Development of a 3D EEG feature extraction for brain balanced index (BBI) using artificial neural network (ANN) / Norfaiza Fuad

The thesis presents the development of a new three-dimensional (3D) EEG feature extraction for brain balanced index (BBI) using artificial neural network (ANN). There were five (5) indexes stated for BBI, index 1 (unbalanced condition), index 2 (less balanced), index 3 (moderately balanced), index 4...

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Bibliographic Details
Main Author:	Fuad, Norfaiza
Format:	Book Section
Language:	English
Published:	Institute of Graduate Studies, UiTM 2017
Subjects:	Malaysia
Online Access:	http://ir.uitm.edu.my/id/eprint/19922/ http://ir.uitm.edu.my/id/eprint/19922/1/ABS_NORFAIZA%20FUAD%20TDRA%20VOL%2012%20IGS%2017.pdf

id	uitm-19922
recordtype	eprints
spelling	uitm-199222018-06-11T02:30:22Z http://ir.uitm.edu.my/id/eprint/19922/ Development of a 3D EEG feature extraction for brain balanced index (BBI) using artificial neural network (ANN) / Norfaiza Fuad Fuad, Norfaiza Malaysia The thesis presents the development of a new three-dimensional (3D) EEG feature extraction for brain balanced index (BBI) using artificial neural network (ANN). There were five (5) indexes stated for BBI, index 1 (unbalanced condition), index 2 (less balanced), index 3 (moderately balanced), index 4 (balanced) and index 5 (highly balanced). There are four (4) sub-bands of frequency for EEG signals; δ band (0.2- 3 Hz), θ band (3- 8 Hz), α band (8-12 Hz) and β band (12-30 Hz). These sub-bands can be used to analyze human brain activities. This research involved 96 healthy subjects for EEG data collection. The EEG 3D signals are produced through signal processing and image processing techniques. The development of 3D involved preprocessing of raw EEG signals and construction of 2D EEG images or spectrograms. EEG signals are pre-processed using artifact removal and band pass filter technique. The resultant images for 2D EEG image are constructed via Short Time Fourier Transform (STFT). Power spectral density (PSD) values are extracted as features. Some techniques for data analysis like Shapiro-Wilk for data distribution analysis and Pearson correlation for data correlation analysis have been implemented. These features are analyzed to signify the pattern for brain balanced index. Institute of Graduate Studies, UiTM 2017 Book Section PeerReviewed text en http://ir.uitm.edu.my/id/eprint/19922/1/ABS_NORFAIZA%20FUAD%20TDRA%20VOL%2012%20IGS%2017.pdf Fuad, Norfaiza (2017) Development of a 3D EEG feature extraction for brain balanced index (BBI) using artificial neural network (ANN) / Norfaiza Fuad. In: The Doctoral Research Abstracts. IGS Biannual Publication, 12 (12). Institute of Graduate Studies, UiTM, Shah Alam.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
topic	Malaysia
spellingShingle	Malaysia Fuad, Norfaiza Development of a 3D EEG feature extraction for brain balanced index (BBI) using artificial neural network (ANN) / Norfaiza Fuad
description	The thesis presents the development of a new three-dimensional (3D) EEG feature extraction for brain balanced index (BBI) using artificial neural network (ANN). There were five (5) indexes stated for BBI, index 1 (unbalanced condition), index 2 (less balanced), index 3 (moderately balanced), index 4 (balanced) and index 5 (highly balanced). There are four (4) sub-bands of frequency for EEG signals; δ band (0.2- 3 Hz), θ band (3- 8 Hz), α band (8-12 Hz) and β band (12-30 Hz). These sub-bands can be used to analyze human brain activities. This research involved 96 healthy subjects for EEG data collection. The EEG 3D signals are produced through signal processing and image processing techniques. The development of 3D involved preprocessing of raw EEG signals and construction of 2D EEG images or spectrograms. EEG signals are pre-processed using artifact removal and band pass filter technique. The resultant images for 2D EEG image are constructed via Short Time Fourier Transform (STFT). Power spectral density (PSD) values are extracted as features. Some techniques for data analysis like Shapiro-Wilk for data distribution analysis and Pearson correlation for data correlation analysis have been implemented. These features are analyzed to signify the pattern for brain balanced index.
format	Book Section
author	Fuad, Norfaiza
author_facet	Fuad, Norfaiza
author_sort	Fuad, Norfaiza
title	Development of a 3D EEG feature extraction for brain balanced index (BBI) using artificial neural network (ANN) / Norfaiza Fuad
title_short	Development of a 3D EEG feature extraction for brain balanced index (BBI) using artificial neural network (ANN) / Norfaiza Fuad
title_full	Development of a 3D EEG feature extraction for brain balanced index (BBI) using artificial neural network (ANN) / Norfaiza Fuad
title_fullStr	Development of a 3D EEG feature extraction for brain balanced index (BBI) using artificial neural network (ANN) / Norfaiza Fuad
title_full_unstemmed	Development of a 3D EEG feature extraction for brain balanced index (BBI) using artificial neural network (ANN) / Norfaiza Fuad
title_sort	development of a 3d eeg feature extraction for brain balanced index (bbi) using artificial neural network (ann) / norfaiza fuad
publisher	Institute of Graduate Studies, UiTM
publishDate	2017
url	http://ir.uitm.edu.my/id/eprint/19922/ http://ir.uitm.edu.my/id/eprint/19922/1/ABS_NORFAIZA%20FUAD%20TDRA%20VOL%2012%20IGS%2017.pdf
first_indexed	2023-09-18T23:03:35Z
last_indexed	2023-09-18T23:03:35Z
_version_	1777418334629789696

Development of a 3D EEG feature extraction for brain balanced index (BBI) using artificial neural network (ANN) / Norfaiza Fuad

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