手康复训练混合脑机接口的脑肌电分析方法

IF 0.7 4区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Computing and Informatics Pub Date : 2023-01-01 DOI:10.31577/cai_2023_3_741

Lubo Fu, Hao Li, Hongfei Ji, Jie Li

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引用次数: 0

摘要

．脑机接口(bci)在帮助中风患者进行身体康复过程中显示出巨大的潜力。通过重塑连接患者大脑和四肢的神经回路，这些接口有助于运动功能的恢复，最终显著改善患者的整体生活质量。然而，目前的脑机接口主要依赖于脑电图(EEG)运动图像(MI)，其识别粒度相对粗糙，难以准确识别特定的手部动作。为了解决这一限制，本文提出了一个基于脑电图和肌电图(EEG)的混合脑机接口框架

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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EEG-EMG Analysis Method in Hybrid Brain Computer Interface for Hand Rehabilitation Training

. Brain-computer interfaces (BCIs) have demonstrated immense potential in aiding stroke patients during their physical rehabilitation journey. By reshaping the neural circuits connecting the patient’s brain and limbs, these interfaces contribute to the restoration of motor functions, ultimately leading to a significant improvement in the patient’s overall quality of life. However, the current BCI primarily relies on Electroencephalogram (EEG) motor imagery (MI), which has relatively coarse recognition granularity and struggles to accurately recognize specific hand movements. To address this limitation, this paper proposes a hybrid BCI framework based on Electroencephalogram and Electromyography (EEG-∗ Corresponding author

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来源期刊

Computing and Informatics 工程技术-计算机：人工智能

CiteScore

1.60

自引率

14.30%

发文量

审稿时长

9 months

期刊介绍： Main Journal Topics: COMPUTER ARCHITECTURES AND NETWORKING PARALLEL AND DISTRIBUTED COMPUTING THEORETICAL FOUNDATIONS SOFTWARE ENGINEERING KNOWLEDGE AND INFORMATION ENGINEERING Apart from the main topics given above, the Editorial Board welcomes papers from other areas of computing and informatics.