eprintid: 9156
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/91/56
datestamp: 2023-11-09 16:21:07
lastmod: 2023-11-09 16:21:07
status_changed: 2023-11-09 16:14:24
type: article
metadata_visibility: show
creators_name: Hajian, A.
creators_name: Yong, S.-P.
title: Feature extraction from EEG data for a P300 based brain-computer interface
ispublished: pub
keywords: Administrative data processing; Biomedical signal processing; Brain; Computer control systems; Data mining; Electroencephalography; Enterprise resource management; Extraction; Feature extraction; Independent component analysis; Interfaces (computer); Learning systems; Neurophysiology; Sensory analysis, Classification accuracy; Common spatial patterns; Discriminative features; Electro-encephalogram (EEG); Feature extraction techniques; Independent component analysis(ICA); P300; Physical activity, Brain computer interface
note: cited By 2; Conference of 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017 held in conjuction with the Workshop on Machine Learning for Sensory Data Analysis, MLSDA 2017, Workshop on Biologically Inspired Data-Mining Techniques, BDM 2017, Pacific Asia Workshop on Intelligence and Security Informatics, PAISI 2017 and Workshop on Data Mining in Business Process Management, DM-BPM 2017 ; Conference Date: 23 May 2017 Through 23 May 2017; Conference Code:200079
abstract: 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.
date: 2017
publisher: Springer Verlag
official_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
id_number: 10.1007/978-3-319-67274-8₄
full_text_status: none
publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
volume: 10526 
pagerange: 39-50
refereed: TRUE
isbn: 9783319672731
issn: 03029743
citation:   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.  ISSN 03029743