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• W2188000735 abstract "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." @default.
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• W2188000735 date "2013-05-20" @default.
• W2188000735 modified "2023-10-18" @default.
• W2188000735 title "Effectiveness of Statistical Features for Human Emotions Classification using EEG Biosensors" @default.
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• W2188000735 doi "https://doi.org/10.19026/rjaset.5.4401" @default.
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