Real time EEG-based stress detection and meditation application with K-nearest neighbor

Prima Dewi Purnamasari, Alya Fernandya

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

Abstract

The desire to end someone's life commonly triggered by depression or mental health. One way to detect whether someone is depressed is by checking whether the person is constantly stressed for a long time. Electroencephalograph (EEG) is one of physiological signal that can be used for monitoring someone's mental condition, such as stress. The proposed and developed Stress Detection and Meditation Application is an application that can be used for meditating and reducing stress level by using EEG to get brainwave signals. In this developed system, Fast Fourier Transform (FFT) was used as the feature extraction and k-Nearest Neighbor (k-NN) was used to classify the features and detect whether the person is stressed or not. The Delta, theta, alpha, and beta waves as the features were the most suitable feature type for the stress detection and meditation application, while the value of k = 3 in k-NN classification provides the best k value with 80% accuracy.

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

  • Brainwaves
  • EEG
  • K-NN
  • Meditation
  • Stress

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