EEG motor imagery classification using dynamic connectivity patterns and convolutional autoencoder

Abstract

Recently, Brain computer Interface (BCI), plays an important role in recognizing brain activities and rehabilitation. Motor imagery (MI) classification based on Electroencephalography (EEG) signal analysis has received a lot of attention for the purpose of movement intent recognition. In this paper, we propose a novel feature extraction method which leads to promising MI classification performance. We split the individual EEG channels’ time series into temporal blocks and compute connectivity matrices for each block using adaptive sparse representation (ASR). The connectivity matrices indicate the correlation among different channels’ temporal blocks. They construct the dynamic connectivity patterns which are 3D tensors. Then, kernel PCA or non-linear convolutional autoencoders are applied to these tensors to learn discriminatory representations. This way, we incorporate the joint spatial-temporal structure of the EEG signals in the acquired features. We use a Gradient boosting model for classification. It is shown that the proposed feature extraction approach can lead to promising EEG MI classification performance compared to the state-of-the-art methods.