Convolutional neural network and riemannian geometry hybrid approach for motor imagery classification

Abstract

The electroencephalogram (EEG) signal is commonly applied in the brain-computer interface (BCI) system of the motor imagery paradigm because it is noninvasive and has a high time resolution. This paper proposes a motor imagery classification method based on convolutional neural networks and Riemannian geometry to overcome the problem of noise and extreme values impacting motor imagery classification performance. The time-domain properties of EEG signals are extracted using multiscale temporal convolutions, whereas the spatial aspects of EEG signals are extracted using multiple convolutional kernels learned by spatial convolution. The extracted features are mapped to a Riemannian manifold space, and bilinear mapping and logarithmic operations are performed on the features to solve the problem of noise and extreme values. The effectiveness of the proposed method is validated using four types of motor imagery in the BCI competition IV dataset 2a to evaluate the classification ability. The experimental results show that the proposed approach has obvious advantages in the classification performance of motor imagery.