@inproceedings{scholars15573,
            note = {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},
            year = {2021},
             doi = {10.1007/978-981-16-4513-6{$_4$}{$_7$}},
       publisher = {Springer Science and Business Media B.V.},
         journal = {Springer Proceedings in Complexity},
           title = {K-means Clustering Analysis for EEG Features of Situational Interest Detection in Classroom Learning},
           pages = {541--550},
          author = {Othman, E. S. and Faye, I. and Babiker, A. and Hussaan, A. M.},
            issn = {22138684},
            isbn = {9789811645129},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123276466&doi=10.1007\%2f978-981-16-4513-6\%5f47&partnerID=40&md5=3f4c92f3574e9582beb6ff78b4a0ec4d},
        abstract = {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{\^A} 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. {\^A}{\copyright} 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.}
}