Home Journals Articles Updates About
CN
image
收藏切换
A Recognition Method for Steady-State Visual Evoked Potential EEG Signals Based on CNN-CBAM-LSTM
收藏切换
PDF
Xuan-lin GONG1, Qing TAO2, *, Na SU3, Jin-xu MA1
Science Technology and Engineering | 2025, 25(10) : 4175 - 4182
Less
收藏切换
Science Technology and Engineering | 2025, 25(10): 4175-4182
• Papers·Electronic and Communicational Technology •
A Recognition Method for Steady-State Visual Evoked Potential EEG Signals Based on CNN-CBAM-LSTM
Full
Xuan-lin GONG1, Qing TAO2, *, Na SU3, Jin-xu MA1
Affiliations
  • 1 School of Intelligent Manufacturing Modern Industry (School of Mechanical Engineering), Xinjiang University, Urumqi 830017, China
  • 2 College of Engineering, Xinjiang University, Urumqi 830017, China
  • 3 The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
Published: 2025-04-08 doi: 10.12404/j.issn.1671-1815.2403775
Outline
收藏切换

When traditional methods were used to evoked the potentials SSVEP (steady-state visual evoked potentials) EEG(electroencephalogram) signals, the accuracy and sufficiency of feature extraction were insufficient, which affected the recognition accuracy of signals. A novel approach was proposed which based on a CNN (convolutional neural network) integrated with a CBAM (convolutional block attention module) and a LSTM (long short-term memory network). By incorporating attention mechanisms, both channel and spatial features were effectively extracted within the CNN framework. Additionally, LSTM was introduced to enhance the extraction of temporal features, enabling accurate recognition of SSVEP signals. The experimental results show that the proposed method can effectively extract hierarchical features and achieves a high recognition accuracy.Compared to canonical correlation analysis (CCA), CNN, CBAM-LSTM, and CNN-CBAM, the proposed model improves the recognition accuracy by 5.3%, 2.95%, 2.27%, and 1.71% respectively. It can be seen that the model has a good performance in the classification and recognition of SSVEP signals.

SSVEP(steady state visual evoked potential)  /  CNN (convolutional neural networks)  /  convolutional attention mechanism module  /  LSTM(long short-term memory network)  /  target recognition
Xuan-lin GONG, Qing TAO, Na SU, Jin-xu MA. A Recognition Method for Steady-State Visual Evoked Potential EEG Signals Based on CNN-CBAM-LSTM[J]. Science Technology and Engineering, 2025 , 25 (10) : 4175 -4182 . DOI: 10.12404/j.issn.1671-1815.2403775
Appendix
Less
Year 2025 volume 25 Issue 10
PDF
434
224
Cite this Article
BibTeX
Article Info
doi: 10.12404/j.issn.1671-1815.2403775
  • Receive Date：2024-05-21
  • Online Date：2025-07-09
  • Published：2025-04-08
Article Data
Affiliations
History
  • Received：2024-05-21
  • Revised：2025-01-13
Funding
Affiliations
    1 School of Intelligent Manufacturing Modern Industry (School of Mechanical Engineering), Xinjiang University, Urumqi 830017, China
    2 College of Engineering, Xinjiang University, Urumqi 830017, China
    3 The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China
References
Share
https://castjournals.cast.org.cn/joweb/kxjsygc/EN/10.12404/j.issn.1671-1815.2403775
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House