A Dynamic Multi-Scale Network for EEG Signal Classification

Accurate and automatic classification of the speech imagery electroencephalography (EEG) signals from a Brain-Computer Interface (BCI) system is highly demanded in clinical diagnosis. The key factor in designing an automatic classification system is to extract essential features from the original input; though many methods have achieved great success in this domain, they may fail to process the multi-scale representations from different receptive fields and thus hinder the model from achieving a higher performance. To address this challenge, in this paper, we propose a novel dynamic multi-scale network to achieve the EEG signal classification. The whole classification network is based on ResNet, and the input signal first encodes the features by the Short-time Fourier Transform (STFT); then, to further improve the multi-scale feature extraction ability, we incorporate a dynamic multi-scale (DMS) layer, which allows the network to learn multi-scale features from different receptive fields at a more granular level. To validate the effectiveness of our designed network, we conduct extensive experiments on public dataset III of BCI competition II, and the experimental results demonstrate that our proposed dynamic multi-scale network could achieve promising classification performance in this task.