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AuthorShams, Wafaa Khazaal
AuthorWahab, Abdul
AuthorQidwai, Uvais A.
Available date2024-05-07T05:39:59Z
Publication Date2012
Publication NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ResourceScopus
Identifierhttp://dx.doi.org/10.1007/978-3-642-34478-7_47
ISSN3029743
URIhttp://hdl.handle.net/10576/54695
AbstractThis study proposes a new type of features extracted from Electroencephalography (EEG) signals to distinguish between different tasks. EEG signals are collected from six children aged between two to six years old during opened and closed eyes tasks. For each time-sample, Time Difference of Arrival (TDOA) is applied to EEG time series to compute the source-temporal- features that are assigned to x, y and z coordinates. The features are classified using neural network. The results show an accuracy of around 100% for eyes open task and around (83%-95%) for eyes closed tasks for the same subject. This study highlights the use of new types of features (source-temporal features), to characterize the brain functional behavior.
Languageen
PublisherSpringer Nature
SubjectClassification
EEG signals
Source-Temporal features
TDOA approach
TitleDetecting different tasks using EEG-source-temporal features
TypeConference Paper
Pagination380-387
Issue NumberPART 4
Volume Number7666 LNCS
dc.accessType Abstract Only


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