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Wavelet analysis for EEG feature extraction in deception detection
Please use this identifier to cite or link to this item:
http://hdl.handle.net/1860/1646
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| Title: | Wavelet analysis for EEG feature extraction in deception detection |
| Authors: | Merzagora, Anna Caterina
Bunce, Scott
Izzetoglu, Meltem
Onaral, Banu |
| Issue Date: | 2006 |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
| Citation: | Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06: pp. 2434-2437. |
| Abstract: | Deception detection has important clinical and
legal implications. However, the reliability of methods for the
discrimination between truthful and deceptive responses is still
limited. Efforts to improve reliability have examined measures
of central nervous system function such as EEG. However,
EEG analyses based on either time- or frequency-domain
parameters have had mixed results. Because EEG is a
nonstationary signal, the use of joint time-frequency features
may yield more reliable results for detecting deception. The
goal of this study was to investigate the feasibility of deception
detection based on EEG features extracted through wavelet
transformation. EEG was recorded from 4 electrode sites (F3,
F4, F7, F8) during a modified version of the Guilty Knowledge
Test (GKT) in 5 subjects. Wavelet analysis revealed significant
differences between deceptive and truthful responses. These
differences were detected in features whose frequency range
roughly corresponds to the EEG beta rhythm and within a time
window which coincides with the P300 component. These
preliminary results indicate that joint time-frequency EEG
features extracted through wavelet analysis may provide a
more reliable method for detecting deception than standard
ERPs. |
| URI: | http://dx.doi.org/10.1109/IEMBS.2006.260247
http://hdl.handle.net/1860/1646 |
| Appears in Collections: | Faculty Research and Publications (Biomed Eng)
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