通过利用言语的被动感知来对隐蔽言语进行在线的第三级分类
1Institute of Biomedical Engineering, University of Toronto, Holland Bloorview Kid's Rehabilitation Hospital, Toronto, Ontario, Canada.
International journal of neural systems
|July 31, 2023
概括
这项研究表明,脑-计算机接口 (BCI) 可以通过使用语音感知 (SP) 数据来改善隐蔽语音 (CS) 信号质量. 这种方法可能会减少培训通信BCI所需的努力.
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
- 信号处理 信号处理
背景情况:
- 大脑-计算机接口 (BCI) 为语言障碍患者提供了沟通替代方案.
- 基于隐蔽语音 (CS) 的BCI依赖于想象式语音,是直观的,但需要广泛的数据收集.
- 现有研究表明,在电脑电图 (EEG) 信号中,隐蔽语音 (CS) 和语音感知 (SP) 之间存在共享的神经编码模式.
研究的目的:
- 调查一个利用SP和CS之间的信号相似性的模型是否可以有效地实时区分与语音相关的EEG信号.
- 探索一种新的方法来提高CS信号的质量,并减少BCI培训的收集负担.
主要方法:
- 十名参与者参与了一项协议,包括倾听,然后在脑海中练习单词.
- 离线分析确定了基于EEG信号的最可分离的单词,用于在线分类.
- 开发了一个功能映射模型,使用Riemannian方法从SP和CS信号中提取特征.
- 在线测试中实施了三元分类任务 (两个单词和休息).
主要成果:
- 平均在线分类准确率达到了75.3%,远高于60%的机会水平.
- 个体参与者的准确率高达93%.
- 基于CS-SP对应的感知隐蔽建模,增强了CS信号的信号噪声比 (SNR),特别是在高频频段.
结论:
- 这些发现表明使用SP-CS信号相似性的可行性,以提高在线BCI性能.
- 这种方法为培训演讲BCI中的数据收集瓶提供了潜在的解决方案.
- 减少数据收集负担可以加速通信BCI的发展和词汇扩展.
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