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|Title:||ICA Based EEG Energy Spectrum for Detection of Negative Emotion by EEG||Authors:||ZHAN LIANG||Keywords:||EEG, Emotion, ICA, SVM, Energy Spectrum||Issue Date:||24-May-2007||Citation:||ZHAN LIANG (2007-05-24). ICA Based EEG Energy Spectrum for Detection of Negative Emotion by EEG. ScholarBank@NUS Repository.||Abstract:||In recent years, there are increasing interests in emotion-measurement technologies with the widespread hope that they will be invaluable in the safety, medical and criminal investigation. In the literature, various efforts have been put in the emotion measurement methods, including facial recognition, voice recognition, and electrophysiological based measurements. Among them, Electroencephalogram (EEG) might be one of the most predictive and reliable physiological indicators of emotion. However, most previously published research findings on EEG changes in relationship to emotion have found varying, even conflicting results, which could be due to methodological limitation. It needs further research before we can eventually come out with an EEG-based emotion monitor.For detection of anxiety emotion by EEG measurement, an Independent Component Analysis (ICA) based energy spectrum feature is presented. In this study, EEG measurements on human subjects with and without anxiety emotion were conducted, the measured data was decomposed using ICA into a number of independent components, and all the independent components were loaded on an energy mapping system that shows the locations of the independent components on the scalp. By counting the number of independent components fall into both sides of the anterior temporal, clear correlation between the number of independent components on both sides of the anterior temporal and the status of anxiety emotion was observed. The results from all the subjects tested showed that in both sides of the anterior temporal, the number of independent components for anxiety status was 50% to 100% higher than that of emotion void status. The ability of this ICA-based method was verified by SVM prediction accuracy. Prediction accuracy shows that there is a high probability to develop subject-specific negative emotion monitoring system.||URI:||http://scholarbank.nus.edu.sg/handle/10635/16202|
|Appears in Collections:||Master's Theses (Open)|
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