从电皮质学来分类语音模式:电极和参与者之间的传输
Aurélie de Borman1, Benjamin Wittevrongel1, Bob Van Dyck1
1Laboratory for Neuro- and Psychophysiology, KU Leuven, Leuven, Belgium.
Journal of neural engineering
|July 22, 2025
概括
这项研究通过分类说话,倾听和沉默期间的大脑活动,开发了用于脑计算机接口 (BCI) 的准确语音探测器. 这些检测器通过将预期语音与其他语言处理区分开来,对现实世界的BCI应用具有前景.
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
- 机器学习 机器学习
背景情况:
- 语音脑电脑接口 (BCI) 旨在恢复言语丧失患者的沟通.
- 语音探测器对于BCI来说至关重要,以区分语音意图与沉默.
- 现有的探测器必须解释非说话语言相关的大脑活动,如阅读或倾听.
研究的目的:
- 分析各种语音模式中的大脑活动:说话,倾听,想象说话,阅读和口语.
- 开发和评估使用电皮质谱 (ECoG) 数据的语音模式分类器.
- 评估训练有素的分类器在单电极和多电极配置的参与者之间的可转移性.
主要方法:
- 收集了29名参与者执行不同语音相关任务的ECoG数据.
- 开发了用于语音模式检测的线性分类器.
- 评估了单电极和多电极组件的分类准确性.
- 评估了对二进制和多类场景的跨参与者分类器可转移性.
主要成果:
- 实现了高分类准确率:单电极分类器的88.89%和多电极分类器的96.49%,区分说话,听话和沉默.
- 在上旋和感觉运动皮质上确定了最佳电极位置.
- 单电极分类器在记录站点之间成功转移.
- 与多类任务相比,多电极分类器在二进制任务中显示出更好的可转移性.
结论:
- 准确的语音检测对于可靠的语音BCI至关重要,防止错误输出,并允许在实验室外使用.
- 分类器的跨参与者转移对于减少培训时间是有价值的,特别是当学科培训很困难时.
- 开发的语音模式分类器具有推动实际语音BCI技术的巨大潜力.
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