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MS-CANet: lightweight multi-scale channel attention network with depthwise residual blocks for EEG-based spatial
Xueguang Xie1,2, Fengyi Hu1, Shenghao Yuan1,2
1School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, 100083, China.
Medical & Biological Engineering & Computing
|October 27, 2025
Summary
We developed a lightweight deep learning model for analyzing electroencephalogram (EEG) signals to assess spatial cognition. This efficient network improves accuracy and is suitable for mobile healthcare devices.
Area of Science:
- Neuroscience
- Artificial Intelligence
- Biomedical Engineering
Background:
- Objective assessment of spatial cognitive ability is vital for early detection of cognitive impairment and for neurorehabilitation.
- Current deep learning methods for electroencephalogram (EEG) analysis are often complex, leading to poor generalization and hindering mobile healthcare deployment.
- There is a need for efficient and accurate EEG analysis models for spatial cognition assessment.
Purpose of the Study:
- To propose a novel, lightweight deep learning network for objective assessment of spatial cognitive ability using EEG signals.
- To overcome the limitations of complex deep learning models, such as poor generalization and high computational cost.
- To enable practical deployment of advanced EEG analysis on mobile healthcare devices.
Main Methods:
- Developed a lightweight multi-scale channel attention network incorporating depthwise residual blocks.
- Utilized multi-scale convolutional layers to capture diverse temporal and spatial patterns in EEG signals.
- Implemented channel attention mechanisms for dynamic prioritization of informative EEG channels and introduced depthwise separable residual blocks to reduce computational complexity.
Main Results:
- The proposed network achieved higher accuracy compared to baseline models on a spatial cognition EEG dataset.
- The model has a significantly reduced parameter count (8.453M), enhancing efficiency and practicality for mobile deployment.
- Demonstrated stable performance despite reduced computational complexity.
Conclusions:
- The novel lightweight network offers an efficient and accurate solution for spatial cognitive ability assessment using EEG.
- The model's efficiency makes it suitable for deployment on mobile healthcare devices, facilitating broader accessibility.
- The network shows potential for early screening and intervention strategies for a range of cognitive disorders.
