Enhancing Cross-Subject Motor Imagery Classification in EEG-Based Brain–Computer Interfaces by Using Multi-Branch CNN
A brain–computer interface (BCI) is a computer-based system that allows for communication between the brain and the outer world, enabling users to interact with computers using neural activity. This brain signal is obtained from electroencephalogram (EEG) signals. A significant obstacle to the development of BCIs based on EEG is the classification of subject-independent motor imagery data since EEG data are very individualized. Deep learning techniques such as the convolutional neural network (CNN) have illustrated their influence on feature extraction to increase classification accuracy. In this paper, we present a multi-branch (five branches) 2D convolutional neural network that employs several hyperparameters for every branch. The proposed model achieved promising results for cross-subject classification and outperformed EEGNet, ShallowConvNet, DeepConvNet, MMCNN, and EEGNet_Fusion on three public datasets. Our proposed model, EEGNet Fusion V2, achieves 89.6% and 87.8% accuracy for the actual and imagined motor activity of the eegmmidb dataset and scores of 74.3% and 84.1% for the BCI IV-2a and IV-2b datasets, respectively. However, the proposed model has a bit higher computational cost, i.e., it takes around 3.5 times more computational time per sample than EEGNet_Fusion.
Item Type | Article |
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Uncontrolled Keywords | Convolutional neural network (CNN); Brain–computer interface (BCI); Deep Learning; fusion network; motor imagery (MI); Electroencephalography (EEG); brain–computer interface (BCI); electroencephalography (EEG); deep learning; convolutional neural network (CNN); Brain-Computer Interfaces; Brain; Neural Networks, Computer; Electroencephalography |
Subjects |
Social Sciences(all) > Communication Chemistry(all) > Analytical Chemistry Computer Science(all) > Information Systems Physics and Astronomy(all) > Instrumentation Physics and Astronomy(all) > Atomic and Molecular Physics, and Optics Engineering(all) > Electrical and Electronic Engineering Biochemistry, Genetics and Molecular Biology(all) > Biochemistry |
Date Deposited | 26 Jul 2024 19:24 |
Last Modified | 26 Jul 2024 19:24 |
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- Biocomputation Research Group
- School of Physics, Engineering & Computer Science
- Department of Computer Science
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