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dc.contributor.authorMohamad Rizon, Mohamed Juhari-
dc.contributor.authorMurugappan, M.-
dc.contributor.authorRamachandran, Nagarajan-
dc.contributor.authorSazali, Yaacob-
dc.date.accessioned2009-12-14T06:48:37Z-
dc.date.available2009-12-14T06:48:37Z-
dc.date.issued2008-
dc.identifier.citationWSEAS Transactions on Signal Processing, vol.4 (10), 2008, pages 596-603.en_US
dc.identifier.issn1790-5052-
dc.identifier.urihttp://www.worldses.org/journals/signal/index.html-
dc.identifier.urihttp://dspace.unimap.edu.my/123456789/7415-
dc.descriptionLink to publisher's homepage at http://www.worldses.orgen_US
dc.description.abstractElectroencephalogram (EEG) is one of the most reliable physiological signals used for detecting the emotional states of human brain. We propose Asymmetric Ratio (AR) based channel selection for human emotion recognition using EEG: Selection of channels reduces the feature size, computational load requirements and robustness of emotions classification. We address this crisis using Asymmetric Variance Ratio (AVR) and Amplitude Asymmetric Ratio (AAR) as new channel selection methods. Using these methods the 28 homogeneous pairs of EEG channels is reduced to 4 and 2 channel pairs respectively. These methods significantly reduce the number of homogeneous pair of channels to be used for emotion detection. This approach is illustrated with 5 distinct emotions (disgust, happy, surprise, sad, and fear) on 63 channels EEG data recorded from 5 healthy subjects. In this study, we used Multi-Resolution Analysis (MRA) based feature extraction the original and reduced set of channels for emotion classification. These approaches were empirically evaluated by using a simple unsupervised classifier, Fuzzy C-Means clustering with variable clusters. The paper concludes by discussing the impact of reduced channels on emotion recognition with larger number of channels and outlining the potential of the new channel selection method.en_US
dc.language.isoenen_US
dc.publisherWorld Scientific abd Engineering Academy and Scoiety (WSEAS)en_US
dc.subjectAsymmetric ratiosen_US
dc.subjectChannel selectionen_US
dc.subjectEEGen_US
dc.subjectFuzzy C-Means (FCM) clusteringen_US
dc.subjectHuman emotionsen_US
dc.subjectWavelet transformen_US
dc.subjectElectroencephalogramen_US
dc.titleAsymmetric ratio and FCM based salient channel selection for human emotion detection using EEGen_US
dc.typeArticleen_US
Appears in Collections:School of Mechatronic Engineering (Articles)
Sazali Yaacob, Prof. Dr.
Ramachandran, Nagarajan, Prof. Dr.
Mohd. Rizon Mohamed Juhari, Prof. Ir. Dr.
M. Murugappan, Dr.

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