{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T03:48:55Z","timestamp":1775274535776,"version":"3.50.1"},"publisher-location":"Cham","reference-count":44,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030042387","type":"print"},{"value":"9783030042394","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-030-04239-4_43","type":"book-chapter","created":{"date-parts":[[2018,11,17]],"date-time":"2018-11-17T01:55:08Z","timestamp":1542419708000},"page":"478-488","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["EEG Sparse Representation Based Alertness States Identification Using Gini Index"],"prefix":"10.1007","author":[{"given":"Muna","family":"Tageldin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Talal","family":"Al-Mashaikki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hamza","family":"Bali","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mostefa","family":"Mesbah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,11,18]]},"reference":[{"key":"43_CR1","doi-asserted-by":"crossref","unstructured":"Yan, J.J., Kuo, H.H., et al.: Real-time driver drowsiness detection system based on PERCLOS and grayscale image processing. In: International Symposium on Computer, Consumer and Control (IS3C), pp. 243\u2013246 (2016)","DOI":"10.1109\/IS3C.2016.72"},{"key":"43_CR2","doi-asserted-by":"crossref","unstructured":"Alshaqaqi, B., Baquhaizel, A.S., et al.: Driver drowsiness detection system. In: Workshop on Systems, Signal Processing, and their Applications, pp. 151\u2013155 (2013)","DOI":"10.1109\/WoSSPA.2013.6602353"},{"issue":"9","key":"43_CR3","doi-asserted-by":"publisher","first-page":"1991","DOI":"10.3390\/s17091991","volume":"17","author":"M Awais","year":"2017","unstructured":"Awais, M., Badruddin, N., Drieberg, M.: A Hybrid approach to detect driver drowsiness utilizing physiological signals to improve system performance and wearability. Sensors (Basel) 17(9), 1991 (2017)","journal-title":"Sensors (Basel)"},{"key":"43_CR4","unstructured":"Nodine, E.: The detection of drowsy drivers through driver performance indicators. Master of Science, Tuffs University (2006)"},{"issue":"2","key":"43_CR5","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.medengphy.2013.07.011","volume":"36","author":"CA Garc\u00e9s","year":"2014","unstructured":"Garc\u00e9s, C.A., Orosco, L., et al.: Automatic detection of drowsiness in EEG records based on multimodal analysis. Med. Eng. Phys. 36(2), 244\u2013249 (2014)","journal-title":"Med. Eng. Phys."},{"issue":"2","key":"43_CR6","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.biopsycho.2011.03.003","volume":"87","author":"RR Johnson","year":"2011","unstructured":"Johnson, R.R., Popovic, D.P., et al.: Drowsiness\/alertness algorithm development and validation using synchronized EEG and cognitive performance to individualize a generalized model. Biol. Psychol. 87(2), 241\u2013250 (2011)","journal-title":"Biol. Psychol."},{"issue":"1","key":"43_CR7","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/S0987-7053(01)00289-1","volume":"32","author":"JL Cantero","year":"2002","unstructured":"Cantero, J.L., Atienza, M., et al.: Human alpha oscillations in wakefulness, drowsiness period, and REM sleep: different electroencephalographic phenomena within the alpha band. Neurophysiologie Clinique\/Clin. Neurophysiol. 32(1), 54\u201371 (2002)","journal-title":"Neurophysiologie Clinique\/Clin. Neurophysiol."},{"key":"43_CR8","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/j.jneumeth.2004.04.027","volume":"139","author":"MK Kiymik","year":"2004","unstructured":"Kiymik, M.K., Akin, M., et al.: Automatic recognition of alertness level by using wavelet transform and artificial neural network. J. Neurosci. Methods 139, 231\u2013240 (2004)","journal-title":"J. Neurosci. Methods"},{"key":"43_CR9","doi-asserted-by":"crossref","unstructured":"Yu, S., Li, P. et al.: Support vector machine based detection of drowsiness using minimum EEG features. In: SocialCom, pp. 827\u2013835 (2013)","DOI":"10.1109\/SocialCom.2013.124"},{"issue":"6","key":"43_CR10","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1007\/s12209-013-2027-3","volume":"19","author":"X Wang","year":"2013","unstructured":"Wang, X., Zhang, Y., et al.: Alertness staging based on improved self-organizing map. Trans. Tianjin Univ. 19(6), 459\u2013462 (2013)","journal-title":"Trans. Tianjin Univ."},{"key":"43_CR11","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.procs.2014.07.045","volume":"34","author":"N Gurudath","year":"2014","unstructured":"Gurudath, N., Riley, H.B.: Drowsy driving detection by EEG analysis using wavelet transform and K-means clustering. Procedia Comput. Sci. 34, 400\u2013409 (2014)","journal-title":"Procedia Comput. Sci."},{"key":"43_CR12","doi-asserted-by":"crossref","unstructured":"Al-Ani, A., Mesbah, M.: EEG rhythm\/channel selection for fuzzy rule-based alertness state characterization. Neural Comput Appl (2016)","DOI":"10.1007\/s00521-016-2835-1"},{"issue":"2","key":"43_CR13","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1109\/TPAMI.2008.79","volume":"31","author":"J Wright","year":"2009","unstructured":"Wright, J., Yang, A.Y., et al.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210\u2013227 (2009)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"43_CR14","doi-asserted-by":"publisher","first-page":"172","DOI":"10.3389\/fnagi.2016.00172","volume":"8","author":"D Wen","year":"2016","unstructured":"Wen, D., Jia, P., et al.: Review of sparse representation-based classification methods on EEG signal processing for epilepsy detection, brain-computer interface and cognitive impairment. Front. Aging Neurosci. 8, 172 (2016)","journal-title":"Front. Aging Neurosci."},{"key":"43_CR15","doi-asserted-by":"crossref","unstructured":"Yu, H., Lu, H. et al.: Vigilance detection based on sparse representation of EEG. In: Proceedings of IEEE Engineering in Medicine and Biology Society Conference, pp. 2439\u20132442 (2010)","DOI":"10.1109\/IEMBS.2010.5626084"},{"issue":"2","key":"43_CR16","doi-asserted-by":"publisher","first-page":"242","DOI":"10.3390\/s16020242","volume":"16","author":"Z Zhang","year":"2016","unstructured":"Zhang, Z., Luo, D., et al.: A Vehicle active safety model: vehicle speed control based on driver vigilance detection using wearable EEG and sparse representation. Sensors 16(2), 242 (2016)","journal-title":"Sensors"},{"key":"43_CR17","doi-asserted-by":"crossref","unstructured":"Luo, D.Y., Zhang, Z.T.: A novel vehicle speed control based on driver\u2019s vigilance detection using EEG and sparse representation. In: Applied Mechanics and Materials, pp. 607\u2013611 (2014)","DOI":"10.4028\/www.scientific.net\/AMM.651-653.607"},{"issue":"10","key":"43_CR18","doi-asserted-by":"publisher","first-page":"4723","DOI":"10.1109\/TIT.2009.2027527","volume":"55","author":"N Hurley","year":"2009","unstructured":"Hurley, N., Rickard, S.: Comparing measures of sparsity. IEEE Trans. Inf. Theory 55(10), 4723\u20134741 (2009)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"43_CR19","doi-asserted-by":"crossref","unstructured":"Baali, H., Mesbah, M.: Ventricular ectopic beats classification using sparse representation and Gini index. In: Proceedings of IEEE Engineering in Medicine and Biology Society Conference, pp. 5821\u20135824 (2015)","DOI":"10.1109\/EMBC.2015.7319715"},{"issue":"5","key":"43_CR20","doi-asserted-by":"publisher","first-page":"927","DOI":"10.1109\/JSTSP.2011.2160711","volume":"5","author":"D Zonoobi","year":"2011","unstructured":"Zonoobi, D., Kassim, A.A., et al.: Gini index as sparsity measure for signal reconstruction from compressive samples. IEEE J. Sel. Topics Signal Process. 5(5), 927\u2013932 (2011)","journal-title":"IEEE J. Sel. Topics Signal Process."},{"issue":"6","key":"43_CR21","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1109\/JPROC.2010.2040551","volume":"98","author":"R Rubinstein","year":"2010","unstructured":"Rubinstein, R., Bruckstein, A.M., et al.: Dictionaries for sparse representation modeling. Proc. IEEE 98(6), 1045\u20131057 (2010)","journal-title":"Proc. IEEE"},{"issue":"11","key":"43_CR22","doi-asserted-by":"publisher","first-page":"4311","DOI":"10.1109\/TSP.2006.881199","volume":"5","author":"M Aharon","year":"2006","unstructured":"Aharon, M., Elad, M., et al.: K-SVD: An Algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. 5(11), 4311\u20134322 (2006)","journal-title":"IEEE Trans. Signal Process."},{"key":"43_CR23","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1109\/ACCESS.2015.2430359","volume":"3","author":"Z Zhang","year":"2015","unstructured":"Zhang, Z., Xu, Y., et al.: A Survey of sparse representation: algorithms and applications. IEEE Access 3, 490\u2013530 (2015)","journal-title":"IEEE Access"},{"issue":"4","key":"43_CR24","first-page":"441","volume":"11","author":"C Feng","year":"2014","unstructured":"Feng, C., Xiao, L., et al.: Compressive sensing inverse synthetic aperture radar imaging based on Gini index regularization. IJAC 11(4), 441\u2013448 (2014)","journal-title":"IJAC"},{"issue":"13","key":"43_CR25","first-page":"185","volume":"7","author":"C Feng","year":"2013","unstructured":"Feng, C., Xiao, L., et al.: Parameterized lorenz curve based compressive sensing reconstruction. JDCTA 7(13), 185\u2013194 (2013)","journal-title":"JDCTA"},{"key":"43_CR26","unstructured":"Huang, Z., Liu, Y., et al.: Study on sparse representation based classification for biometric verification, https:\/\/arxiv.org\/abs\/1502.06073 (2015)"},{"key":"43_CR27","unstructured":"Gangeh, M.J., Farahat, A.K., et al.: Supervised dictionary learning and sparse representation: a review, https:\/\/arxiv.org\/abs\/1502.05928 (2015)"},{"key":"43_CR28","doi-asserted-by":"crossref","unstructured":"Goldberger, A.L., Amaral, L.A.N., et al.: PhysioBank, PhysioToolkit, and PhysioNet. Circulation 101(23), E215\u2013E220 (2000)","DOI":"10.1161\/01.CIR.101.23.e215"},{"issue":"9","key":"43_CR29","doi-asserted-by":"publisher","first-page":"1185","DOI":"10.1109\/10.867928","volume":"47","author":"B Kemp","year":"2000","unstructured":"Kemp, B., et al.: Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE Trans. Bio-Med. Eng. 47(9), 1185\u20131194 (2000)","journal-title":"IEEE Trans. Bio-Med. Eng."},{"issue":"11","key":"43_CR30","doi-asserted-by":"publisher","first-page":"1587","DOI":"10.1093\/sleep\/30.11.1587","volume":"30","author":"C Berthomier","year":"2007","unstructured":"Berthomier, C., Drouot, X., et al.: Automatic analysis of single-channel sleep EEG: validation in healthy individuals. Sleep 30(11), 1587\u20131595 (2007)","journal-title":"Sleep"},{"key":"43_CR31","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1016\/j.eswa.2016.02.041","volume":"55","author":"TLT da Silveira","year":"2016","unstructured":"da Silveira, T.L.T., Kozakevicius, A.J., et al.: Automated drowsiness detection through wavelet packet analysis of a single EEG channel. Expert Sys. Appl. 55, 559\u2013565 (2016)","journal-title":"Expert Sys. Appl."},{"key":"43_CR32","doi-asserted-by":"crossref","unstructured":"Pal, N.R., Chuang, C.Y., et al.: EEG-based subject- and session-independent drowsiness detection: an unsupervised approach. EURASIP J. Adv. Signal Process., 519480 (2008)","DOI":"10.1155\/2008\/519480"},{"key":"43_CR33","doi-asserted-by":"crossref","unstructured":"Imtiaz, S.A. at al.: An open-source toolbox for standardized use of PhysioNet Sleep EDF expanded database. In: Proceedings of IEEE Engineering in Medicine and Biology Society Conference, pp. 6014\u20136017 (2015)","DOI":"10.1109\/EMBC.2015.7319762"},{"key":"43_CR34","doi-asserted-by":"crossref","unstructured":"\u015een, B., Peker, M., \u00c7avu\u015fo\u011flu, A., \u00c7elebi, F.V.: A comparative study on classification of sleep stage based on EEG signals using feature selection and classification algorithms. J. Med. Syst. 38(3), 18 (2014)","DOI":"10.1007\/s10916-014-0018-0"},{"issue":"5","key":"43_CR35","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1016\/0013-4694(96)95636-9","volume":"98","author":"J Fell","year":"1996","unstructured":"Fell, J., et al.: Discrimination of sleep stages: a comparison between spectral and nonlinear EEG measures. Electroencephalogr. Clin. Neurophysiol. 98(5), 401\u2013410 (1996)","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"43_CR36","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1007\/978-3-642-25664-6_87","volume-title":"Advances in Intelligent and Soft Computing","author":"Hongfei Ji","year":"2011","unstructured":"Ji, H., Li, J., Cao, L., Wang, D.: A EEG-based brain computer interface system towards applicable vigilance monitoring. In: Wang, Y., Li, T. (eds.) Foundations of Intelligent Systems. Advances in Intelligent and Soft Computing, vol 122. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-25664-6_87"},{"issue":"2","key":"43_CR37","doi-asserted-by":"publisher","first-page":"2352","DOI":"10.1016\/j.eswa.2007.12.043","volume":"36","author":"B Jap","year":"2009","unstructured":"Jap, B., Lal, S., et al.: Using EEG spectral components to assess algorithms for detecting fatigue. Expert Syst. Appl. 36(2), 2352\u20132359 (2009)","journal-title":"Expert Syst. Appl."},{"issue":"4","key":"43_CR38","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/j.ergon.2004.09.006","volume":"35","author":"H Eoh","year":"2005","unstructured":"Eoh, H., Chung, M., et al.: Electroencephalographic study of drowsiness in simulated driving with sleep deprivation. Int. J. Ind. Ergon. 35(4), 307\u2013320 (2005)","journal-title":"Int. J. Ind. Ergon."},{"key":"43_CR39","doi-asserted-by":"crossref","unstructured":"Spall, J.C.: Simultaneous Perturbation Stochastic Approximation - Introduction to stochastic Search and Optimization. Wiley (2003)","DOI":"10.1002\/0471722138"},{"key":"43_CR40","doi-asserted-by":"crossref","unstructured":"Sadegh, P., Spall, J.C.: Optimal random perturbations for stochastic approximation using a simultaneous perturbation gradient approximation. In: Proceedings of the 1997 American Control Conference, pp. 3582\u20133586 (1997)","DOI":"10.1109\/ACC.1997.609490"},{"issue":"7","key":"43_CR41","doi-asserted-by":"publisher","first-page":"4680","DOI":"10.1109\/TIT.2011.2146090","volume":"57","author":"TT Cai","year":"2011","unstructured":"Cai, T.T., Wang, L.: Orthogonal matching pursuit for sparse signal recovery with noise. IEEE Trans. Inf. Theory 57(7), 4680\u20134688 (2011)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"43_CR42","unstructured":"Shaban, M.: OMP (2015). https:\/\/www.mathworks.com\/matlabcentral\/fileexchange\/50584-orthognal-matching-pursuit-algorithm-omp"},{"issue":"2","key":"43_CR43","doi-asserted-by":"publisher","first-page":"890","DOI":"10.1137\/080714488","volume":"31","author":"E van den Berg","year":"2008","unstructured":"van den Berg, E., Friedlander, M.: Probing the pareto frontier for basis pursuit solutions. SIAM J. Sci. Comput. 31(2), 890\u2013912 (2008)","journal-title":"SIAM J. Sci. Comput."},{"key":"43_CR44","unstructured":"Berg, E.v.d., Friedlander, M.P.: SPGL1: a solver for large-scale sparse reconstruction (2007). https:\/\/www.cs.ubc.ca\/~mpf\/spgl1\/"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-04239-4_43","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T02:55:47Z","timestamp":1775271347000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-04239-4_43"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030042387","9783030042394"],"references-count":44,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-04239-4_43","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"18 November 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Siem Reap","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cambodia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 December 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 December 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conference.cs.cityu.edu.hk\/iconip\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"575","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"401","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"70% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}