Detecting different tasks using EEG-source-temporal features
| Author | Shams, Wafaa Khazaal |
| Author | Wahab, Abdul |
| Author | Qidwai, Uvais A. |
| Available date | 2024-05-07T05:39:59Z |
| Publication Date | 2012 |
| Publication Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Resource | Scopus |
| Identifier | http://dx.doi.org/10.1007/978-3-642-34478-7_47 |
| ISSN | 3029743 |
| Abstract | This 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. |
| Language | en |
| Publisher | Springer Nature |
| Subject | Classification EEG signals Source-Temporal features TDOA approach |
| Type | Conference Paper |
| Pagination | 380-387 |
| Issue Number | PART 4 |
| Volume Number | 7666 LNCS |
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