共享语音生成的潜在表现,用于跨患者的语音解码
bioRxiv : the preprint server for biology
|September 2, 2025
概括
结合患者数据可以改善语音脑电脑接口 (BCI). 这种方法使神经数据与共享空间保持一致,使神经运动障碍患者能够更快,更准确地恢复沟通.
科学领域:
- 神经科学
- 生物医学工程
- 康复技术
背景情况:
- 语音脑电脑接口 (BCI) 为神经运动障碍患者提供沟通恢复.
- 目前的BCI需要大量的患者特异性数据,限制了可用性和部署速度.
- 组合患者数据的挑战包括神经解剖学差异和不同的电极放置.
研究的目的:
- 使用多个患者的综合数据开发训练语音BCI的方法.
- 克服患者特定数据要求的局限性,加快BCI的部署.
- 提高语音BCI的准确性和可用性.
主要方法:
- 使用正规关联分析 (CCA) 将患者特异的神经数据调整为共享的隐藏空间.
- 使用高密度微电皮质谱 (μECoG) 进行详细的神经信号捕获.
- 在聚合和对齐的神经数据上培训的跨患者解码模型.
主要成果:
- 在使用CCA的患者中成功发现共享的神经潜伏动态.
- 证明了对齐的神经数据可以保存微观语音信息.
- 与患者特定模型相比,跨患者模型实现了更好的解码精度.
- 突出了μECoG的高分辨率和广泛覆盖的好处.
结论:
- 将神经数据与共享的潜伏空间对齐,可以有效地训练多患者数据的语音BCI.
- 这种方法显著提高了BCI的准确性,并促进了快速部署.
- 未来的语音BCI可以更有效,改善沟通障碍患者的生活质量.
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