Temporal modeling of EEG during self-paced hand movement and its application in onset detection

The temporal behavior of electroencephalography (EEG) recorded during self-paced hand movement is investigated for the purpose of improving EEG classification in general and onset detection in particular. Four temporal models based on conditional random fields are developed and applied to classify E...

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Published inJournal of neural engineering Vol. 8; no. 5; p. 056015
Main Authors Hasan, Bashar Awwad Shiekh, Gan, John Q
Format Journal Article
LanguageEnglish
Published England IOP Publishing 01.10.2011
Subjects
Adult
Algorithms
Brain - physiology
Data Interpretation, Statistical
Electroencephalography - methods
Electroencephalography - statistics & numerical data
Electromyography - methods
Electromyography - statistics & numerical data
Female
Hand - physiology
Humans
Linear Models
Male
Markov Chains
Models, Statistical
Movement - physiology
Random Allocation
Reproducibility of Results
Signal Processing, Computer-Assisted
User-Computer Interface
Online AccessGet full text
ISSN1741-2552
1741-2560
1741-2552
DOI10.1088/1741-2560/8/5/056015

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Abstract The temporal behavior of electroencephalography (EEG) recorded during self-paced hand movement is investigated for the purpose of improving EEG classification in general and onset detection in particular. Four temporal models based on conditional random fields are developed and applied to classify EEG data into the movement or idle class. They are further used for building an onset detection system and tested on self-paced EEG signals recorded from five subjects. True-false rates ranging from 74% to 98% have been achieved on different subjects, with significant improvement over non-temporal methods. The effectiveness of the proposed methods suggests their potential use in self-paced brain-computer interfaces.
AbstractList The temporal behavior of electroencephalography (EEG) recorded during self-paced hand movement is investigated for the purpose of improving EEG classification in general and onset detection in particular. Four temporal models based on conditional random fields are developed and applied to classify EEG data into the movement or idle class. They are further used for building an onset detection system and tested on self-paced EEG signals recorded from five subjects. True-false rates ranging from 74% to 98% have been achieved on different subjects, with significant improvement over non-temporal methods. The effectiveness of the proposed methods suggests their potential use in self-paced brain-computer interfaces.The temporal behavior of electroencephalography (EEG) recorded during self-paced hand movement is investigated for the purpose of improving EEG classification in general and onset detection in particular. Four temporal models based on conditional random fields are developed and applied to classify EEG data into the movement or idle class. They are further used for building an onset detection system and tested on self-paced EEG signals recorded from five subjects. True-false rates ranging from 74% to 98% have been achieved on different subjects, with significant improvement over non-temporal methods. The effectiveness of the proposed methods suggests their potential use in self-paced brain-computer interfaces.
The temporal behavior of electroencephalography (EEG) recorded during self-paced hand movement is investigated for the purpose of improving EEG classification in general and onset detection in particular. Four temporal models based on conditional random fields are developed and applied to classify EEG data into the movement or idle class. They are further used for building an onset detection system and tested on self-paced EEG signals recorded from five subjects. True-false rates ranging from 74% to 98% have been achieved on different subjects, with significant improvement over non-temporal methods. The effectiveness of the proposed methods suggests their potential use in self-paced brain-computer interfaces.
Author Hasan, Bashar Awwad Shiekh
Gan, John Q
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CitedBy_id crossref_primary_10_1109_TBME_2019_2900206
crossref_primary_10_1109_TNSRE_2017_2754879
crossref_primary_10_3389_fnins_2018_00540
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SubjectTerms Adult
Algorithms
Brain - physiology
Data Interpretation, Statistical
Electroencephalography - methods
Electroencephalography - statistics & numerical data
Electromyography - methods
Electromyography - statistics & numerical data
Female
Hand - physiology
Humans
Linear Models
Male
Markov Chains
Models, Statistical
Movement - physiology
Random Allocation
Reproducibility of Results
Signal Processing, Computer-Assisted
User-Computer Interface
Title Temporal modeling of EEG during self-paced hand movement and its application in onset detection
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https://www.ncbi.nlm.nih.gov/pubmed/21926453
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Volume 8
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