ORIGINAL RESEARCH article
Front. Hum. Neurosci.
Sec. Brain-Computer Interfaces
Advancing Individual Finger Classification Through a Sandwich Enhanced CBAM Network with Ultra-High-Density EEG Data
Xinguo Zhang 1
Yiman Zhang 2
Hong Peng 3
Tao DENG 4,1
1. Key Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education, Chinese National Information Technology Research Institute, Northwest Minzu University, Lanzhou, China
2. School of Artificial Intelligence, Hebei Institute of Communications, Shijiazhuang, China
3. Lanzhou University School of Information Science and Engineering, Lanzhou, China
4. School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, China
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Abstract
Ultra-High-Density Electroencephalography (uHD EEG) has gained increasing attention for its potential in individual finger decoding. However, accurately classifying these movements remains challenging due to the subtle spatial overlaps in cortical activity, which standard architectures often fail to isolate. To address this, we propose the Sandwich enhanced Convolutional Block Attention Module (SCBAM). The unique sandwich structure integrates dual attention mechanisms between convolutional layers, enabling the network to more effectively refine high-dimensional spatial features. The proposed network achieves an average accuracy of 78.63 (1.56)% in binary classification across ten finger pairs in five subjects, with the highest accuracy of 85% obtained at Thumb versus Ring. The proposed network achieves an average accuracy of 61.12 (0.95)% in five-class classification across five subjects, with a highest accuracy of 62.36% on subject S2. The five-class classification is performed using 10 binary classifiers under a one-vs-one strategy. Notably, five-class classification of individual fingers has not been extensively explored in the current literature, particularly with high-density EEG (HDEEG) data. This work addresses this gap, offering a valuable reference for future discussions. We conduct ablation studies to investigate the individual and synergistic effects of the modules in the proposed model. The results highlight the effects of two sequential attention mechanisms in this task. We conduct comparative experiments of our proposed model against six benchmark networks. The results from SCBAM significantly outperform these established models with FBCSP features. The proposed SCBAM significantly improves accuracy in binary finger classification compared to SVM and MLP using the same uHD EEG dataset. In summary, this study presents a high-performance hybrid network for individual finger classification and highlights the potential of uHD EEG for dexterous task decoding in Brain-Computer Interfaces (BCI).
Summary
Keywords
attention mechanism, Brain-Computer Interfaces, Convolutional block attention module, individual finger classification, ultra-high-density EEG
Received
21 November 2025
Accepted
03 February 2026
Copyright
© 2026 Zhang, Zhang, Peng and DENG. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Tao DENG
Disclaimer
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