EEG-based Motor Imagery Classification through Transfer Learning of the CNN

Abstract

Brain computer interface (BCI) is a system which is able to translate EEG signals into comprehensive commands for the computers. EEG-based motor imagery (MI) signals are one of the most widely used signals in this topic. In this paper, an efficient algorithm to classify 2-class MI signals based on the convolutional neural network (CNN) through the transfer learning is introduced. To this end, different 3D representations of EEG signals are injected into the CNN. These proposed 3D representations are prepared by combination of some frequency and time-frequency algorithms such as Fourier Transform, CSP, DCT and EMD. Then, CNN will be trained to classify MI-EEG signals. The average accuracy of classification for 5 subjects achieved 98.5% on the BCI competition iii database IVa.