| Autor: |
Jiaming, Chen, Dan, Wang, Bo, Hu, Weibo, Yi, Meng, Xu, Dingrui, Chen, Qing, Zhao |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). |
| DOI: |
10.1109/embc48229.2022.9871385 |
| Popis: |
Motor Imagery-based Brain Computer Interface (MI-BCI) is a typical active BCI with a main focus on motor intention identification. Hybrid motor imagery (MI) decoding methods that based on multi-modal fusion of Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), especially deep learning-based methods, become popular in recent MI-BCI studies. However, the fusion strategy and network design in deep learning-based methods are complex. To solve this problem, we proposed the multi-channel fusion method (MCF) to simplify current fusion methods, and we designed a multi-channel fusion hybrid network (MCFHNet) based on MCF. MCFHNet combines depthwise convolutional layers, channel attention mechanism, and Bidirectional Long Short Term Memory (Bi-LSTM) layers, which enables strong capability of feature extraction in spatial and temporal domain. The comparison between MCFHNet and representative deep learning-based methods was performed on an open EEG-fNIRS dataset. We found the proposed method can yield superior performance (mean accuracy of 99.641 % in 5-fold cross validation of an intra-subject experiment). This work provides a new option for multi-modal MI decoding, which can be applied in the rehabilitation field based on hybrid BCI systems. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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