A frequency domain classifier of steady-state visual evoked potentials using deep separable convolutional neural networks

Steady state visual evoked potential (SSVEP)-based brain computer interface (BCI) systems has attracted paramount amount of attention due to their higher signal to noise ratio and high information transfer rate. In this paper a SSVEP-BCI-based on a convolutional neural network (CNN) classifier is presented. The visual stimulation is provided to the participants with with LED matrices blinking at 6, 7, 8 and 9 Hz respectively. A wireless EEG amplifier, the g.Nautilus was used to acquire the electroencephalogram (EEG) signals from eight parietal and occipital electrodes. The features were derived using Fast Fourier Transformation (FFT) of the 8 channels using a 2s moving window in the form of 8 × 8 grey scale images. The proposed CNN architecture has provided superior average accuracy of 94.7% for four subjects, compared to the average accuracy of 87.4% of the state of the art canonical correlation analysis (CCA) performance.