Characterization of EEG frontal energy ratio and KNN with entrained binaural beats for mental states classification / Haryanti Norhazman

A binaural beat is a form of auditory stimulation on which two pure tones with slightly different frequencies are generated simultaneously in the left and right ear. The frequency difference between the two tones is the beat perceived by the brain. A binaural beat is believed to be able to entrain o...

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Main Author:	Norhazman, Haryanti
Format:	Thesis
Language:	English
Published:	2020
Subjects:	Nervous system Brain
Online Access:	https://ir.uitm.edu.my/id/eprint/32451/1/32451.pdf
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id	my-uitm-ir.32451
record_format	uketd_dc
spelling	my-uitm-ir.324512022-11-07T08:21:05Z Characterization of EEG frontal energy ratio and KNN with entrained binaural beats for mental states classification / Haryanti Norhazman 2020 Norhazman, Haryanti Nervous system Brain A binaural beat is a form of auditory stimulation on which two pure tones with slightly different frequencies are generated simultaneously in the left and right ear. The frequency difference between the two tones is the beat perceived by the brain. A binaural beat is believed to be able to entrain our cognitive and neural activities to synchronize with the perceived tone. Most research has been done only on the effects of the binaural-beats during stimulation. This research is carried out to explore the potential contributions of binaural beats to the entrained frontal Energy Spectrum Density (ESD) of alpha and beta sub waves for stressed subjects. The protocols involved 40 volunteers answering Depression Anxiety Stress Scale (DASS) questionnaires, after which they were segregated into two groups, namely Calm and Stress groups. Their EEG signals were recorded during three different sessions, namely "Resting", "Listen to Noise" and "After Binaural Beats". The frequency of the Binaural Beats used in this study was 10 Hz, which was intended to induce relaxation to the subjects. The EEG signals were pre-processed using Fast Fourier Transform to derive the ESD. The behaviors of the raw ESD for all sub waves was observed before the Alpha Beta Frontal Energy Ratio (ABFER) was calculated from the raw ESD. The ABFER feature was used to observe the pattern of alpha and beta sub waves in left and right hemisphere in both groups, namely Calm and Stress subjects, during the three mentioned sessions. The features selected were used as input in k-Nearest Neighbor (k-NN) classification, and the performance measures were used to determine whether the selected features were able to distinguish between Calm and Stress subjects in the three different sessions. The results showed the k-NN model managed to rule out the Stress subjects with a high accuracy of 87.5% during all sessions except the "Listen to Noise" session. The classification results were then validated using K-Fold Cross-Validation, with the highest accuracy of 90% obtained during "After Binaural Beats" session. In summary, the findings have shown that ABFER feature has the potential to be a marker to identify stress subjects, while the k-NN technique managed to classify calm and stressed subjects based on this feature. The novelty of this research is the analysis on the behaviors of the frontal alpha and beta ESD during noise stimulation and post binaural beats of Stress subjects. 2020 Thesis https://ir.uitm.edu.my/id/eprint/32451/ https://ir.uitm.edu.my/id/eprint/32451/1/32451.pdf text en public phd doctoral Universiti Teknologi MARA (UiTM) Faculty of Electrical Engineering Mohammad Zaini, Norliza Taib, Mohd Nasir Jailani, Rozita Omar, Hasmila Akmar
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Mohammad Zaini, Norliza Taib, Mohd Nasir Jailani, Rozita Omar, Hasmila Akmar
topic	Nervous system Brain
spellingShingle	Nervous system Brain Norhazman, Haryanti Characterization of EEG frontal energy ratio and KNN with entrained binaural beats for mental states classification / Haryanti Norhazman
description	A binaural beat is a form of auditory stimulation on which two pure tones with slightly different frequencies are generated simultaneously in the left and right ear. The frequency difference between the two tones is the beat perceived by the brain. A binaural beat is believed to be able to entrain our cognitive and neural activities to synchronize with the perceived tone. Most research has been done only on the effects of the binaural-beats during stimulation. This research is carried out to explore the potential contributions of binaural beats to the entrained frontal Energy Spectrum Density (ESD) of alpha and beta sub waves for stressed subjects. The protocols involved 40 volunteers answering Depression Anxiety Stress Scale (DASS) questionnaires, after which they were segregated into two groups, namely Calm and Stress groups. Their EEG signals were recorded during three different sessions, namely "Resting", "Listen to Noise" and "After Binaural Beats". The frequency of the Binaural Beats used in this study was 10 Hz, which was intended to induce relaxation to the subjects. The EEG signals were pre-processed using Fast Fourier Transform to derive the ESD. The behaviors of the raw ESD for all sub waves was observed before the Alpha Beta Frontal Energy Ratio (ABFER) was calculated from the raw ESD. The ABFER feature was used to observe the pattern of alpha and beta sub waves in left and right hemisphere in both groups, namely Calm and Stress subjects, during the three mentioned sessions. The features selected were used as input in k-Nearest Neighbor (k-NN) classification, and the performance measures were used to determine whether the selected features were able to distinguish between Calm and Stress subjects in the three different sessions. The results showed the k-NN model managed to rule out the Stress subjects with a high accuracy of 87.5% during all sessions except the "Listen to Noise" session. The classification results were then validated using K-Fold Cross-Validation, with the highest accuracy of 90% obtained during "After Binaural Beats" session. In summary, the findings have shown that ABFER feature has the potential to be a marker to identify stress subjects, while the k-NN technique managed to classify calm and stressed subjects based on this feature. The novelty of this research is the analysis on the behaviors of the frontal alpha and beta ESD during noise stimulation and post binaural beats of Stress subjects.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Norhazman, Haryanti
author_facet	Norhazman, Haryanti
author_sort	Norhazman, Haryanti
title	Characterization of EEG frontal energy ratio and KNN with entrained binaural beats for mental states classification / Haryanti Norhazman
title_short	Characterization of EEG frontal energy ratio and KNN with entrained binaural beats for mental states classification / Haryanti Norhazman
title_full	Characterization of EEG frontal energy ratio and KNN with entrained binaural beats for mental states classification / Haryanti Norhazman
title_fullStr	Characterization of EEG frontal energy ratio and KNN with entrained binaural beats for mental states classification / Haryanti Norhazman
title_full_unstemmed	Characterization of EEG frontal energy ratio and KNN with entrained binaural beats for mental states classification / Haryanti Norhazman
title_sort	characterization of eeg frontal energy ratio and knn with entrained binaural beats for mental states classification / haryanti norhazman
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Electrical Engineering
publishDate	2020
url	https://ir.uitm.edu.my/id/eprint/32451/1/32451.pdf
_version_	1783734164135084032

Characterization of EEG frontal energy ratio and KNN with entrained binaural beats for mental states classification / Haryanti Norhazman

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