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使用基于深度学习的解码进行持续跟踪,用于非侵入性脑计算机接口的解码

Dylan Forenzo¹, Hao Zhu¹, Jenn Shanahan¹

¹Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

|May 1, 2024

概括

深度学习解码器在复杂的任务中显著提高了脑计算机接口 (BCI) 性能. 这一进步增强了BCI应用,适用于健康人群和运动障碍者.

科学领域:

神经科学是一个神经科学.
计算机科学计算机科学
生物医学工程生物医学工程

背景情况:

使用脑电图的非侵入性脑电脑接口 (BCI) 可以在没有肌肉激活的情况下进行互动.
当前的BCI在性能一致性和自由度方面存在局限性,限制了它们的应用.

研究的目的:

调查基于深度学习 (DL) 的解码器对复杂的BCI任务的有效性:在线连续追踪 (CP).
评估DL模型性能,并将其与传统的BCI解码器进行比较.

主要方法:

开发了CP数据的标签系统,以实现监督学习.
训练有素的DL解码器使用两个架构,包括一个新的PointNet适应.
评估了28名参与者在多个在线会议中的解码器性能.

主要成果:

基于DL的模型显示,随着培训数据的增加,在整个课程中表现有所改善.
在最后一个会话中,DL解码器显著超过了传统的BCI解码器.
中期会议模型更新显示了潜在的好处,而受试者预训练并没有显著提高表现.

结论:

深度学习解码器有效地提高BCI在复杂任务中的性能,例如持续追求.
提高BCI性能可以扩大BCI设备的适用性.
这些进展有望改善运动障碍或没有运动障碍的个人的生活质量.

关键词:

大脑计算机接口不断的追求,不断的追求.深度学习是一种深度学习.人类机器智能人机智能运动影像图像学

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