EEG based sensing of brain signals : emotional states feature extraction

Emotions play a significant role in human behaviour, decision making and actions. They direct attention and enhance the memory in encoding and storing of information. The human brain practically runs every function in the body. Brain signals generated by visual stimuli are collected by EEG system...

Full description

Saved in:
Bibliographic Details
Main Author: Per, Sau Wei.
Other Authors: Ser Wee
Format: Final Year Project
Language:English
Published: 2011
Subjects:
Online Access:http://hdl.handle.net/10356/45624
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-45624
record_format dspace
spelling sg-ntu-dr.10356-456242023-07-07T16:22:45Z EEG based sensing of brain signals : emotional states feature extraction Per, Sau Wei. Ser Wee School of Electrical and Electronic Engineering Centre for Signal Processing DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronics Emotions play a significant role in human behaviour, decision making and actions. They direct attention and enhance the memory in encoding and storing of information. The human brain practically runs every function in the body. Brain signals generated by visual stimuli are collected by EEG system. The time-domain and frequency-domain features of these signals are then extracted with the use of MATLAB algorithm. Different combinations of features are grouped and classified by Linear Discriminant classification into Happy and Sad emotional states. Experiments were set up to compute and analyse the brain signals related to Happy and Sad states. Placements of electrodes based on the 10-20 system were used to extract both alpha and beta waves from the frontal and parietal lobes. The features, average power and peak power from frequency domain, and minimum amplitude, maximum amplitude, mean and standard deviation from time domain, were extracted using the MATLAB algorithm. Visual inspection of the brain signals was performed to determine the optimal time interval for the features to be used in linear discriminant analysis. Different combinations of features were chosen to obtain the highest classification accuracy. The combination results from alpha wave, average power, peak power, mean and standard deviation, gave the highest accuracy of 64%. Bachelor of Engineering 2011-06-15T08:03:42Z 2011-06-15T08:03:42Z 2011 2011 Final Year Project (FYP) http://hdl.handle.net/10356/45624 en Nanyang Technological University 75 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronics
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronics
Per, Sau Wei.
EEG based sensing of brain signals : emotional states feature extraction
description Emotions play a significant role in human behaviour, decision making and actions. They direct attention and enhance the memory in encoding and storing of information. The human brain practically runs every function in the body. Brain signals generated by visual stimuli are collected by EEG system. The time-domain and frequency-domain features of these signals are then extracted with the use of MATLAB algorithm. Different combinations of features are grouped and classified by Linear Discriminant classification into Happy and Sad emotional states. Experiments were set up to compute and analyse the brain signals related to Happy and Sad states. Placements of electrodes based on the 10-20 system were used to extract both alpha and beta waves from the frontal and parietal lobes. The features, average power and peak power from frequency domain, and minimum amplitude, maximum amplitude, mean and standard deviation from time domain, were extracted using the MATLAB algorithm. Visual inspection of the brain signals was performed to determine the optimal time interval for the features to be used in linear discriminant analysis. Different combinations of features were chosen to obtain the highest classification accuracy. The combination results from alpha wave, average power, peak power, mean and standard deviation, gave the highest accuracy of 64%.
author2 Ser Wee
author_facet Ser Wee
Per, Sau Wei.
format Final Year Project
author Per, Sau Wei.
author_sort Per, Sau Wei.
title EEG based sensing of brain signals : emotional states feature extraction
title_short EEG based sensing of brain signals : emotional states feature extraction
title_full EEG based sensing of brain signals : emotional states feature extraction
title_fullStr EEG based sensing of brain signals : emotional states feature extraction
title_full_unstemmed EEG based sensing of brain signals : emotional states feature extraction
title_sort eeg based sensing of brain signals : emotional states feature extraction
publishDate 2011
url http://hdl.handle.net/10356/45624
_version_ 1772828419772383232