Feature extraction from EEG data for a P300 based brain-computer interface

Brain-computer interface (BCI) is an input method that helps users to control a computer system using their brain activity rather than a physical activity that is required when using a keyboard or mouse. BCI can be especially helpful for users with limb disabilities or limitations as it does not req...

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Main Authors: Hajian, A., Yong, S.-P.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.20273 /
Published: Springer Verlag 2017
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031396072&doi=10.1007%2f978-3-319-67274-8_4&partnerID=40&md5=236693212fe1afbbc1415ae82e3f8d1e
http://eprints.utp.edu.my/20273/
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id utp-eprints.20273
recordtype eprints
spelling utp-eprints.202732018-04-23T01:01:42Z Feature extraction from EEG data for a P300 based brain-computer interface Hajian, A. Yong, S.-P. Brain-computer interface (BCI) is an input method that helps users to control a computer system using their brain activity rather than a physical activity that is required when using a keyboard or mouse. BCI can be especially helpful for users with limb disabilities or limitations as it does not require any muscle movement and instead relies on user’s brain activity. These brain activities are recorded using electroencephalogram (EEG). Classification of the EEG data will help to map the relevant data to certain stimuli effect. The work in this paper is aiming to find a feature extraction technique that can lead to improve the classification accuracy of EEG based BCI systems that are specifically designed for incapacitated subjects. Through the experiments, the implementation of Independent Component Analysis (ICA) and Common Spatial Pattern (CSP) extracted features from P300 based BCI EEG data and it was found that ICA and CSP produce more discriminative feature sets as compared to raw EEG signals. © 2017, Springer International Publishing AG. Springer Verlag 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031396072&doi=10.1007%2f978-3-319-67274-8_4&partnerID=40&md5=236693212fe1afbbc1415ae82e3f8d1e Hajian, A. and Yong, S.-P. (2017) Feature extraction from EEG data for a P300 based brain-computer interface. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10526 . pp. 39-50. http://eprints.utp.edu.my/20273/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Brain-computer interface (BCI) is an input method that helps users to control a computer system using their brain activity rather than a physical activity that is required when using a keyboard or mouse. BCI can be especially helpful for users with limb disabilities or limitations as it does not require any muscle movement and instead relies on user’s brain activity. These brain activities are recorded using electroencephalogram (EEG). Classification of the EEG data will help to map the relevant data to certain stimuli effect. The work in this paper is aiming to find a feature extraction technique that can lead to improve the classification accuracy of EEG based BCI systems that are specifically designed for incapacitated subjects. Through the experiments, the implementation of Independent Component Analysis (ICA) and Common Spatial Pattern (CSP) extracted features from P300 based BCI EEG data and it was found that ICA and CSP produce more discriminative feature sets as compared to raw EEG signals. © 2017, Springer International Publishing AG.
format Article
author Hajian, A.
Yong, S.-P.
spellingShingle Hajian, A.
Yong, S.-P.
Feature extraction from EEG data for a P300 based brain-computer interface
author_sort Hajian, A.
title Feature extraction from EEG data for a P300 based brain-computer interface
title_short Feature extraction from EEG data for a P300 based brain-computer interface
title_full Feature extraction from EEG data for a P300 based brain-computer interface
title_fullStr Feature extraction from EEG data for a P300 based brain-computer interface
title_full_unstemmed Feature extraction from EEG data for a P300 based brain-computer interface
title_sort feature extraction from eeg data for a p300 based brain-computer interface
publisher Springer Verlag
publishDate 2017
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031396072&doi=10.1007%2f978-3-319-67274-8_4&partnerID=40&md5=236693212fe1afbbc1415ae82e3f8d1e
http://eprints.utp.edu.my/20273/
_version_ 1741196345351340032
score 11.62408

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