Deep learning approach on surface EEG based Brain Computer Interface

Abstract

In this work we analysed the application of con-volutional neural networks in motor imagery classification for the Brain Computer Interface (BCI) purposes. To increase the accuracy of classification we proposed the solution that combines the Common Spatial Pattern (CSP) with convolutional network (ConvNet). The electroencephalography (EEG) is one of the modalities we try to use for controlling the prosthetic arm. Therefor in this paper we exploited the subject dependent approach and show results for models trained individually for a particular subject. Although the ConvNets are design to work directly with EEG data, presented approach of joining CSP with ConvNet shows increase in accuracy of movement classification. In average, our approach resulted in ∼80% accuracy.