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从皮质活动解读公开语音:不同线性方法的比较.

Gaël Le Godais1, Philémon Roussel1, Florent Bocquelet1

  • 1Univ. Grenoble Alpes, INSERM, U1216, Grenoble Institut Neurosciences, Grenoble, France.

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|July 10, 2023
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概括
此摘要是机器生成的。

线性解码器显示为实时语音大脑计算机接口 (BCI) 的承诺,将皮质活动解码为语音. 虽然表现超出了机会,但需要进一步开发可理解的语音重建.

关键词:
这是一个ECoG.关节综合症 关节综合症大脑-计算机接口接口解码的解码方法是内录音的记录.线性方法 线性方法语音假体 语音假体 语音假体

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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:

  • 语音脑计算机接口 (BCI) 旨在从皮质活动实时重建语音.
  • 快速的,毫秒时间尺度重建需要高效的计算,这使得线性解码器具有吸引力.
  • 线性解码器在运动BCI中已经很成熟,但在语音和关节运动解码方面尚未得到充分探索.

研究的目的:

  • 为了比较线性回归方法用于从皮质活动中脱机解码公开语音.
  • 调查直接声学解码与通过关运动间接解码.
  • 为了评估不同的线性解码策略的表现,用于语音重建.

主要方法:

  • 比较了香草线性回归,度调节的线性回归和部分最小平方回归.
  • 研究了两个解码范式:直接的声学特征解码和间接的解码通过表达表示.
  • 使用动态时间扭曲来估计关节轨迹,并通过特征相关性评估解码器精度.

主要成果:

  • 所有线性方法都实现了显著高于机会水平的性能.
  • 直接和间接解码范式显示了相似的性能,直接解码略有优势.
  • 当前的线性方法无法实现可理解的语音重建.

结论:

  • 线性解码器可用于从皮质活动进行初始语音重建.
  • 需要进一步的研究来改进神经语音解码器,以实现毫秒时间尺度,可理解的语音.
  • 优化解码器以实现快速的,逐重建仍然是语音BCI的一个关键挑战.