Hybrid Brain-Computer Interface: a Novel Method on the Integration of EEG and sEMG Signal for Active Prosthetic Control - Darmakusuma, Reza; Prihatmanto, Ary Setijadi; Indrayanto, Adi; Mengko, Tati Latifah (Makara Journal of Technology, 2018)
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.