Tongue interface based on surface EMG signals of suprahyoid muscles

Sasaki, Makoto; Kohei, Onishi; Stefanov, Dimitar; Kamata, Katsuhiro; Nakayama, Atsuishi; Yoshikawa, Masahiro; Obinata, Goro

doi:10.1186/s40648-016-0048-0

Cited by 35 publications

(20 citation statements)

References 39 publications

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“…[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%

“…Sometimes, authors combine features in the time domain with those in the frequency domain. Sasaki et al [47] developed and tested a tongue interface to detect six motions, including saliva swallowing, from the surface of suprahyoid muscles at the underside of the jaw. They combined RMS from time domain with CC features from frequency domain and achieved 95.1 ± 1.9% classification accuracy.…”

Section: Svm-based Myoelectric Signal Classificationmentioning

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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Section: Number Of Channelsmentioning

confidence: 99%

Section: Referencementioning

confidence: 99%

Section: Svm-based Myoelectric Signal Classificationmentioning

confidence: 99%

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

et al. 2019

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“…With reference to such backgrounds, we have been developing an interface focusing on tongue motion (Sasaki et al, 2016). Tongue motor functions are usually preserved even in people with cervical spinal cord damage.…”

Section: Introductionmentioning

confidence: 99%

“…They also might entail various difficulties while talking and eating. Therefore, we developed a surface electromyography (sEMG)-based tongue interface to solve these problems (Sasaki et al, 2016). The proposed interface classified tongue motions from sEMG signals of the suprahyoid muscles detected from the underside of the jaw.…”

Section: Introductionmentioning

confidence: 99%

Development of sEMG-based robust oral motion classification method and its application to electric wheelchair operation

Nakai

Sasaki

Kamata

et al. 2019

Mechanical Engineering Journal

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Interfaces based on surface electromyography (sEMG) signals are one of the important methods for noninvasively extracting the intention of a severely disabled person and supporting environmental control of wheelchairs and personal computers. However, sEMG-based interfaces generally have a common and maximum disadvantage of vulnerability to changes in electrode position. In this study, we aimed to develop a robust oral motion classification method that is robust to change in electrode position. Five healthy adult male subjects participated in this experiment. sEMG signals of the suprahyoid muscles during five oral motions (right, left, up tongue motion, jaw opening, and clenching) were measured using a boomerang-shaped 22-channel electrode adhered to the underside of the jaw. Oral motion classification from sEMG signals was performed using a support vector machine (SVM). When sEMG signals measured at a position different from the 22-channnel electrode position where the training data for SVM classifier was obtained were used as the test data, the classification accuracy of five oral motions sharply decreased from 92.0% to 72.8%. In contrast, when the 10 trials of sEMG signals obtained in advance at different electrode positions on different days were used as training data, the robustness against electrode position change was improved drastically and the mean classification accuracy of all subjects reached 90.4%. Furthermore, we developed an electric wheelchair control system that can operate based on classified motions and verified its usefulness for wheelchair operability and driving performance thorough the experiment. The results showed that the proposed method can omit the SVM training process required every time after the electrode is attached and can operate the wheelchair immediately after electrode attachment. Such advancement of interfaces eliminates the annoyance caused to the user who uses the interface on a daily basis and is expected to lead to an improvement in the quality of life.

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