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A brain-computer interface (BCI) is an highly interdisciplinary research topic which involved psychology, signal processing, machine learning, medical engineering etc. A steady-state visually evoked potentials (SSVEP) paradigm-based Brain Computer Interface systems are the most promising and reliable communication systems for people with disabilities on clinical purposes. This paper proposes the use of Deep Neural Network models for decoding the data acquired through visual-based Electroencephalography (EEG). in this paper, we concentrate on two points with the Convolutional Neural Network The first is decoding of the EEG data using the raw signal (time domain) and the extracted frequency features of the signal after Fourier analysis. The second point explored in this paper is the evaluation of the selection of electrodes on the performance of the Deep Neural Network models for raw signals (time domain data). We have also compared the performance of Canonical Correlation analysis of the data with the Deep Neural Network models.

Decoding SSVEP on time and frequency domain using Convolutional Neural Network.