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ERNN: A biologically inspired feedforward neural network to discriminate emotion from EEG signal

Khosrowabadi, Reza and Quek, Chai and Ang, Kai Keng and Abdul Rahman, Abdul Wahab (2014) ERNN: A biologically inspired feedforward neural network to discriminate emotion from EEG signal. IEEE Transactions on Neural Networks and Learning Systems, 25 (3). pp. 609-620. ISSN 2162-237X

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Abstract

Emotions play an important role in human cognition, perception, decision making, and interaction. This paper presents a six-layer biologically inspired feedforward neural network to discriminate human emotions from EEG. The neural network comprises a shift register memory after spectral filtering for the input layer, and the estimation of coherence between each pair of input signals for the hidden layer. EEG data are collected from 57 healthy participants from eight locations while subjected to audio-visual stimuli. Discrimination of emotions from EEG is investigated based on valence and arousal levels. The accuracy of the proposed neural network is compared with various feature extraction methods and feedforward learning algorithms. The results showed that the highest accuracy is achieved when using the proposed neural network with a type of radial basis function.

Item Type: Article (Journal)
Additional Information: 6145/36713
Uncontrolled Keywords: Affective computing; arousal-valence plane; EEG-based emotion recognition; functional connectivity
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL500 Aeronautics
Kulliyyahs/Centres/Divisions/Institutes: Kulliyyah of Information and Communication Technology > Department of Computer Science
Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User: Prof Abdul Wahab Abdul Rahman
Date Deposited: 27 May 2014 14:44
Last Modified: 20 Jun 2018 14:24
URI: http://irep.iium.edu.my/id/eprint/36713

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