Research Journal of Applied Sciences Engineering and Technology · 2013 · 15 citations · 33 references
EngineeringBioengineeringNeuroscienceBiomedical Signal ProcessingMachine LearningPsychophysiologyAffective ComputingElectroencephalogram AnalysisElectroencephalogram Signal ProcessingElectroencephalographyEmotion ProcessingEmotion RecognitionEeg Feature ExtractionElectroencephalogram ClassificationElectroencephalography Feature ExtractionEmotionsPhysiological Signal AnalysisStatistical Feature DesignStatistical Feature EngineeringStatistical Feature Extraction
This study proposes a statistical features-based classification system for human emotions by using Electroencephalogram (EEG) bio-sensors. A total of six statistical features are computed from the EEG data and Artificial Neural Network is applied for the classification of emotions. The system is trained and tested with the statistical features extracted from the psychological signals acquired under emotions stimulation experiments. The effectiveness of each statistical feature and combinations of statistical features in classifying different types of emotions has been studied and evaluated. In the experiment of classifying four main types of emotions: Anger, Sad, Happy and Neutral, the overall classification rate as high as 90% is achieved.
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Nearest neighbor pattern classification
Thomas M. Cover, Peter E. Hart · IEEE Transactions on Information Theory · 1967 · 16.1K citations