Machine-learning approaches for recognizing muscle activities involved in facial expressions captured by multi-channels surface electromyogram

Cai, Yi; Guo, Yifan; Jiang, Haotian; Huang, Ming-Chun

doi:10.1016/j.smhl.2017.11.002

Cited by 18 publications

(20 citation statements)

References 21 publications

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“…Sampling Frequency [15,[32][33][34] 500 Hz [1,9,10,[12][13][14][17][18][19][20][21][27][28][29] 1 kHz [2] 1.5 kHz [7,8,11,16,30,[35][36][37] 2 kHz [22] 3 kHz [6,25,26] 4 kHz [31] 10 kHz…”

Section: Referencementioning

confidence: 99%

“…[25] 1 [16,17,20,26] 2 [24,31] 3 [1,[8][9][10]13,21,23,29,35,39,[44][45][46] 4 [19,36,40,41] 6 [2,7,11,15,22,30,32,34] 8 [37] 12 [33] 14 [12,14] 16 [47] 22 Table 4. Electrodes type and place of electrode placement body.…”

Section: Number Of Channelsmentioning

confidence: 99%

“…Electrode Type and Body Region [5,14,16,18,19,37,39,40,[48][49][50] Monopolar and Upper limb [2,12,26,51] Monopolar and Lower limb [36,47] Monopolar and Facial muscles [4,6,[9][10][11]15,20,22,24,27,29,31,32,34,35,44,[52][53][54] Bipolar and Upper limb [13,30] Bipolar and Lower limb [45] Bipolar and Cheek [7] Bipolar and Facial muscles…”

Section: Referencementioning

confidence: 99%

“…They combined RMS from time domain with CC features from frequency domain and achieved 95.1 ± 1.9% classification accuracy. In addition, Cai et al [36] performed a classification of eight facial expressions. They used 74 features for each expression using only six channels, and achieved 99.6% of overall accuracy with a cubic kernel in SVM classifier.…”

Section: Svm-based Myoelectric Signal Classificationmentioning

confidence: 99%

“…It is the mean value of the differential value of all peak values of the EMG signal. MDV is described by [36]:…”

Section: Median Differential Value (Mdv)mentioning

confidence: 99%

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Support Vector Machine-Based EMG Signal Classification Techniques: A Review

et al. 2019

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This paper gives an overview of the different research works related to electromyographic signals (EMG) classification based on Support Vector Machines (SVM). The article summarizes the techniques used to make the classification in each reference. Furthermore, it includes the obtained accuracy, the number of signals or channels used, the way the authors made the feature vector, and the type of kernels used. Hence, this article also includes a compilation about the bands used to filter signals, the number of signals recommended, the most commonly used sampling frequencies, and certain features that can create the characteristics of the vector. This research gathers articles related to different kinds of SVM-based classification and other tools for signal processing in the field.

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“…Sampling Frequency [15,[32][33][34] 500 Hz [1,9,10,[12][13][14][17][18][19][20][21][27][28][29] 1 kHz [2] 1.5 kHz [7,8,11,16,30,[35][36][37] 2 kHz [22] 3 kHz [6,25,26] 4 kHz [31] 10 kHz…”

Section: Referencementioning

confidence: 99%

Section: Number Of Channelsmentioning

confidence: 99%

Section: Referencementioning

confidence: 99%

Section: Svm-based Myoelectric Signal Classificationmentioning

confidence: 99%

“…It is the mean value of the differential value of all peak values of the EMG signal. MDV is described by [36]:…”

Section: Median Differential Value (Mdv)mentioning

confidence: 99%

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Support Vector Machine-Based EMG Signal Classification Techniques: A Review

et al. 2019

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Human–Machine Interfaces Based on Bioelectric Signals: A Narrative Review with a Novel System Proposal

Hayashi

Tsuji

2022

IEEJ Transactions Elec Engng

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Bioelectric signals such as electromyogram (EMG) and electroencephalogram (EEG) reflect human internal states and intended actions, and have therefore been applied to various types of human-machine interfaces. Such biosignal-based interfaces are beneficial because they not only allow intuitive control of machines, but can also support persons with disabilities. In this paper, we review the recent studies on human-machine interfaces based on bioelectric signals including EMG and EEG signals. Furthermore, we introduce our recent work on the development of a brain-machine interface for domestic appliance control.

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