EEG-based human emotion recognition using k-NN machine learning

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Mental health issue is growing rapidly in these recent years. Teenagers and young adult aged of 16-30 years old are the most common victims. Mental health is a really serious issue concerning emotional health. One of the causes on emotional health issues is a lack of self-awareness, which is the key cornerstone on maintaining emotional state. In this study, EEG Neurostyle of 24 channels was used to obtain brain electrical signals. The mental emotions of the subjects were obtained from their reactions due to a set of audio-visual stimuli of approximately 5 minutes, the subject consists of 6 subjects aged 18-22 years old. The expressions of the subjects were recorded EEG signals separately to ensure their emotion according to the source (i.e. sad clips resulting sad emotion). The signals were processed using DFT and PSD to extract their features. The features were used to classify the emotions into 4 classes: happy, sad, scared, and disgust. In this study, the k-NN as classifier was used and obtained training and testing accuracy for all the features were greater than 52 % and 30 % for 4 classes.

Original languageEnglish
Title of host publicationProceedings of the 4th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2018
EditorsTerry Mart, Djoko Triyono, Ivandini T. Anggraningrum
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735419155
DOIs
Publication statusPublished - 4 Nov 2019
Event4th International Symposium on Current Progress in Mathematics and Sciences 2018, ISCPMS 2018 - Depok, Indonesia
Duration: 30 Oct 201831 Oct 2018

Publication series

NameAIP Conference Proceedings
Volume2168
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference4th International Symposium on Current Progress in Mathematics and Sciences 2018, ISCPMS 2018
CountryIndonesia
CityDepok
Period30/10/1831/10/18

Keywords

  • classification
  • EEG
  • emotions
  • k-NN
  • neurostyle

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