Building an improved emotion recognition system for affective learning via brainwaves signals

Building an improved emotion recognition system for affective learning via brainwaves signals

Multiple studies show that emotions can be extracted from Electroencephalogram (EEG) signals. In order to achieve a high recognition rate, feature extraction techniques must be properly applied when working with brainwave signals. Of these techniques, the more commonly used are statistical features...

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Main Authors: Berin, Joshua-Mari, King, Mark Kevin W.
Format: text
Language:English
Published: Animo Repository 2014
Subjects:
Electroencephalography
Theta rhythm
Fourier transformations
Artificial Intelligence and Robotics
Computer Sciences
Online Access:https://animorepository.dlsu.edu.ph/etd_bachelors/5559
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Institution: De La Salle University
Language: English
id oai:animorepository.dlsu.edu.ph:etd_bachelors-6036
record_format eprints
spelling oai:animorepository.dlsu.edu.ph:etd_bachelors-60362022-03-03T05:53:20Z Building an improved emotion recognition system for affective learning via brainwaves signals Berin, Joshua-Mari King, Mark Kevin W. Multiple studies show that emotions can be extracted from Electroencephalogram (EEG) signals. In order to achieve a high recognition rate, feature extraction techniques must be properly applied when working with brainwave signals. Of these techniques, the more commonly used are statistical features and Fast Fourier transform. Such feature extraction however, was only able to achieve the highest recognition rate of 67. 2014-01-01T08:00:00Z text https://animorepository.dlsu.edu.ph/etd_bachelors/5559 Bachelor's Theses English Animo Repository Electroencephalography Theta rhythm Fourier transformations Artificial Intelligence and Robotics Computer Sciences
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
language English
topic Electroencephalography
Theta rhythm
Fourier transformations
Artificial Intelligence and Robotics
Computer Sciences
spellingShingle Electroencephalography
Theta rhythm
Fourier transformations
Artificial Intelligence and Robotics
Computer Sciences
Berin, Joshua-Mari
King, Mark Kevin W.
Building an improved emotion recognition system for affective learning via brainwaves signals
description Multiple studies show that emotions can be extracted from Electroencephalogram (EEG) signals. In order to achieve a high recognition rate, feature extraction techniques must be properly applied when working with brainwave signals. Of these techniques, the more commonly used are statistical features and Fast Fourier transform. Such feature extraction however, was only able to achieve the highest recognition rate of 67.
format text
author Berin, Joshua-Mari
King, Mark Kevin W.
author_facet Berin, Joshua-Mari
King, Mark Kevin W.
author_sort Berin, Joshua-Mari
title Building an improved emotion recognition system for affective learning via brainwaves signals
title_short Building an improved emotion recognition system for affective learning via brainwaves signals
title_full Building an improved emotion recognition system for affective learning via brainwaves signals
title_fullStr Building an improved emotion recognition system for affective learning via brainwaves signals
title_full_unstemmed Building an improved emotion recognition system for affective learning via brainwaves signals
title_sort building an improved emotion recognition system for affective learning via brainwaves signals
publisher Animo Repository
publishDate 2014
url https://animorepository.dlsu.edu.ph/etd_bachelors/5559
_version_ 1728621038621687808

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