EEG classification based on Grassmann manifold and matrix recovery

Abstract

Extracting features from EEG signals through time, frequency and spatial-domain gives rise to the problem of neglecting the property of nonlinear manifold structure of the data, and traditional feature extractions based on Euclidean space are unfavorable for the full utilization of the manifold feature. In this paper, a new EEG classification method is proposed by representing EEG signals on the Grassmann manifold. Firstly, a low rank representation method based on matrix recovery is introduced into the Grassmann manifold to explore the subspace information and re-express the EEG signals. Then a deep neural network is proposed to reduce the dimension of the recovered EEG data, and the low rank representation makes the EEG data separable by using T-SNE visualization; Finally, the dimension reduced features are input to the traditional classifier for classification. The proposed method is performed on four datasets for seizure detection, disease recognition and mental arithmetic recognition respectively. The corresponding accuracy reaches 99.99%, 99.61%, 100%, and 99.72% which shows that the proposed method outperforms other state of art methods on EEG classification.