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Electroencephalogram (EEG) Signal Classification Using Artificial Neural Network to Control Electric Artificial Hand Movement

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%.

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Electroencephalogram (EEG) Signal Classification Using Artificial Neural Network to Control Electric Artificial Hand Movement

Semantic Scholar · Engineering · 2020

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%.

Source of record · Semantic Scholar

References (5)

01Design and fabrication of prosthetic human hand using eeg and force sensor with arduino micro controllerM. Mohan, M. Purushothaman2017 · 2017 Third International Conference on Science Technology Engineering & Management (ICONSTEM) · 5 citations
02User-Friendly LabVIEW GUI for Prosthetic Hand Control Using Emotiv EEG HeadsetM. A. A. Kasim, C. Y. Low, M. A. Ayub et al.2017 · 35 citations
03EEG-based brain controlled prosthetic armDany Bright, Amrita R. Nair, Devashish Salvekar et al.2016 · 2016 Conference on Advances in Signal Processing (CASP) · 105 citations
04A systematic analysis of performance measures for classification tasksMarina Sokolova, G. Lapalme2009 · Information Processing & Management · 6.7k citations
05Development of a prosthetic hand regarding complex motion and controllabilityO. B. Lőrinczi, P. Aradi2011 · 1 citation

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