Emotion recognition by exploiting temporal resolution of EEG signals using transformation and learning methods

Cataloged from PDF version of thesis.

書誌詳細
主要な著者:	Uddin, Md.Tousif, Hossain, Mohammed Imaad, Khan, Shakik, Hoque, Tajwar-ul, Ahsan, Md.Jabid
その他の著者:	Parvez, Mohammad Zavid
フォーマット:	学位論文
言語:	en_US
出版事項:	Brac University 2021
主題:	Electroencephalogram (EEG) Fast Fourier transform (FFT) Power Spectral Density (PWD) Discrete Wavelet Transformation (DWT)
オンライン･アクセス:	http://hdl.handle.net/10361/15216

id	10361-15216
record_format	dspace
spelling	10361-152162022-01-26T10:15:56Z Emotion recognition by exploiting temporal resolution of EEG signals using transformation and learning methods Uddin, Md.Tousif Hossain, Mohammed Imaad Khan, Shakik Hoque, Tajwar-ul Ahsan, Md.Jabid Parvez, Mohammad Zavid Mannafee, Istiaque Department of Computer Science and Engineering, Brac University Electroencephalogram (EEG) Fast Fourier transform (FFT) Power Spectral Density (PWD) Discrete Wavelet Transformation (DWT) Cataloged from PDF version of thesis. Includes bibliographical references (pages 39-46). This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020. Individuals are free to articulate thousands of emotions. Emotion can be associated with feelings articulated by or observable by voice intonation, a facial expression of body language, an initial response from one’s mood relationship with others and most strikingly, a predicament within thus they are. Recognizing sentiments is a daunting task due in part to the non-linear features of the EEG signal. This paper addresses advanced pre-processed DEAP dataset EEG signals for emotional recognition. The valence and arousal components of the raw EEG signal are ﬁrst retained in the PSD approach Fast Fourier transform (FFT). Power Spectral Density (PWD) consisting four features are being selected for all 32 participants. Features derived from the PSD are considerably less accurate precise. Through implementing 10-fold cross validation on the DWT (discrete wavelet transformation) to get the time-based features, Gradient boosting Classiﬁer gave the best result among six diﬀerent classiﬁers. Our proposed method provides 93.12% accuracy by using a benchmark dataset. The results of experiments on DEAP datasets indicate that our system. Md.Tousif Uddin Mohammed Imaad Hossain Shakik Khan Tajwar-ul Hoque Md.Jabid Ahsan B. Computer Science 2021-10-11T09:09:31Z 2021-10-11T09:09:31Z 2020 2020-10 Thesis ID: 13201032 ID: 16301215 ID: 16301219 ID: 18201210 ID: 16301193 http://hdl.handle.net/10361/15216 en_US Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 46 Pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	en_US
topic	Electroencephalogram (EEG) Fast Fourier transform (FFT) Power Spectral Density (PWD) Discrete Wavelet Transformation (DWT)
spellingShingle	Electroencephalogram (EEG) Fast Fourier transform (FFT) Power Spectral Density (PWD) Discrete Wavelet Transformation (DWT) Uddin, Md.Tousif Hossain, Mohammed Imaad Khan, Shakik Hoque, Tajwar-ul Ahsan, Md.Jabid Emotion recognition by exploiting temporal resolution of EEG signals using transformation and learning methods
description	Cataloged from PDF version of thesis.
author2	Parvez, Mohammad Zavid
author_facet	Parvez, Mohammad Zavid Uddin, Md.Tousif Hossain, Mohammed Imaad Khan, Shakik Hoque, Tajwar-ul Ahsan, Md.Jabid
format	Thesis
author	Uddin, Md.Tousif Hossain, Mohammed Imaad Khan, Shakik Hoque, Tajwar-ul Ahsan, Md.Jabid
author_sort	Uddin, Md.Tousif
title	Emotion recognition by exploiting temporal resolution of EEG signals using transformation and learning methods
title_short	Emotion recognition by exploiting temporal resolution of EEG signals using transformation and learning methods
title_full	Emotion recognition by exploiting temporal resolution of EEG signals using transformation and learning methods
title_fullStr	Emotion recognition by exploiting temporal resolution of EEG signals using transformation and learning methods
title_full_unstemmed	Emotion recognition by exploiting temporal resolution of EEG signals using transformation and learning methods
title_sort	emotion recognition by exploiting temporal resolution of eeg signals using transformation and learning methods
publisher	Brac University
publishDate	2021
url	http://hdl.handle.net/10361/15216
work_keys_str_mv	AT uddinmdtousif emotionrecognitionbyexploitingtemporalresolutionofeegsignalsusingtransformationandlearningmethods AT hossainmohammedimaad emotionrecognitionbyexploitingtemporalresolutionofeegsignalsusingtransformationandlearningmethods AT khanshakik emotionrecognitionbyexploitingtemporalresolutionofeegsignalsusingtransformationandlearningmethods AT hoquetajwarul emotionrecognitionbyexploitingtemporalresolutionofeegsignalsusingtransformationandlearningmethods AT ahsanmdjabid emotionrecognitionbyexploitingtemporalresolutionofeegsignalsusingtransformationandlearningmethods
_version_	1814308550931906560

Emotion recognition by exploiting temporal resolution of EEG signals using transformation and learning methods

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