机译:基于表面肌电的人机界面分类方法的性能比较
College of Mathematics and Information, South China Agricultural University, Guangzhou, China;
School of Automation Science and Engineering, South China University of Technology, Guangzhou, China Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, Shanghai, China;
Faculty of Engineering and Natural Sciences, Aalesund University College, Aalesund, Norway;
BIOPATREC Dataset; Human-Machine Interface; Pattern Classification; Power Spectrum; Surface Electromyography (sEMG);
机译:基于表面肌电图的人机界面,可以最大程度地减少肌肉疲劳的影响
机译:纳米纹理形状和表面能对电粘性人机界面性能的影响
机译:残疾人P300脑机接口分类方法的比较
机译:窗口条件参数对基于EMG的特征提取方法的分类性能和稳定性的影响
机译:基于EMG的机器人控制接口:超越解码
机译:残疾人P300脑机接口分类方法的比较
机译:基于表面肌电图的人机界面,可以最大程度地减少肌肉疲劳的影响