Robust CNN architecture for classification of reach and grasp actions from neural correlates: an edge device perspective

Sultan, Hajrah; Ijaz, Haris; Waris, Asim; Mushtaq, Shafaq; Mushtaq, Khurram; Khan, Niaz B.; Khan, Said Ghani; Tlija, Mehdi; Iqbal, Jamshed

Abstract

Brain-computer interfaces (BCIs) systems traditionally use machine learning (ML) algorithms that require extensive signal processing and feature extraction. Deep learning (DL)-based convolutional neural networks (CNNs) recently achieved state-of-the-art electroencephalogram (EEG) signal classification accuracy. CNN models are complex and computationally intensive, making them difficult to port to edge devices for mobile and efficient BCI systems. For addressing the problem, a lightweight CNN architecture for efficient EEG signal classification is proposed. In the proposed model, a combination of a convolution layer for spatial feature extraction from the signal and a separable convolution layer to extract spatial features from each channel. For evaluation, the performance of the proposed model along with the other three models from the literature referred to as EEGNet, DeepConvNet, and EffNet on two different embedded devices, the Nvidia Jetson Xavier NX and Jetson Nano. The results of the Multivariant 2-way ANOVA (MANOVA) show a significant difference between the accuracies of ML and the proposed model. In a comparison of DL models, the proposed models, EEGNet, DeepConvNet, and EffNet, achieved 92.44 ± 4.30, 90.76 ± 4.06, 92.89 ± 4.23, and 81.69 ± 4.22 average accuracy with standard deviation, respectively. In terms of inference time, the proposed model performs better as compared to other models on both the Nvidia Jetson Xavier NX and Jetson Nano, achieving 1.9 sec and 16.1 sec, respectively. In the case of power consumption, the proposed model shows significant values on MANOVA (p < 0.05) on Jetson Nano and Xavier. Results show that the proposed model provides improved classification results with less power consumption and inference time on embedded platforms.

Citation

Sultan, H., Ijaz, H., Waris, A., Mushtaq, S., Mushtaq, K., Khan, N. B., …Iqbal, J. (2024). Robust CNN architecture for classification of reach and grasp actions from neural correlates: an edge device perspective. Measurement Science and Technology, 35(3), Article 035703. https://doi.org/10.1088/1361-6501/ad1157

• Journal Article Type: Article
• Acceptance Date: Nov 30, 2023
• Online Publication Date: Dec 11, 2023
• Publication Date: Mar 1, 2024
• Deposit Date: Jan 5, 2024
• Publicly Available Date: Jan 8, 2024
• Journal: Measurement Science and Technology
• Print ISSN: 0957-0233
• Electronic ISSN: 1361-6501
• Publisher: IOP Publishing
• Peer Reviewed: Peer Reviewed
• Volume: 35
• Issue: 3
• Article Number: 035703
• DOI: https://doi.org/10.1088/1361-6501/ad1157
• Keywords: Brain computer interface; Deep learning; Convolutional neural networks; Embedded edge devices
• Public URL: https://hull-repository.worktribe.com/output/4501047