MRMHNet: a new convolutional neural network approach for decoding electroencephalogram motor imagery signals

Menghao Liu; Dongjiong Wu; Xuehua Tang; Zhiyong Zhou; Pengfei Zhao; Xiangmin Li; Xu Zhang

doi:10.1117/12.2687404

28 August 2023 MRMHNet: a new convolutional neural network approach for decoding electroencephalogram motor imagery signals

Menghao Liu, Dongjiong Wu, Xuehua Tang, Zhiyong Zhou, Pengfei Zhao, Xiangmin Li, Xu Zhang

Author Affiliations +

Proceedings Volume 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023); 127241D (2023) https://doi.org/10.1117/12.2687404
Event: Second International Conference on Biomedical and Intelligent Systems (IC-BIS2023), 2023, Xiamen, China

Abstract

The decoding of electroencephalogram (EEG) signals plays an extremely important role in brain-computer interfaces (BCI). However, the processing of physiological signals, particularly the decoding of multi-channel EEG signals, still poses significant challenges. Past deep learning methods often relied on subject-dependent settings, which resulted in new users needing to perform complex calibration procedures before they could use BCI devices. Therefore, we proposed a novel end-to-end deep learning model, MRMHNet, for motor imagery (MI) classification. Firstly, we utilized a feature extraction block based on a Multi-Resolution convolutional neural network (MRCNN) to extract features in both frequency and spatial domains. Secondly, we utilized a block based on the Multi-Head Attention (MHA) to extract global temporal information of the features. Finally, we validated the classification performance of our method using OpenBMI datasets, and the results showed that our method achieved the highest accuracy in both subject-dependent and subject-independent settings. Specifically, in the subject-independent setting, our method achieved the highest accuracy and F1-score, with values of 73.74±13.35% and 73.33±14.87%, respectively. This indicates that our method has good classification performance and high practical value in the field of BCI.

Citation Download Citation

Menghao Liu, Dongjiong Wu, Xuehua Tang, Zhiyong Zhou, Pengfei Zhao, Xiangmin Li, and Xu Zhang "MRMHNet: a new convolutional neural network approach for decoding electroencephalogram motor imagery signals", Proc. SPIE 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023), 127241D (28 August 2023); https://doi.org/10.1117/12.2687404

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KEYWORDS

Electroencephalography

Feature extraction

Brain-machine interfaces

Deep learning

Convolutional neural networks

Performance modeling

Cross validation

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