@inproceedings{scholars11822,
         journal = {2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018 - Proceedings},
            year = {2019},
           pages = {200--204},
           title = {Studying the effect of lecture content on students{\^a}?? EEG data in classroom using SVD},
            note = {cited By 3; Conference of 2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018 ; Conference Date: 3 December 2018 Through 6 December 2018; Conference Code:144644},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
             doi = {10.1109/IECBES.2018.8626664},
        keywords = {Biomedical engineering; Brain; Electroencephalography; Human computer interaction; Neurophysiology; Singular value decomposition; Support vector machines; Surveys, Brain activity; Classroom; Eeg datum; Human computer interfaces; Learning materials; Learning process; Singular values; Situational interest, Students},
        abstract = {The recent innovation in technology led to huge advancement in Human-Computer Interface (HCI) systems and applications. Detection of brain activities is the vital element in these applications. This paper is employing Singular Value Decomposition (SVD) on EEG data acquired simultaneously from students in classroom to detect the changes of brain activities during learning process. Situational interest of subjects and the learning materials were evaluated through questionnaires. After preprocessing and segmentation of the data, SVD was applied on each segment separately. The 2-norms of the singular values were compared to the subject baseline and the overall result complied with the questionnaire result. Furthermore, feeding these features to Support Vector Machine (SVM) classifier achieved 83.3 accuracy in differentiating between high and low situationally interested students. It is therefore, suggested that SVD could be applied successfully to detect changes in students{\^a}?? brain activities in classrooms. {\^A}{\copyright} 2018 IEEE.},
          author = {Babiker, A. and Faye, I. and Malik, A. S. and Sato, H.},
            isbn = {9781538624715},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062785773&doi=10.1109\%2fIECBES.2018.8626664&partnerID=40&md5=629ab3f8d0f817e565e7d0522405fd67}
}