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All Journal Makara Journal of Technology
Tati Latifah Mengko, Tati Latifah
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering

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Analysis of Arm Movement Prediction by Using the Electroencephalography Signal Darmakusuma, Reza; Prihatmanto, Ary Setijadi; Indrayanto, Adi; Mengko, Tati Latifah; Andarini, Lidwina Ayu; Idrus, Achmad Furqon
Makara Journal of Technology Vol. 20, No. 1
Publisher : UI Scholars Hub

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Various technological approaches have been developed in order to help those people who are unfortunate enough to be afflicted with different types of paralysis which limit them in performing their daily life activities independently. One of the proposed technologies is the Brain-Computer Interface (BCI). The BCI system uses electroencephalography (EEG) which is generated by the subject’s mental activity as input, and converts it into commands. Some previous experiments have shown the capability of the BCI system to predict the movement intention before the actual movement is onset. Thus research has predicted the movement by discriminating between data in the “rest” condition, where there is no movement intention, with “pre-movement” condition, where movement intention is detected before actual movement occurs. This experiment, however, was done to analyze the system for which machine learning was applied to data obtained in a continuous time interval, between 3 seconds before the movement was detected until 1 second after the actual movement was onset. This experiment shows that the system can discriminate the “pre-movement” condition and “rest” condition by using the EEG signal in 7-30 Hz where the Mu and Beta rhythm can be discovered with an average True Positive Rate (TPR) value of 0.64 ± 0.11 and an average False Positive Rate (FPR) of 0.17 ± 0.08. This experiment also shows that by using EEG signals obtained nearing the movement onset, the system has higher TPR or a detection rate in predicting the movement intention.
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 Vol. 22, No. 1
Publisher : UI Scholars Hub

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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.
Co-Authors Achmad Furqon Idrus, Achmad Furqon Adi Indrayanto Ary Setijadi Prihatmanto Lidwina Ayu Andarini, Lidwina Ayu Reza Darmakusuma, Reza