EEG Motor Imagery Classification With Sparse Spectrotemporal Decomposition and Deep Learning

摘要

Classification of electroencephalogram-based motor imagery (MI-EEG) tasks raises a big challenge in the design and development of brain-computer interfaces (BCIs). In view of the characteristics of nonstationarity, time-variability, and individual diversity of EEG signals, a deep learning framework termed SSD-SE-convolutional neural network (CNN) is proposed for MI-EEG classification. The framework consists of three parts: 1) the sparse spectrotemporal decomposition (SSD) algorithm is proposed for feature extraction, overcoming the drawbacks of conventional time-frequency analysis methods and enhancing the robustness to noise; 2) a CNN is constructed to fully exploit the time-frequency features, thus outperforming traditional classification methods both in terms of accuracy and kappa value; and 3) the squeeze-and-excitation (SE) blocks are adopted to adaptively recalibrate channelwise feature responses, which further improves the overall performance and offers a compelling classification solution for MI-EEG applications. Experimental results on two datasets reveal that the proposed framework outperforms state-of-the-art methods in terms of both classification quality and robustness. The advantages of SSD-SE-CNN include high accuracy, high efficiency, and robustness to cross-trial and cross-session variations, making it an ideal candidate for long-term MI-EEG applications. Note to Practitioners-Motor imagery-based brain-computer interfaces (MI-BCIs) are widely used to allow a user to control a device using only his or her neural activity. This article proposed a new framework to classify two-class MI tasks based on electroencephalography (EEG) signals. In this framework, a new sparse spectrotemporal decomposition method is used to extract time-frequency features from EEG signals. A convolutional neural network with squeeze-and-excitation blocks is then constructed to classify the MI tasks. We show the superiority of our method on two datasets and prove its feasibility for long-term MI-BCI applications.