network. The first step allows the creation of different channels. The second step is dedicated to the transformation of the signal in the frequency domain. The last step is the classification. It uses a hybrid rejection strategy that uses a junk class for the mental transition states and thresholds for the confidence values. The presented results with offline processing are obtained with 6 electrodes on 2 subjects with a time segment of 1s. The system is reliable for both subjects over 95%, with rejection criterion.
['Hubert Cecotti', 'Axel Graeser']
Convolutional Neural Network with embedded Fourier Transform for EEG classification