Human-robot interaction in motor imagery: A system based on the STFCN for unilateral upper limb rehabilitation assistance

IF 2.7 4区医学 Q2 BIOCHEMICAL RESEARCH METHODS

Journal of Neuroscience Methods Pub Date : 2024-08-05 DOI:10.1016/j.jneumeth.2024.110240

Hui Tian

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引用次数: 0

Abstract

Background

Rehabilitation training based on the brain-computer interface of motor imagery (MI-BCI) can help restore the connection between the brain and movement. However, the performance of most popular MI-BCI system is coarse-level, which means that they are good at guiding the rehabilitation exercises of different parts of the body, but not for the individual component.

New methods

In this paper, we designed a fine-level MI-BCI system for unilateral upper limb rehabilitation assistance. Besides, due to the low discrimination of different sample classes in a single part, a classification algorithm called spatial-temporal filtering convolutional network (STFCN) was proposed that used spatial filtering and deep learning.

Comparison with existing methods

Our STFCN outperforms popular methods in recent years using BCI IV 2a and 2b data sets.

Results

To verify the effectiveness of our system, we recruited 6 volunteers and collected their data for a four-classification online experiments, resulting in an average accuracy of 62.7 %.

Conclusion

This fine-level MI-BCI system has good appli-cation prospects, and inspires more exploration of rehabilitation in a single part of the human body.

查看原文本刊更多论文

运动想象中的人机交互：基于 STFCN 的单侧上肢康复辅助系统。

背景：基于运动意象脑机接口（MI-BCI）的康复训练有助于恢复大脑与运动之间的联系。然而，大多数流行的 MI-BCI 系统的性能都比较粗糙，这意味着它们只能很好地指导身体不同部位的康复训练，而不能指导单个组件的康复训练。新方法本文设计了一种用于单侧上肢康复辅助的精细级 MI-BCI 系统。此外，由于单个部位不同样本类别的区分度较低，我们提出了一种名为空间-时间滤波卷积网络（STFCN）的分类算法，该算法使用了空间滤波和深度学习：使用 BCI IV 2a 和 2b 数据集，我们的 STFCN 优于近年来流行的方法：为了验证我们系统的有效性，我们招募了 6 名志愿者，收集他们的数据进行四分类在线实验，结果平均准确率为 62.7%：结论：这一精细级别的 MI-BCI 系统具有良好的应用前景，将为人体单个部位的康复探索带来更多启发。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Neuroscience Methods 医学-神经科学

CiteScore

7.10

自引率

3.30%

发文量

226

审稿时长

52 days

期刊介绍： The Journal of Neuroscience Methods publishes papers that describe new methods that are specifically for neuroscience research conducted in invertebrates, vertebrates or in man. Major methodological improvements or important refinements of established neuroscience methods are also considered for publication. The Journal''s Scope includes all aspects of contemporary neuroscience research, including anatomical, behavioural, biochemical, cellular, computational, molecular, invasive and non-invasive imaging, optogenetic, and physiological research investigations.