通过分布式大脑记录转移学习使可靠的语音解码成为可能
Aditya Singh1,2, Tessy Thomas1,2, Jinlong Li1,2,3
1Vivian L. Smith Department of Neurosurgery, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA.
Nature communications
|October 1, 2025
概括
这项研究引入了一种新的脑计算机接口 (BCI) 语音解码,该接口使用转移学习来提高个人之间的准确性. 这种方法增强了那些有沟通障碍的人的语音假肢.
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
- 人工智能的人工智能
背景情况:
- 语音脑电脑接口 (BCI) 集成神经记录和大型语言模型,用于实时语音解码.
- 目前的BCI需要广泛的皮质覆盖,由于个体大脑变异而限制可扩展性.
研究的目的:
- 为神经语音解码开发可扩展的转移学习策略.
- 为了提高BCI在具有异质大脑组织的个体中普遍性的可用性.
主要方法:
- 在一项苛刻的语音运动任务中,利用了大量人群的立体电脑学 (sEEG) 记录.
- 采用序列对序列模型来解码可变长度的语音序列.
- 开发了一个跨学科的转移学习框架,以确定共享的神经模式.
主要成果:
- 集团衍生解码器显著优于单个模型,证明了增强的解码稳定性.
- 转移学习框架成功地隔离了跨学科的共享潜伏多元体.
- 尽管神经覆盖和激活模式可变,但实现了可靠的语音解码.
结论:
- 突出了通用神经假体用于言语和语言障碍的途径.
- 利用分布式采样和共享任务需求的大规模内数据集对于BCI的进步至关重要.
- 这种方法为未来的语音BCI提供了更好的可扩展性和稳定性.
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