Time Analysis of Surface Electromyography Signal for Active Prosthetics Control

Darmakusuma, Reza; Prihatmanto, Ary Setijadi; Indrayanto, A.; Mengko, Tati L. R.

doi:10.17654/ec016020381

Cited by 1 publication

(4 citation statements)

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“…The RTA-2 method can also be implemented in proportional active prosthetics. As movement velocity command of active prosthetics uses the threshold method, [12][13] hence, by integrating with the BCI system, the active prosthetics will feature a xN 2 level of movement velocity command (with N existing at the level of active prosthetics based on sEMG signals).…”

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…In this analysis, an on/off sEMG-based active prosthetic [12,13], is used to prove the effectiveness of the alternative method (RTA-2). This consideration is based on the fact that the on/off active prosthetic system is the simplest function for active prosthetics [12,13]. On the other hand, this analysis also uses the BCI system that was implemented by Kirchner et al [5], Lew et al [6] and Planellas et al [8].…”

Section: Methodsmentioning

confidence: 99%

“…After all sensory information are received by the brain, humans deliberates on procedural activities to response to stimuli. Some examples of this phenomenon can be observed from event-related desynchronization/event-related synchronization of brain signals if the response(s) shows correlation with body movements [5][6][7][8][9][10][11][12][13][14][15][16][17][18]. Thus, a movement starts when an individual thinks or intends to move his body until the end of movement.…”

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Hybrid Brain-Computer Interface: a Novel Method on the Integration of EEG and sEMG Signal for Active Prosthetic Control

Darmakusuma¹,

Prihatmanto²,

Indrayanto³

et al. 2018

MST

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This paper describes a novel method for controlling active prosthetics by integrating surface electromyography (sEMG) and electroencephalograph signals to improve its intuitiveness. This paper also compares the new method (RTA-2) with other existing methods (AND and OR) for controlling active prosthetics. Based on analysis, RTA-2 features higher true positive rate (TPR) and balanced accuracy (BA) than AND method. On the other hand, the new method (RTA-2) yields lower false detection rate (FPR) than OR method. Analysis also shows that RTA-2 possesses equal TPR, FPR, and BA with the detection of movement intention using sEMG-based system. Although the RTA-2 method shows equal performance with the sEMG-based system, it presents an advantage for driving active prosthetics to move faster and to reduce its total time response by generating more movement commands. Abstrak Hybrid Brain-Computer Interface: Metode Baru dalam Integrasi Sinyal EEG dan sEMG untuk Pengendalian Aktif Prosthetics. Paper ini menjelaskan metode baru untuk mengendalikan prosthetics aktif dengan mengintegrasikan sinyal elektromiograf (sEMG) dan elektroensefalograf (EEG) dalam rangka meningkatkan sifat intuitif yang dimilikinya. Selain itu, dalam paper ini juga membandingkan metode baru (RTA-2) dengan metode lain yang telah ada (AND dan OR) untuk mengendilikan prosthetics aktif. Berdasarkan analisis, metode RTA-2 memiliki nilai True Positive Rate (TPR) dan Balanced Accuracy (BA) lebih tinggi dibandingkan metode AND. Selain hal terebut, metode RTA-2 memilki kesalahan deteksi (FPR) yang lebih rendah dibandingkan metode OR. Berdasarkan analasis, nilai TPR, FPR dan BA yang dimiliki metode RTA-2 ini sama dengan akurasi deteksi intensi gerakan berbasis sinyal sEMG. Namun demikian, meskipun TPR, FPR dan BA dari metode RTA-2 sama dengan metode yang hanya berbasis sinyal sEMG, metode RTA-2 memiliki keunggulan dalam mengendalikan prosthetic aktif sehingga dapat bergerak dengan kecepatan lebih cepat dari sebelumnya dan mengurangi total waktu responnya dengan cara menghasilkan perintah keluaran kecepatan gerakan yang lebih banyak.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%