Electroencephalogram (EEG) signal classification using artificial neural network to control electric artificial hand movement

A. S. Saragih, A. Pamungkas, B. Y. Zain, W. Ahmed, A. S. Saragih

Research output: Contribution to journalConference articlepeer-review

Abstract

All due to the complex nature of the electroencephalography (EEG) signal, it is a challenge to be able to use it as the driver of an electric artificial hand. By using EEG signal, the command for artificial hand movements becomes more intuitive and natural. This study aims to classify EEG signals to serve as electronic hand control. Classification is conducted using artificial neural networks (ANN), in which EEG signal datasets are obtained from a commercial brain computer interface (BCI). The ANN model obtained is expected to be able to determine that the EEG signal is one of the five EEG signals generated from five predetermined hand movements. This study proposes feature extraction and processing that is very simple but performs well, indicated by its small error value. The results show that ANN can classify five hand movements tested with an overall accuracy rate of 80%.

Original languageEnglish
Article number012005
JournalIOP Conference Series: Materials Science and Engineering
Volume938
Issue number1
DOIs
Publication statusPublished - 28 Oct 2020
Event2020 7th International Conference on Mechanics and Mechatronics Research, ICMMR 2020 - Berkley, United States
Duration: 27 Jun 202029 Jun 2020

Fingerprint Dive into the research topics of 'Electroencephalogram (EEG) signal classification using artificial neural network to control electric artificial hand movement'. Together they form a unique fingerprint.

Cite this