Comparison of EMG Based Finger Motion Classification Algorithms

Author(s):  
Enes Altan ◽  
Kubra Pehlivan ◽  
Erkan Kaplanoglu
2018 ◽  
Vol 22 (5) ◽  
pp. 1395-1405 ◽  
Author(s):  
Youjia Huang ◽  
Xingchen Yang ◽  
Yuefeng Li ◽  
Dalin Zhou ◽  
Keshi He ◽  
...  

Author(s):  
Keisuke Ishikawa ◽  
Masashi Toda ◽  
Shigeru Sakurazawa ◽  
Junichi Akita ◽  
Kazuaki Kondo ◽  
...  

2020 ◽  
Vol 15 (3) ◽  
pp. 240-247
Author(s):  
Seulah Lee ◽  
◽  
Yuna Choi ◽  
Gwangyeol Cha ◽  
Minchang Sung ◽  
...  

Motor Control ◽  
2021 ◽  
Vol 25 (1) ◽  
pp. 100-116
Author(s):  
Xiangyu Liu ◽  
Meiyu Zhou ◽  
Chenyun Dai ◽  
Wei Chen ◽  
Xinming Ye

Surface electromyogram-based finger motion classification has shown its potential for prosthetic control. However, most current finger motion classification models are subject-specific, requiring calibration when applied to new subjects. Generalized subject-nonspecific models are essential for real-world applications. In this study, the authors developed a subject-nonspecific model based on motor unit (MU) voting. A high-density surface electromyogram was first decomposed into individual MUs. The features extracted from each MU were then fed into a random forest classifier to obtain the finger label (primary prediction). The final prediction was selected by voting for all primary predictions provided by the decomposed MUs. Experiments conducted on 14 subjects demonstrated that our method significantly outperformed traditional methods in the context of subject-nonspecific finger motion classification models.


2000 ◽  
Vol 14 (3) ◽  
pp. 151-158 ◽  
Author(s):  
José Luis Cantero ◽  
Mercedes Atienza

Abstract High-resolution frequency methods were used to describe the spectral and topographic microstructure of human spontaneous alpha activity in the drowsiness (DR) period at sleep onset and during REM sleep. Electroencephalographic (EEG), electrooculographic (EOG), and electromyographic (EMG) measurements were obtained during sleep in 10 healthy volunteer subjects. Spectral microstructure of alpha activity during DR showed a significant maximum power with respect to REM-alpha bursts for the components in the 9.7-10.9 Hz range, whereas REM-alpha bursts reached their maximum statistical differentiation from the sleep onset alpha activity at the components between 7.8 and 8.6 Hz. Furthermore, the maximum energy over occipital regions appeared in a different spectral component in each brain activation state, namely, 10.1 Hz in drowsiness and 8.6 Hz in REM sleep. These results provide quantitative information for differentiating the drowsiness alpha activity and REM-alpha by studying their microstructural properties. On the other hand, these data suggest that the spectral microstructure of alpha activity during sleep onset and REM sleep could be a useful index to implement in automatic classification algorithms in order to improve the differentiation between the two brain states.


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