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EEG-based emotion recognition using self-organizing map for boundary detection

This paper presents an EEG-based emotion recognition system using self-organizing map for boundary detection. Features from EEG signals are classified by considering the subjects’ emotional responses using scores from SAM questionnaire. The selection of appropriate threshold levels for arousal and...

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Main Authors: Khosrowabadi, Reza, Quek, Hiok Chai, Abdul Rahman, Abdul Wahab, Ang, Kai Keng
Format: Conference or Workshop Item
Language:English
Published: 2010
Subjects:
T Technology (General)
Online Access:http://irep.iium.edu.my/38090/1/EEG-based_emotion_recognition_using_self-organizing_map_for_boundary_detection.pdf
http://irep.iium.edu.my/38090/
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5597763
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spelling my.iium.irep.380902020-12-16T16:07:12Z http://irep.iium.edu.my/38090/ EEG-based emotion recognition using self-organizing map for boundary detection Khosrowabadi, Reza Quek, Hiok Chai Abdul Rahman, Abdul Wahab Ang, Kai Keng T Technology (General) This paper presents an EEG-based emotion recognition system using self-organizing map for boundary detection. Features from EEG signals are classified by considering the subjects’ emotional responses using scores from SAM questionnaire. The selection of appropriate threshold levels for arousal and valence is critical to the performance of the recognition system. Therefore, this paper investigates the performance of a proposed EEG-based emotion recognition system that employed self-organizing map to identify the boundaries between separable regions. A study was performed to collect 8 channels of EEG data from 26 healthy right-handed subjects in experiencing 4 emotional states while exposed to audio-visual emotional stimuli. EEG features were extracted using the magnitude squared coherence of the EEG signals. The boundaries of the EEG features were then extracted using SOM. 5-fold crossvalidation was then performed using the k-nn classifier. The results showed that proposed method improved the accuracies to 84.5%. 2010-08 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/38090/1/EEG-based_emotion_recognition_using_self-organizing_map_for_boundary_detection.pdf Khosrowabadi, Reza and Quek, Hiok Chai and Abdul Rahman, Abdul Wahab and Ang, Kai Keng (2010) EEG-based emotion recognition using self-organizing map for boundary detection. In: 2010 20th International Conference on Pattern Recognition (ICPR 2010), 23-26 Aug. 2010, Istanbul, Turkey. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5597763
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Khosrowabadi, Reza
Quek, Hiok Chai
Abdul Rahman, Abdul Wahab
Ang, Kai Keng
EEG-based emotion recognition using self-organizing map for boundary detection
description This paper presents an EEG-based emotion recognition system using self-organizing map for boundary detection. Features from EEG signals are classified by considering the subjects’ emotional responses using scores from SAM questionnaire. The selection of appropriate threshold levels for arousal and valence is critical to the performance of the recognition system. Therefore, this paper investigates the performance of a proposed EEG-based emotion recognition system that employed self-organizing map to identify the boundaries between separable regions. A study was performed to collect 8 channels of EEG data from 26 healthy right-handed subjects in experiencing 4 emotional states while exposed to audio-visual emotional stimuli. EEG features were extracted using the magnitude squared coherence of the EEG signals. The boundaries of the EEG features were then extracted using SOM. 5-fold crossvalidation was then performed using the k-nn classifier. The results showed that proposed method improved the accuracies to 84.5%.
format Conference or Workshop Item
author Khosrowabadi, Reza
Quek, Hiok Chai
Abdul Rahman, Abdul Wahab
Ang, Kai Keng
author_facet Khosrowabadi, Reza
Quek, Hiok Chai
Abdul Rahman, Abdul Wahab
Ang, Kai Keng
author_sort Khosrowabadi, Reza
title EEG-based emotion recognition using self-organizing map for boundary detection
title_short EEG-based emotion recognition using self-organizing map for boundary detection
title_full EEG-based emotion recognition using self-organizing map for boundary detection
title_fullStr EEG-based emotion recognition using self-organizing map for boundary detection
title_full_unstemmed EEG-based emotion recognition using self-organizing map for boundary detection
title_sort eeg-based emotion recognition using self-organizing map for boundary detection
publishDate 2010
url http://irep.iium.edu.my/38090/1/EEG-based_emotion_recognition_using_self-organizing_map_for_boundary_detection.pdf
http://irep.iium.edu.my/38090/
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5597763
_version_ 1687393050520715264
score 13.211869

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