Feature Extraction of Electroencephalography Signals Using Fast Fourier Transform
This article discusses a method within the area of brain-computer interface. The proposed method is to use the features extracted from the Electroencephalograph signal and a three hidden-layer artificial neural network to map the brain signal features to the computer cursor movement. The evaluated f...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Bina Nusantara University
2016-10-01
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| Series: | CommIT Journal |
| Subjects: | |
| Online Access: | https://journal.binus.ac.id/index.php/commit/article/view/1548 |
| _version_ | 1797711882366222336 |
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| author | Hindarto Hindarto Sumarno Sumarno |
| author_facet | Hindarto Hindarto Sumarno Sumarno |
| author_sort | Hindarto Hindarto |
| collection | DOAJ |
| description | This article discusses a method within the area of brain-computer interface. The proposed method is to use the features extracted from the Electroencephalograph signal and a three hidden-layer artificial neural network to map the brain signal features to the computer cursor movement. The evaluated features are the root mean square and the average power spectrum. The empirical evaluation using 200 records taken from 2003 BCI Competition dataset shows that the current approach can accurately classify a simple cursor movement within 92.5% accuracy in a short computation time. |
| first_indexed | 2024-03-12T07:13:06Z |
| format | Article |
| id | doaj.art-c3973b775489429587f6264795c7c1d3 |
| institution | Directory Open Access Journal |
| issn | 1979-2484 2460-7010 |
| language | English |
| last_indexed | 2024-03-12T07:13:06Z |
| publishDate | 2016-10-01 |
| publisher | Bina Nusantara University |
| record_format | Article |
| series | CommIT Journal |
| spelling | doaj.art-c3973b775489429587f6264795c7c1d32023-09-02T22:59:33ZengBina Nusantara UniversityCommIT Journal1979-24842460-70102016-10-01102495210.21512/commit.v10i2.15481400Feature Extraction of Electroencephalography Signals Using Fast Fourier TransformHindarto Hindarto0Sumarno Sumarno1Universitas Muhammadiyah SidoarjoMuhammadiyah Sidoarjo UniversityThis article discusses a method within the area of brain-computer interface. The proposed method is to use the features extracted from the Electroencephalograph signal and a three hidden-layer artificial neural network to map the brain signal features to the computer cursor movement. The evaluated features are the root mean square and the average power spectrum. The empirical evaluation using 200 records taken from 2003 BCI Competition dataset shows that the current approach can accurately classify a simple cursor movement within 92.5% accuracy in a short computation time.https://journal.binus.ac.id/index.php/commit/article/view/1548Electroencephalography (EEG)Brain Com- puter Interface (BCI)Fast Fourier Transform (FFT) |
| spellingShingle | Hindarto Hindarto Sumarno Sumarno Feature Extraction of Electroencephalography Signals Using Fast Fourier Transform CommIT Journal Electroencephalography (EEG) Brain Com- puter Interface (BCI) Fast Fourier Transform (FFT) |
| title | Feature Extraction of Electroencephalography Signals Using Fast Fourier Transform |
| title_full | Feature Extraction of Electroencephalography Signals Using Fast Fourier Transform |
| title_fullStr | Feature Extraction of Electroencephalography Signals Using Fast Fourier Transform |
| title_full_unstemmed | Feature Extraction of Electroencephalography Signals Using Fast Fourier Transform |
| title_short | Feature Extraction of Electroencephalography Signals Using Fast Fourier Transform |
| title_sort | feature extraction of electroencephalography signals using fast fourier transform |
| topic | Electroencephalography (EEG) Brain Com- puter Interface (BCI) Fast Fourier Transform (FFT) |
| url | https://journal.binus.ac.id/index.php/commit/article/view/1548 |
| work_keys_str_mv | AT hindartohindarto featureextractionofelectroencephalographysignalsusingfastfouriertransform AT sumarnosumarno featureextractionofelectroencephalographysignalsusingfastfouriertransform |