对异步 (无) BCI系统的识别方法的比较:对40类SSVEP数据集的调查
Heegyu Kim1, Kyungho Won2, Minkyu Ahn3
1School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Bukgu, Gwangju, 61005 Korea.
Biomedical engineering letters
|April 22, 2024
概括
这项研究比较了使用稳定状态视觉唤起潜力 (SSVEP) 的三种异步脑计算机接口 (BCI) 的方法. 使用正规相关性分析 (CCA) 的两阶段方法在实际的BCI应用中表现最好.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 人与计算机的交互
背景情况:
- 基于稳态视觉唤起潜力 (SSVEP) 的脑计算机接口 (BCI) 提供了高速通信潜力.
- 不同步BCI与同步BCI不同,可以检测用户的实时控制意图,但识别方法需要进一步调查.
研究的目的:
- 为了比较三种广泛使用的识别方法 (功率光谱密度分析,正规相关性分析 (CCA) 和支持矢量机 (SVM)) 在异步SSVEPBCI系统中的性能.
- 评估和比较两个异步系统设计的性能:一阶段和两阶段方法.
主要方法:
- 使用了40个受试者的40个类SSVEP数据集.
- 研究了三种识别方法 (功率光谱密度分析,CCA,SVM).
- 异步系统被分为1阶段和2阶段的方法进行性能比较.
主要成果:
- 基于CCA的方法在两阶段方法中实现了卓越的性能.
- 这种方法的灵敏度为97.62 ± 02.06%,特异性为76.50 ± 23.50%,准确度为75.59 ± 10.09%.
结论:
- 两个阶段的异步SSVEPBCI方法与基于CCA的识别和过器银行CCA (FB-CCA) 分类相结合,显示出实践实施的巨大潜力.
- 这种配置为开发有效的实时异步BCI系统提供了强大的解决方案.
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