跨主题的心理工作负载识别使用双分类器域对抗性学习

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Updated: Jun 3, 2025

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
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P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

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Yueying Zhou1,2,3, Pengpai Wang4, Peiliang Gong2,3

  • 1School of Mathematics Science, Liaocheng University, Liaocheng, 252000 China.

Cognitive neurodynamics
|January 13, 2025
PubMed
概括

本研究引入了基于脑电图 (EEG) 的心理工作负载识别 (MWR) 系统的新领域适应方法. 该方法通过将数据在全球和按工作负载类别对齐,提高了跨主题的准确性.

关键词:
双分类器域名调整适应大脑与计算机的接口.这是一个跨学科的跨学科.电脑电图 (EEG) 是一个电脑电图.心理状态的识别和认知

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