Neural Network Approach for Classification of Human Emotions from EEG Signal

Kumar, Shashi G S and Niranjana, S and Shetty, Harikishan (2018) Neural Network Approach for Classification of Human Emotions from EEG Signal. In: Ist International Conference on Engineering Vibration, Communiction and Information Processing, 09/03/2018, Manipal University Jaipur.

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Emotionsplayanimportantroleinhumancognition,perception,decisionmaking,andinteraction.Inthispaper,NeuralNetwork(NN)basedsystemforhuman emotions classification by extracting features from Electroencephalogram (EEG) signal is proposed. EEG data for the classification of emotions is obtained from the DEAP database. Extracted more than 30 features from EEG and they are used for the emotion classification. Totally, 33 varieties of features are extracted from EEG data. However, there are reports on voice-based, facial-image-based study of expressionstorecognizetheiremotions.However,emotionidentificationusingboth methodscanbebiasedastheycanbefaked.Inordertoovercomethisdifficulty,many researchers analyze brain physiological signals to represent the changing patterns during emotional fluctuations. Neural networks have widely been used in emotion classification. Reported here is the classification with the backpropagation artificial neuralnetwork.Experimentalresultshaveshownanaverageaccuracyabove94.45% is achieved for all the subjects and regions combined

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Emotion, EEG, Neural network, Backpropagation,Classification
Subjects: Engineering > MIT Manipal > Biomedical
Engineering > MIT Manipal > Electronics and Communication
Depositing User: MIT Library
Date Deposited: 03 Jul 2019 04:01
Last Modified: 03 Jul 2019 04:01

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