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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Bibliographic Details
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
Description
Summary: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.

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