Abstract:EEG signals classification plays a crucial role in motor imagery brain computer interface systems. Traditional convolutional neural networks tend to ignore temporal information when classifying motor imagery EEG signals, it uses a single-scale convolutional kernel, resulting in poor classification performance. In this paper, we propose a parallel fusion algorithm based on dual attentional multi-scale convolutional neural networks (DAMSCN) and long and short-term memory (LSTM). Firstly, DAMSCN uses convolutiona… Show more
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