Exploring the feature selection of the EEG signal time and frequency domain features for k-NN and weighted k-NN

K. Theresia Diah, Akhmad Faqih, Benyamin Kusumoputro

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Emotion recognition based on EEG signals has become a prospective research area because of its potential application. The features used for classifier input play an important role in the classification results, while there is no exact method for defining the best feature for a classifier. In this paper, we use nine types of time frequency domains as features. We also use some feature selection methods to select the best feature for classification. We compare the results of classifications from each method of feature selection. This method is implemented using a DEAP (Dataset for emotion analysis using psychological signal) dataset. The results of the experiment demonstrate that the time-frequency domain feature extraction shows the best performance when using PCA feature selection and the k-NN classifier with a 60.68% recognition rate. This experiment also shows that there is no significant result between k-NN and weighted k-NN classification for each feature selection. In this case, we can conclude that the DEAP data needs a more reliable feature selection method.

Original languageEnglish
Title of host publicationProceedings of 2019 IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728108346
DOIs
Publication statusPublished - Nov 2019
Event2019 IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2019 - Depok, Indonesia
Duration: 12 Nov 201914 Nov 2019

Publication series

NameIEEE Region 10 Humanitarian Technology Conference, R10-HTC
Volume2019-November
ISSN (Print)2572-7621

Conference

Conference2019 IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2019
CountryIndonesia
CityDepok
Period12/11/1914/11/19

Keywords

  • DEAP
  • EEG
  • k-NN
  • Time-Frequency Domain Feature
  • weighted k-NN

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    Theresia Diah, K., Faqih, A., & Kusumoputro, B. (2019). Exploring the feature selection of the EEG signal time and frequency domain features for k-NN and weighted k-NN. In Proceedings of 2019 IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2019 [9042448] (IEEE Region 10 Humanitarian Technology Conference, R10-HTC; Vol. 2019-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/R10-HTC47129.2019.9042448