Recognize enhanced temporal-spatial-spectral features with a parallel multi-branch CNN and GRU

AbstractDeep learning has been applied to the recognition of motor imagery electroencephalograms (MI-EEG) in brain-computer interface, and the performance results depend on data representation as well as neural network structure. Especially, MI-EEG is so complex with the characteristics of non-stationarity, specific rhythms, and uneven distribution; however, its multidimensional feature information is difficult to be fused and enhanced simultaneously in the existing recognition methods. In this paper, a novel channel importance (NCI) based on time –frequency analysis is proposed to develop an image sequence generation method (NCI-ISG) for enhancing the integrity of data representation and highlighting the contribution inequalities of different channels as well. Each electrode of MI-EEG is converted to a time–frequency spectrum by utilizin g short-time Fourier transform; the corresponding part to 8–30 Hz is combined with random forest algorithm for computing NCI; and it is further divided into three sub-images covered byα (8 –13 Hz),β1 (13 –21 Hz), andβ2 (21 –30 Hz) bands; their spectral powers are further weighted by NCI and interpolated to 2-dimensional electrode coordinates, producing three main sub-band image sequences. Then, a parallel multi-branch convolutional neural network and gate recurrent unit (PMBCG) is designed to successively extract a nd identify the spatial-spectral and temporal features from the image sequences. Two public four-class MI-EEG...
Source: Medical and Biological Engineering and Computing - Category: Biomedical Engineering Source Type: research