A novel residual shrinkage block-based convolutional neural network for improving the recognition of motor imagery EEG signals
International Journal of Intelligent Computing and Cybernetics
ISSN: 1756-378X
Article publication date: 23 December 2022
Issue publication date: 12 July 2023
Abstract
Purpose
Recently, the convolutional neural network (ConvNet) has a wide application in the classification of motor imagery EEG signals. However, the low signal-to-noise electroencephalogram (EEG) signals are collected under the interference of noises. However, the conventional ConvNet model cannot directly solve this problem. This study aims to discuss the aforementioned issues.
Design/methodology/approach
To solve this problem, this paper adopted a novel residual shrinkage block (RSB) to construct the ConvNet model (RSBConvNet). During the feature extraction from EEG signals, the proposed RSBConvNet prevented the noise component in EEG signals, and improved the classification accuracy of motor imagery. In the construction of RSBConvNet, the author applied the soft thresholding strategy to prevent the non-related motor imagery features in EEG signals. The soft thresholding was inserted into the residual block (RB), and the suitable threshold for the current EEG signals distribution can be learned by minimizing the loss function. Therefore, during the feature extraction of motor imagery, the proposed RSBConvNet de-noised the EEG signals and improved the discriminative of classification features.
Findings
Comparative experiments and ablation studies were done on two public benchmark datasets. Compared with conventional ConvNet models, the proposed RSBConvNet model has obvious improvements in motor imagery classification accuracy and Kappa coefficient. Ablation studies have also shown the de-noised abilities of the RSBConvNet model. Moreover, different parameters and computational methods of the RSBConvNet model have been tested on the classification of motor imagery.
Originality/value
Based on the experimental results, the RSBConvNet constructed in this paper has an excellent recognition accuracy of MI-BCI, which can be used for further applications for the online MI-BCI.
Keywords
Acknowledgements
This work was supported by the Education and Scientific Research Project for Young and Middle-aged Teachers in Fujian Province (No. JAT200581). The author wants to thank the members of College of Mathematics and Information Engineering in Longyan University for their proofreading comments. The author is very grateful to the anonymous reviewers for their constructive comments which have helped significantly in revising this work.
Citation
Huang, J. (2023), "A novel residual shrinkage block-based convolutional neural network for improving the recognition of motor imagery EEG signals", International Journal of Intelligent Computing and Cybernetics, Vol. 16 No. 3, pp. 420-442. https://doi.org/10.1108/IJICC-05-2022-0130
Publisher
:Emerald Publishing Limited
Copyright © 2022, Emerald Publishing Limited