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All Journal Andalas Journal of Electrical and Electronic Engineering Technology
Minoru Sasaki
Gifu University; Tokai National Higher Education and Research System
Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering

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Estimation of the Shoulder Joint Angle using Brainwaves Minoru Sasaki; Takaaki Iida; Joseph Muguro; Waweru Njeri; Pringgo Widyo Laksono; Muhammad Syaiful Amri bin Suhaimi; Muhammad Ilhamdi Rusydi
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 1 No. 1 (2021): May 2021
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1224.625 KB) | DOI: 10.25077/ajeeet.v1i1.5

Abstract

This paper presents the angle of the shoulder joint as basic research for developing a machine interface using EEG. The raw EEG voltage signals and power density spectrum of the voltage value were used as the learning feature. Hebbian learning was used on a multilayer perceptron network for pattern classification for the estimation of joint angles 0o, 90o and 180o of the shoulder joint. Experimental results showed that it was possible to correctly classify up to 63.3% of motion using voltage values of the raw EEG signal with the neural network. Further, with selected electrodes and power density spectrum features, accuracy rose to 93.3% with more stable motion estimation.
Estimation of the Shoulder Joint Angle using Brainwaves Minoru Sasaki; Takaaki Iida; Joseph Muguro; Waweru Njeri; Pringgo Widyo Laksono; Muhammad Syaiful Amri bin Suhaimi; Muhammad Ilhamdi Rusydi
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 1 No. 1 (2021): May 2021
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/ajeeet.v1i1.5

Abstract

This paper presents the angle of the shoulder joint as basic research for developing a machine interface using EEG. The raw EEG voltage signals and power density spectrum of the voltage value were used as the learning feature. Hebbian learning was used on a multilayer perceptron network for pattern classification for the estimation of joint angles 0o, 90o and 180o of the shoulder joint. Experimental results showed that it was possible to correctly classify up to 63.3% of motion using voltage values of the raw EEG signal with the neural network. Further, with selected electrodes and power density spectrum features, accuracy rose to 93.3% with more stable motion estimation.
Co-Authors Joseph Muguro Muhammad Syaiful Amri bin Suhaimi Pringgo Widyo Laksono Rusydi, Muhammad Ilhamdi Takaaki Iida Waweru Njeri