@inproceedings{6693e5ab9d624c0f89e3097bb28ce0ca,
title = "EEG-based human emotion recognition using k-NN machine learning",
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.",
keywords = "classification, EEG, emotions, k-NN, neurostyle",
author = "A.A. Yusuf and Wijaya, {Sastra Kusuma} and Prawito Prajitno",
note = "Publisher Copyright: {\textcopyright} 2019 Author(s).; 4th International Symposium on Current Progress in Mathematics and Sciences 2018, ISCPMS 2018 ; Conference date: 30-10-2018 Through 31-10-2018",
year = "2019",
month = nov,
day = "4",
doi = "10.1063/1.5132447",
language = "English",
series = "AIP Conference Proceedings",
publisher = "American Institute of Physics Inc.",
editor = "Terry Mart and Djoko Triyono and Anggraningrum, {Ivandini T.}",
booktitle = "Proceedings of the 4th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2018",
address = "United States",
}
