Multichannel MI-EEG Feature Decoding Based on Deep Learning

Regarding as the measure of the electrical fields produced by the active brain, ElectroEncephaloGraphy (EEG) is a brain mapping and neuroimaging technique widely used inside and outside of the clinical domain, which is also widely used in Brain-Computer Interfaces (BCI). However, low spatial resolution is regarded as the deficiency of EEG signified from researches, which can fortunately be made up by synthetic analysis of data from different channels. In order to efficiently obtain subspace features with discriminant characteristics from EEG channel information, a Multi-Channel Convolutional Neural Networks (MC-CNN) model is proposed for MI-EEG decoding. Firstly input data is pre-processed form selected multi-channel signals, then the time-spatial features are extracted using a novel 2D Convolutional Neural Networks (CNN). Finally, these features are transformed to discriminant sub-space of information with Auto-Encoder (AE) to guide the identification network. The experimental results show that the proposed multi-channel spatial feature extraction method has certain advantages in recognition performance and efficiency. © 2021, Science Press. All right reserved.