Lili Shen1, Yu Xia2, Yueping Li3, Mingyang Sun2. 1. Tianjin University, School of Electrical and Information Engineering, Weijin Road, Tianjin 300072, China. Electronic address: sll@tju.edu.cn. 2. Tianjin University, School of Electrical and Information Engineering, Weijin Road, Tianjin 300072, China. 3. Tianjin Eye Hospital, Clinical College of Ophthalmology of Tianjin Medical University, Tianjin Key Laboratory of Ophthalmology and Vision Science, Tianjin 300020, China. Electronic address: leeyueping@aliyun.com.
Abstract
BACKGROUND: Recently, convolutional neural networks (CNN) are widely applied in motor imagery electroencephalography (MI-EEG) signal classification tasks. However, a simple CNN framework is challenging to satisfy the complex MI-EEG signal decoding. NEW METHOD: In this study, we propose a multiscale Siamese convolutional neural network with cross-channel fusion (MSCCF-Net) for MI-EEG classification tasks. The proposed network consists of three parts: Siamese cross-channel fusion streams, similarity module and classification module. Each Siamese cross-channel fusion stream contains multiple branches, and each branch is supplemented by cross-channel fusion modules to improve multiscale temporal feature representation capability. The similarity module is adopted to measure the feature similarity between multiple branches. At the same time, the classification module provides a strong constraint to classify the features from all Siamese cross-channel fusion streams. The combination of the similarity module and classification module constitutes a new joint training strategy to further optimize the network performance. RESULTS: The experiment is conducted on the public BCI Competition IV 2a and 2b datasets, and the results show that the proposed network achieves an average accuracy of 87.36% and 87.33%, respectively. COMPARISON WITH EXISTING METHODS AND CONCLUSIONS: The proposed network adopts cross-channel fusion to learn multiscale temporal characteristics and joint training strategy to optimize the training process. Therefore, the performance outperforms other state-of-the-art MI-EEG signal classification methods.
BACKGROUND: Recently, convolutional neural networks (CNN) are widely applied in motor imagery electroencephalography (MI-EEG) signal classification tasks. However, a simple CNN framework is challenging to satisfy the complex MI-EEG signal decoding. NEW METHOD: In this study, we propose a multiscale Siamese convolutional neural network with cross-channel fusion (MSCCF-Net) for MI-EEG classification tasks. The proposed network consists of three parts: Siamese cross-channel fusion streams, similarity module and classification module. Each Siamese cross-channel fusion stream contains multiple branches, and each branch is supplemented by cross-channel fusion modules to improve multiscale temporal feature representation capability. The similarity module is adopted to measure the feature similarity between multiple branches. At the same time, the classification module provides a strong constraint to classify the features from all Siamese cross-channel fusion streams. The combination of the similarity module and classification module constitutes a new joint training strategy to further optimize the network performance. RESULTS: The experiment is conducted on the public BCI Competition IV 2a and 2b datasets, and the results show that the proposed network achieves an average accuracy of 87.36% and 87.33%, respectively. COMPARISON WITH EXISTING METHODS AND CONCLUSIONS: The proposed network adopts cross-channel fusion to learn multiscale temporal characteristics and joint training strategy to optimize the training process. Therefore, the performance outperforms other state-of-the-art MI-EEG signal classification methods.