Spatial component-wise convolutional network (SCCNet) for motor-imagery EEG classification
We study brain-computer interfaces (BCI) based on the decoding of motor imagery (MI) from
electroencephalography (EEG) neuromonitoring. The robustness of MI-BCI is a major
concern in practical applications, and hence various efforts in the literature have been made
to enhance the MI classification accuracy from EEG signals. Recently, classifiers based on
convolutional neural networks (CNN) have achieved state-of-the-art performance. In further
exploration of applying CNNs to EEG data, we propose a spatial component-wise …
electroencephalography (EEG) neuromonitoring. The robustness of MI-BCI is a major
concern in practical applications, and hence various efforts in the literature have been made
to enhance the MI classification accuracy from EEG signals. Recently, classifiers based on
convolutional neural networks (CNN) have achieved state-of-the-art performance. In further
exploration of applying CNNs to EEG data, we propose a spatial component-wise …
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