Real-Time EEG-Based Happiness Detection System

Real-Time EEG-Based Happiness Detection System

We propose to use real-time EEG signal to classify happy and unhappy emotions elicited by pictures and classical music. We use PSD as a feature and SVM as a classifier. The average accuracies of subject-dependent model and subject-independent model are approximately 75.62% and 65.12%, respectively....

Full description

Bibliographic Details
Main Authors: Noppadon Jatupaiboon, Setha Pan-ngum, Pasin Israsena
Format: Article
Language:English
Published: Hindawi Limited 2013-01-01
Series:The Scientific World Journal
Online Access:http://dx.doi.org/10.1155/2013/618649
id doaj-7cff03962f064e859e1ddc15d1943fd7
record_format Article
spelling doaj-7cff03962f064e859e1ddc15d1943fd72020-11-25T02:19:11ZengHindawi LimitedThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/618649618649Real-Time EEG-Based Happiness Detection SystemNoppadon Jatupaiboon0Setha Pan-ngum1Pasin Israsena2Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, ThailandDepartment of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, ThailandNational Electronics and Computer Technology Center, Pathumthani 12120, ThailandWe propose to use real-time EEG signal to classify happy and unhappy emotions elicited by pictures and classical music. We use PSD as a feature and SVM as a classifier. The average accuracies of subject-dependent model and subject-independent model are approximately 75.62% and 65.12%, respectively. Considering each pair of channels, temporal pair of channels (T7 and T8) gives a better result than the other area. Considering different frequency bands, high-frequency bands (Beta and Gamma) give a better result than low-frequency bands. Considering different time durations for emotion elicitation, that result from 30 seconds does not have significant difference compared with the result from 60 seconds. From all of these results, we implement real-time EEG-based happiness detection system using only one pair of channels. Furthermore, we develop games based on the happiness detection system to help user recognize and control the happiness.http://dx.doi.org/10.1155/2013/618649
collection DOAJ
language English
format Article
sources DOAJ
author Noppadon Jatupaiboon
Setha Pan-ngum
Pasin Israsena
spellingShingle Noppadon Jatupaiboon
Setha Pan-ngum
Pasin Israsena
Real-Time EEG-Based Happiness Detection System
The Scientific World Journal
author_facet Noppadon Jatupaiboon
Setha Pan-ngum
Pasin Israsena
author_sort Noppadon Jatupaiboon
title Real-Time EEG-Based Happiness Detection System
title_short Real-Time EEG-Based Happiness Detection System
title_full Real-Time EEG-Based Happiness Detection System
title_fullStr Real-Time EEG-Based Happiness Detection System
title_full_unstemmed Real-Time EEG-Based Happiness Detection System
title_sort real-time eeg-based happiness detection system
publisher Hindawi Limited
series The Scientific World Journal
issn 1537-744X
publishDate 2013-01-01
description We propose to use real-time EEG signal to classify happy and unhappy emotions elicited by pictures and classical music. We use PSD as a feature and SVM as a classifier. The average accuracies of subject-dependent model and subject-independent model are approximately 75.62% and 65.12%, respectively. Considering each pair of channels, temporal pair of channels (T7 and T8) gives a better result than the other area. Considering different frequency bands, high-frequency bands (Beta and Gamma) give a better result than low-frequency bands. Considering different time durations for emotion elicitation, that result from 30 seconds does not have significant difference compared with the result from 60 seconds. From all of these results, we implement real-time EEG-based happiness detection system using only one pair of channels. Furthermore, we develop games based on the happiness detection system to help user recognize and control the happiness.
url http://dx.doi.org/10.1155/2013/618649
work_keys_str_mv AT noppadonjatupaiboon realtimeeegbasedhappinessdetectionsystem
AT sethapanngum realtimeeegbasedhappinessdetectionsystem
AT pasinisrasena realtimeeegbasedhappinessdetectionsystem
_version_ 1724877876553056256

Similar Items