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

This paper describes a novel method for controlling active prosthetics by integrating surface electrom y graphy (sEMG) and electroencephalograph signals to improve its in tuit veness. This paper also compares the new metho d (RTA-2) with other existing methods (AND and OR) for controlling active prosthetics. Based on analysis, RTA-2 featu r s 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. An alysis also shows that RTA-2 possesses equal TPR, F and BA with the detection of movement intention using sEMG -based system. Although the RTA-2 method shows equa l performance with the sEMG-based system, it presents an advantage for driving active prosthetics to mov e faster and to reduce its total time response by generating more m ove ent commands.