TY  - CONF
N2  - This paper proposes a method to detect situational interest in classroom learning using k-means algorithms. The developed algorithm in this paper had been tested on features from ten students who experienced mathematics learning in a classroom. The subjects were given 21 min of Laplace lecture presentation with some interesting elements introduced. Electroencephalogram (EEG) signal was preprocessed and decomposed using Fast Fourier Transform. The mean power for each sub-frequency band was served as input to the k-means algorithm. Results showed that EEG features can be successfully clustered in the alpha frequency band at the frontal region when visual-auditory stimuli are introduced to the subjects. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
N1  - cited By 0; Conference of 6th International Conference on Fundamental and Applied Sciences, ICFAS 2020 ; Conference Date: 13 July 2021 Through 15 July 2021; Conference Code:270909
ID  - scholars15573
TI  - K-means Clustering Analysis for EEG Features of Situational Interest Detection in Classroom Learning
SP  - 541
AV  - none
A1  - Othman, E.S.
A1  - Faye, I.
A1  - Babiker, A.
A1  - Hussaan, A.M.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123276466&doi=10.1007%2f978-981-16-4513-6_47&partnerID=40&md5=3f4c92f3574e9582beb6ff78b4a0ec4d
EP  - 550
Y1  - 2021///
PB  - Springer Science and Business Media B.V.
SN  - 22138684
ER  -