TY - GEN
T1 - Non-Linear EEG based emotional classification using k-nearest neighbor and weighted k-nearest neighbor with variation of features selection methods
AU - Sari, Dessy Ana Laila
AU - Kusumaningrum, Theresia Diah
AU - Kusumoputro, Benyamin
N1 - Funding Information:
This work was supported by the PITTA program from DPRM Universitas Indonesia with contract no. NKB-0049/UN2.R3.1/HKP.05.00/2019.
Publisher Copyright:
© 2023 Author(s).
PY - 2023/2/14
Y1 - 2023/2/14
N2 - Emotion classification gaining more popular in research world, especially in healthcare. There are many methods can be used to classify emotional state of human and one of them is by using supervised machine learning such as kNN and W-kNN. DEAP's EEG dataset will be used as input signal because of its high dimension. In this research, EEG's non- linear features used to classify the emotional state. We compare recognition rate after variation in feature selection steps to choose which features best uses for this classification. For the emotion classification system we used variation of k parameter in kNN and W-kNN classifier. The results showed that the highest recognition rate was by using Chi-Square selection method with value of 60.15%, but by using those feature selection method did not really give significant difference. Based on that fact, we conclude that DEAP dataset need anoother reliable method to extract its feature and select those feature accurately.
AB - Emotion classification gaining more popular in research world, especially in healthcare. There are many methods can be used to classify emotional state of human and one of them is by using supervised machine learning such as kNN and W-kNN. DEAP's EEG dataset will be used as input signal because of its high dimension. In this research, EEG's non- linear features used to classify the emotional state. We compare recognition rate after variation in feature selection steps to choose which features best uses for this classification. For the emotion classification system we used variation of k parameter in kNN and W-kNN classifier. The results showed that the highest recognition rate was by using Chi-Square selection method with value of 60.15%, but by using those feature selection method did not really give significant difference. Based on that fact, we conclude that DEAP dataset need anoother reliable method to extract its feature and select those feature accurately.
UR - http://www.scopus.com/inward/record.url?scp=85149876163&partnerID=8YFLogxK
U2 - 10.1063/5.0116377
DO - 10.1063/5.0116377
M3 - Conference contribution
AN - SCOPUS:85149876163
T3 - AIP Conference Proceedings
BT - Proceedings of the 7th International Conference on Science and Technology
A2 - Yokozeki, Tomohiro
A2 - Santos, Gil Nonato C.
A2 - Omar, Rohayu Che
A2 - Widodo, null
A2 - Tristan, Abraham Cardenas
A2 - Putri, Ratih Fitria
A2 - Mustika, I. Wayan
PB - American Institute of Physics Inc.
T2 - 7th International Conference on Science and Technology, ICST 2021
Y2 - 7 September 2021 through 8 September 2021
ER -
