Cognitive Workload Recognition Based on Hybrid Brain-Computer Interface Channel Selection and Hierarchical Feature Fusion

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Artificial Intelligence | 更新时间：2024-12-30

- Cognitive Workload Recognition Based on Hybrid Brain-Computer Interface Channel Selection and Hierarchical Feature Fusion
- “In the field of brain computer interfaces, researchers have proposed an improved dynamic graph attention channel selection method that effectively identifies cognitive workloads and provides a solution for model robustness in complex data environments.”
- Software Guide Vol. 23, Issue 12, Pages: 1-9(2024)
- 作者机构：
  
  上海理工大学光电信息与计算机工程学院，上海 200093
- 作者简介：
- 基金信息：
- DOI：10.11907/rjdk.241740
  CLC： TP181
- Published：16 December 2024，
  
  Received：20 September 2024，
- 稿件说明：
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张恒千,詹志远,尹钟.基于混合脑机接口通道选择与分层特征融合的认知工作负荷识别[J].软件导刊,2024,23(12):1-9.

ZHANG Hengqian,ZHAN Zhiyuan,YIN Zhong.Cognitive Workload Recognition Based on Hybrid Brain-Computer Interface Channel Selection and Hierarchical Feature Fusion[J].Software Guide,2024,23(12):1-9.
张恒千,詹志远,尹钟.基于混合脑机接口通道选择与分层特征融合的认知工作负荷识别[J].软件导刊,2024,23(12):1-9. DOI： 10.11907/rjdk.241740.

ZHANG Hengqian,ZHAN Zhiyuan,YIN Zhong.Cognitive Workload Recognition Based on Hybrid Brain-Computer Interface Channel Selection and Hierarchical Feature Fusion[J].Software Guide,2024,23(12):1-9. DOI： 10.11907/rjdk.241740.

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