{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T05:15:50Z","timestamp":1743138950250,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811675966"},{"type":"electronic","value":"9789811675973"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-16-7597-3_17","type":"book-chapter","created":{"date-parts":[[2022,2,28]],"date-time":"2022-02-28T11:02:48Z","timestamp":1646046168000},"page":"213-221","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Selection of Intrinsic Mode Function in Ensemble Empirical Mode Decomposition Based on Peak Frequency of PSD for EEG Data Analysis"],"prefix":"10.1007","author":[{"given":"Mohd Nurul Al Hafiz","family":"Sha\u2019abani","sequence":"first","affiliation":[]},{"given":"Norfaiza","family":"Fuad","sequence":"additional","affiliation":[]},{"given":"Norezmi","family":"Jamal","sequence":"additional","affiliation":[]},{"given":"Engku Mohd Nasri","family":"Engku Mat Nasir","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,28]]},"reference":[{"key":"17_CR1","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/j.chb.2017.12.037","volume":"81","author":"J Xu","year":"2018","unstructured":"Xu, J., Zhong, B.: Review on portable EEG technology in educational research. Comput. Hum. Behav. 81, 340\u2013349 (2018). https:\/\/doi.org\/10.1016\/j.chb.2017.12.037","journal-title":"Comput. Hum. Behav."},{"key":"17_CR2","doi-asserted-by":"publisher","unstructured":"LaRocco, J., Le, M.D., Paeng, D.-G.: A systemic review of available low-cost EEG headsets used for drowsiness detection. Front. Neuroinformat. 14, 553352 (2020). https:\/\/doi.org\/10.3389\/fninf.2020.553352","DOI":"10.3389\/fninf.2020.553352"},{"issue":"4","key":"17_CR3","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1016\/0013-4694(87)90206-9","volume":"66","author":"RW Homan","year":"1987","unstructured":"Homan, R.W., Herman, J., Purdy, P.: Cerebral location of international 10\u201320 system electrode placement. Electroencephalogr. Clin. Neurophysiol. 66(4), 376\u2013382 (1987). https:\/\/doi.org\/10.1016\/0013-4694(87)90206-9","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"17_CR4","doi-asserted-by":"publisher","unstructured":"James, C., Lowe, D.: Extracting information from multichannel versus single channel EEG data in epilepsy analysis. In: Hyder, A.K., Shahbazian, E., Waltz, E. (Eds.) Multisensor Fusion, vol. 1, pp. 889\u2013895. Springer, Dordrecht (2002). https:\/\/doi.org\/10.1007\/978-94-010-0556-2_47","DOI":"10.1007\/978-94-010-0556-2_47"},{"key":"17_CR5","doi-asserted-by":"publisher","unstructured":"Troy M, L., Joseph T, G., Daniel P, F.: How many electrodes are really needed for EEG-based mobile brain imaging? J. Behav. Brain Sci. 2(3), 387\u2013393 (2012). https:\/\/doi.org\/10.4236\/jbbs.2012.23044","DOI":"10.4236\/jbbs.2012.23044"},{"key":"17_CR6","doi-asserted-by":"publisher","unstructured":"So, W.K., Wong, S.W., Mak, J.N., Chan, R.H.: An evaluation of mental workload with frontal EEG. PloS ONE 12(4), e0174949 (2017). https:\/\/doi.org\/10.1371\/journal.pone.0174949","DOI":"10.1371\/journal.pone.0174949"},{"key":"17_CR7","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/j.biopsycho.2018.11.003","volume":"140","author":"D van Son","year":"2019","unstructured":"van Son, D., De Blasio, F.M., Fogarty, J.S., Angelidis, A., Barry, R.J., Putman, P.: Frontal EEG theta\/beta ratio during mind wandering episodes. Biol. Psychol. 140, 19\u201327 (2019). https:\/\/doi.org\/10.1016\/j.biopsycho.2018.11.003","journal-title":"Biol. Psychol."},{"issue":"4","key":"17_CR8","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.neucli.2016.07.002","volume":"46","author":"MK Islam","year":"2016","unstructured":"Islam, M.K., Rastegarnia, A., Yang, Z.: Methods for artifact detection and removal from scalp EEG: a review. Neurophysiologie Clinique\/Clin. Neurophysiol. 46(4), 287\u2013305 (2016). https:\/\/doi.org\/10.1016\/j.neucli.2016.07.002","journal-title":"Neurophysiologie Clinique\/Clin. Neurophysiol."},{"issue":"5","key":"17_CR9","doi-asserted-by":"publisher","first-page":"987","DOI":"10.3390\/s19050987","volume":"19","author":"X Jiang","year":"2019","unstructured":"Jiang, X., Bian, G.-B., Tian, Z.: Removal of artifacts from EEG signals: a review. Sensors 19(5), 987 (2019). https:\/\/doi.org\/10.3390\/s19050987","journal-title":"Sensors"},{"issue":"2","key":"17_CR10","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1109\/TIM.2017.2759398","volume":"67","author":"X Chen","year":"2017","unstructured":"Chen, X., Xu, X., Liu, A., McKeown, M.J., Wang, Z.J.: The use of multivariate EMD and CCA for denoising muscle artifacts from few-channel EEG recordings. IEEE Trans. Instrum. Meas. 67(2), 359\u2013370 (2017). https:\/\/doi.org\/10.1109\/TIM.2017.2759398","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"01","key":"17_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1142\/S1793536909000047","volume":"1","author":"Z Wu","year":"2009","unstructured":"Wu, Z., Huang, N.E.: Ensemble empirical mode decomposition: a noise-assisted data analysis method. J. Adv. Adapt. Data Anal. 1(01), 1\u201341 (2009). https:\/\/doi.org\/10.1142\/S1793536909000047","journal-title":"J. Adv. Adapt. Data Anal."},{"key":"17_CR12","doi-asserted-by":"publisher","unstructured":"Boutana, D., Benidir, M., Barkat, B.: On the selection of intrinsic mode function in EMD method: application on heart sound signal. In: 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies, pp. 1\u20135. IEEE (2010). https:\/\/doi.org\/10.1109\/ISABEL.2010.5702895","DOI":"10.1109\/ISABEL.2010.5702895"},{"key":"17_CR13","doi-asserted-by":"publisher","unstructured":"Cho, S., Shahriar, M. R., Chong, U.: Identification of significant intrinsic mode functions for the diagnosis of induction motor fault. J. Acoust. Soc. Am. 136(2), EL72-EL77 (2014). https:\/\/doi.org\/10.1121\/1.4885541","DOI":"10.1121\/1.4885541"},{"key":"17_CR14","doi-asserted-by":"publisher","unstructured":"Kotan, S., Van Schependom, J., Nagels, G., Akan, A.: Comparison of IMF selection methods in classification of multiple sclerosis EEG data. In: Medical Technologies Congress, pp. 1\u20134. IEEE (2019). https:\/\/doi.org\/10.1109\/TIPTEKNO.2019.8895091","DOI":"10.1109\/TIPTEKNO.2019.8895091"},{"issue":"4","key":"17_CR15","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1016\/j.ymssp.2005.09.011","volume":"20","author":"C Junsheng","year":"2006","unstructured":"Junsheng, C., Dejie, Y., Yu, Y.: Research on the intrinsic mode function (IMF) criterion in EMD method. Mech. Syst. Signal Process. 20(4), 817\u2013824 (2006). https:\/\/doi.org\/10.1016\/j.ymssp.2005.09.011","journal-title":"Mech. Syst. Signal Process."},{"issue":"5","key":"17_CR16","doi-asserted-by":"publisher","first-page":"974","DOI":"10.1016\/j.ymssp.2004.01.006","volume":"19","author":"Z Peng","year":"2005","unstructured":"Peng, Z., Peter, W.T., Chu, F.: A comparison study of improved Hilbert-Huang transform and wavelet transform: application to fault diagnosis for rolling bearing. Mech. Syst. Signal Process. 19(5), 974\u2013988 (2005). https:\/\/doi.org\/10.1016\/j.ymssp.2004.01.006","journal-title":"Mech. Syst. Signal Process."},{"key":"17_CR17","doi-asserted-by":"publisher","unstructured":"Kotan, S., Akan, A.: A new intrinsic mode function selection method based on power spectral density. In: Medical Technologies National Congress, pp. 1\u20134. IEEE (2018). https:\/\/doi.org\/10.1109\/TIPTEKNO.2018.8597127","DOI":"10.1109\/TIPTEKNO.2018.8597127"},{"key":"17_CR18","doi-asserted-by":"publisher","unstructured":"Sha\u2019abani, M.N.A.H., Fuad, N., Jamal, N., Marwan, M., Abd Wahab, M.H., Idrus, S. Z.S.: Development of cognitive and psychomotor task for EEG application with matlab-based GUI. In: IOP Conference Series: Materials Science and Engineering, pp. 012050. vol. 917, no. 1, IOP Publishing (2020). https:\/\/doi.org\/10.1088\/1757-899X\/917\/1\/012050","DOI":"10.1088\/1757-899X\/917\/1\/012050"},{"key":"17_CR19","unstructured":"Teplan, M.: Fundamentals of EEG measurement. Meas. Sci. Rev. 2(2), 1\u201311 (2002). https:\/\/www.measurement.sk\/2002\/S2\/p2.html"},{"key":"17_CR20","doi-asserted-by":"publisher","unstructured":"Kim, D.-W., Im, C.-H.: EEG spectral analysis. In: Im, C.-H. (Ed.) Computational EEG Analysis: Methods and Applications, vol. 1, pp. 35\u201353. Springer, Singapore (2018). https:\/\/doi.org\/10.1007\/978-981-13-0908-3","DOI":"10.1007\/978-981-13-0908-3"},{"issue":"24","key":"17_CR21","doi-asserted-by":"publisher","first-page":"5340","DOI":"10.3390\/app9245340","volume":"9","author":"M Plechawska-W\u00f3jcik","year":"2019","unstructured":"Plechawska-W\u00f3jcik, M., Tokovarov, M., Kaczorowska, M., Zapa\u0142a, D.: A three-class classification of cognitive workload based on EEG spectral data. J. Appl. Sci. 9(24), 5340 (2019). https:\/\/doi.org\/10.3390\/app9245340","journal-title":"J. Appl. Sci."}],"container-title":["Lecture Notes in Networks and Systems","Proceedings of the Third International Conference on Trends in Computational and Cognitive Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-7597-3_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,29]],"date-time":"2022-07-29T14:09:22Z","timestamp":1659103762000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-7597-3_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811675966","9789811675973"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-7597-3_17","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"28 February 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}