EMG-EEG dataset for Upper-Limb Gesture Classification

Electromyography (EMG) has limitations in human machine interface due to disturbances like electrode-shift, fatigue, and subject variability. A potential solution to prevent model degradation is to combine multi-modal data such as EMG and electroencephalography (EEG). This study presents an EMG-EEG dataset for enhancing the development of upper-limb assistive rehabilitation devices. The dataset, acquired from thirty-three volunteers without neuromuscular dysfunction or disease using commercial biosensors is easily replicable and deployable. The dataset consists of seven distinct gestures to maximize performance on the Toronto Rehabilitation Institute hand function test and the Jebsen-Taylor hand function test. The authors aim for this dataset to benefit the research community in creating intelligent and neuro-inspired upper limb assistive rehabilitation devices.肌电图（Electromyography, EMG）在人机交互领域存在局限性，其受限于电极偏移、信号疲劳以及个体差异等干扰因素。为避免模型性能退化，可行的解决方案是融合肌电图与脑电图（electroencephalography, EEG）这类多模态数据。本研究公开了一套肌电图-脑电图数据集，旨在助力上肢辅助康复设备的研发。该数据集通过商用生物传感器采集自33名无神经肌肉功能障碍或疾病的健康志愿者，具备良好的可复现性与部署性。数据集涵盖7种独立手势，旨在优化在多伦多康复研究院手部功能测试及捷森-泰勒手部功能测试中的性能表现。本数据集旨在为致力于研发智能且受神经机制启发的上肢辅助康复设备的科研群体提供支持。

搜集汇总

背景与挑战

背景概述

该数据集是一个多模态数据集，包含33名健康志愿者的上肢手势EMG和EEG数据，旨在支持智能上肢辅助康复设备的研究。数据集包含7种手势，每种手势有6次重复，数据以.csv和.mat格式提供，适合用于监督机器学习任务。