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

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 fo...

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Main Authors:	Khosrowabadi, Reza, Quek, Chai, Ang, Kai Keng, Abdul Rahman, Abdul Wahab
Format:	Article
Language:	English English
Published:	IEEE Computational Intelligence Society 2014
Subjects:	QA75 Electronic computers. Computer science TL500 Aeronautics
Online Access:	http://irep.iium.edu.my/36713/ http://irep.iium.edu.my/36713/ http://irep.iium.edu.my/36713/1/36713.pdf http://irep.iium.edu.my/36713/3/36713_A%20biologically%20inspired%20feedforward%20neural_Scopus.pdf

id	iium-36713
recordtype	eprints
spelling	iium-367132018-06-20T06:24:16Z http://irep.iium.edu.my/36713/ ERNN: A biologically inspired feedforward neural network to discriminate emotion from EEG signal Khosrowabadi, Reza Quek, Chai Ang, Kai Keng Abdul Rahman, Abdul Wahab QA75 Electronic computers. Computer science TL500 Aeronautics 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. IEEE Computational Intelligence Society 2014-03 Article PeerReviewed application/pdf en http://irep.iium.edu.my/36713/1/36713.pdf application/pdf en http://irep.iium.edu.my/36713/3/36713_A%20biologically%20inspired%20feedforward%20neural_Scopus.pdf 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 10.1109/TNNLS.2013.2280271
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	QA75 Electronic computers. Computer science TL500 Aeronautics
spellingShingle	QA75 Electronic computers. Computer science TL500 Aeronautics Khosrowabadi, Reza Quek, Chai Ang, Kai Keng Abdul Rahman, Abdul Wahab ERNN: A biologically inspired feedforward neural network to discriminate emotion from EEG signal
description	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.
format	Article
author	Khosrowabadi, Reza Quek, Chai Ang, Kai Keng Abdul Rahman, Abdul Wahab
author_facet	Khosrowabadi, Reza Quek, Chai Ang, Kai Keng Abdul Rahman, Abdul Wahab
author_sort	Khosrowabadi, Reza
title	ERNN: A biologically inspired feedforward neural network to discriminate emotion from EEG signal
title_short	ERNN: A biologically inspired feedforward neural network to discriminate emotion from EEG signal
title_full	ERNN: A biologically inspired feedforward neural network to discriminate emotion from EEG signal
title_fullStr	ERNN: A biologically inspired feedforward neural network to discriminate emotion from EEG signal
title_full_unstemmed	ERNN: A biologically inspired feedforward neural network to discriminate emotion from EEG signal
title_sort	ernn: a biologically inspired feedforward neural network to discriminate emotion from eeg signal
publisher	IEEE Computational Intelligence Society
publishDate	2014
url	http://irep.iium.edu.my/36713/ http://irep.iium.edu.my/36713/ http://irep.iium.edu.my/36713/1/36713.pdf http://irep.iium.edu.my/36713/3/36713_A%20biologically%20inspired%20feedforward%20neural_Scopus.pdf
first_indexed	2023-09-18T20:52:37Z
last_indexed	2023-09-18T20:52:37Z
_version_	1777410095503638528

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

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