A Dynamic Multi-Scale Network for EEG Signal Classification - Zhang, Guokai; Luo, Jihao; Han, Letong; Lu, Zhuyin; Hua, Rong; Chen, Jianqing; Che, Wenliang (Frontiers in Neuroscience, 2021)
Accurate and automatic classification of the speech imagery electroencephalography (EEG) signals from Brain-Computer Interface (BCI) system is highly demanded in the clinical diagnosis. And the key factor of designing an automatic classification system is to extracting 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 to achieve 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), and 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 the 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.