@inproceedings{scholars5628,
           pages = {7250--7253},
         journal = {Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
            year = {2015},
           title = {Investigation of window size in classification of EEG-emotion signal with wavelet entropy and support vector machine},
             doi = {10.1109/EMBC.2015.7320065},
            note = {cited By 110; Conference of 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 ; Conference Date: 25 August 2015 Through 29 August 2015; Conference Code:116805},
          volume = {2015-N},
            issn = {1557170X},
          author = {Candra, H. and Yuwono, M. and Chai, R. and Handojoseno, A. and Elamvazuthi, I. and Nguyen, H. T. and Su, S.},
            isbn = {9781424492718},
        keywords = {arousal; electroencephalography; emotion; entropy; human; physiology; support vector machine; wavelet analysis, Arousal; Electroencephalography; Emotions; Entropy; Humans; Support Vector Machine; Wavelet Analysis},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953286826&doi=10.1109\%2fEMBC.2015.7320065&partnerID=40&md5=b591a5835889ed726c9edb31b52bc417},
        abstract = {When dealing with patients with psychological or emotional symptoms, medical practitioners are often faced with the problem of objectively recognizing their patients' emotional state. In this paper, we approach this problem using a computer program that automatically extracts emotions from EEG signals. We extend the finding of Koelstra et. al IEEE trans. affective comput., vol. 3, no. 1, pp. 18-31, 2012 using the same dataset (i.e. the DEAP: dataset for emotion analysis using electroencephalogram, physiological and video signals), where we observed that the accuracy can be further improved using wavelet features extracted from shorter time segments. More precisely, we achieved accuracy of 65\% for both valence and arousal using the wavelet entropy of 3 to 12 seconds signal segments. This improvement in accuracy entails an important discovery that information on emotions contained in the EEG signal may be better described in term of wavelets and in shorter time segments. {\^A}{\copyright} 2015 IEEE.}
}