Search

نمایش تعداد 1-10 از 12

Mechanomyography Assisted Myoeletric Sensing for Upper-extremity Prostheses: a Hybrid Approach

نوع: Journal Paper

نویسنده : Weichao Guo; Xinjun Sheng; Honghai Liu; Xiangyang zhu

سال: 2017

Request PDF

Toward an Enhanced Human–Machine Interface for Upper-Limb Prosthesis Control With Combined EMG and NIRS Signals

نوع: Journal Paper

نویسنده : Weichao Guo; Xinjun Sheng; Honghai Liu; Xiangyang Zhu

سال: 2017

Request PDF

Reduced Daily Recalibration of Myoelectric Prosthesis Classifiers Based on Domain Adaptation

نوع: Journal Paper

نویسنده : Jianwei Liu; Xinjun Sheng; Dingguo Zhang; Jiayuan He; Xiangyang Zhu

سال: 2016

Request PDF

Design of an Anthropomorphic Prosthetic Hand towards Neural Interface Control

نوع: Journal Paper

نویسنده : Shunchong Li; Xinjun Sheng; Jianrong Zhang; Xiangyang Zhu

سال: 2012

Request PDF

Improved Semisupervised Adaptation for a Small Training Dataset in the Brain–Computer Interface

نوع: Journal Paper

نویسنده : Jianjun Meng; Xinjun Sheng; Dingguo Zhang; Xiangyang Zhu

ناشر: IEEE

سال: 2014

Combining Motor Imagery With Selective Sensation Toward a Hybrid-Modality BCI

نوع: Journal Paper

نویسنده : Lin Yao; Jianjun Meng; Dingguo Zhang; Xinjun Sheng; Xiangyang Zhu

ناشر: IEEE

سال: 2014

Improving myoelectric pattern recognition using invariant feature extraction

نوع: Conference Paper

نویسنده : Jianwei Liu; Xinjun Sheng; Dingguo Zhang; Xiangyang Zhu

ناشر: IEEE

سال: 2014

An enhanced human-computer interface based on simultaneous sEMG and NIRS for prostheses control

نوع: Conference Paper

نویسنده : Weichao Guo; Pengfei Yao; Xinjun Sheng; Dingguo Zhang; Xiangyang Zhu

ناشر: IEEE

سال: 2014

Boosting training for myoelectric pattern recognition using Mixed-LDA

نوع: Conference Paper

نویسنده : Jianwei Liu; Xinjun Sheng; Dingguo Zhang; Xiangyang Zhu

ناشر: IEEE

سال: 2014

Effects of contraction path and velocity on the coordination of hand muscles during a three-digit force production task

نوع: Conference Paper

نویسنده : Jiayuan He; Xinjun Sheng; Dingguo Zhang; Xiangyang Zhu

ناشر: IEEE

سال: 2014

Search

Mechanomyography Assisted Myoeletric Sensing for Upper-extremity Prostheses: a Hybrid Approach

Toward an Enhanced Human–Machine Interface for Upper-Limb Prosthesis Control With Combined EMG and NIRS Signals

Reduced Daily Recalibration of Myoelectric Prosthesis Classifiers Based on Domain Adaptation

Design of an Anthropomorphic Prosthetic Hand towards Neural Interface Control

Improved Semisupervised Adaptation for a Small Training Dataset in the Brain–Computer Interface

Combining Motor Imagery With Selective Sensation Toward a Hybrid-Modality BCI

Improving myoelectric pattern recognition using invariant feature extraction

An enhanced human-computer interface based on simultaneous sEMG and NIRS for prostheses control

Boosting training for myoelectric pattern recognition using Mixed-LDA

Effects of contraction path and velocity on the coordination of hand muscles during a three-digit force production task

نویسنده

ناشر

سال

کلیدواژه

نوع

زبان

نوع محتوا

عنوان ناشر

Search

Filters

Mechanomyography Assisted Myoeletric Sensing for Upper-extremity Prostheses: a Hybrid Approach

Toward an Enhanced Human–Machine Interface for Upper-Limb Prosthesis Control With Combined EMG and NIRS Signals

Reduced Daily Recalibration of Myoelectric Prosthesis Classifiers Based on Domain Adaptation

Design of an Anthropomorphic Prosthetic Hand towards Neural Interface Control

Improved Semisupervised Adaptation for a Small Training Dataset in the Brain&#x2013;Computer Interface

Combining Motor Imagery With Selective Sensation Toward a Hybrid-Modality BCI

Improving myoelectric pattern recognition using invariant feature extraction

An enhanced human-computer interface based on simultaneous sEMG and NIRS for prostheses control

Boosting training for myoelectric pattern recognition using Mixed-LDA

Effects of contraction path and velocity on the coordination of hand muscles during a three-digit force production task

Improved Semisupervised Adaptation for a Small Training Dataset in the Brain–Computer Interface