{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:52:20Z","timestamp":1742914340359,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319441870"},{"type":"electronic","value":"9783319441887"}],"license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"unspecified","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":[[2016]]},"DOI":"10.1007\/978-3-319-44188-7_11","type":"book-chapter","created":{"date-parts":[[2016,8,18]],"date-time":"2016-08-18T04:18:22Z","timestamp":1471493902000},"page":"147-157","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["EEG-Based Condition Clustering using Self-Organising Neural Network Map"],"prefix":"10.1007","author":[{"given":"Hassan","family":"Hamdoun","sequence":"first","affiliation":[]},{"given":"Aliyu Ahmad","family":"Usman","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,8,19]]},"reference":[{"key":"11_CR1","doi-asserted-by":"publisher","first-page":"52","DOI":"10.4314\/eamj.v83i1.9362","volume":"83","author":"S Mbuya","year":"2006","unstructured":"Mbuya, S.: The role of neuro-electrophysiological diagnostic tests in clinical medicine. East Afr. Med. J. 83, 52\u201360 (2006)","journal-title":"East Afr. Med. J."},{"key":"11_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2014\/906038","volume":"2014","author":"NK Al-Qazzaz","year":"2014","unstructured":"Al-Qazzaz, N.K., Ali, S.H., Ahmad, S.A., Chellappan, K., Islam, M.S., Escudero, J.: Role of EEG as biomarker in the early detection and classification of dementia. Sci. World J. 2014, 1\u201316 (2014). Article ID 906038","journal-title":"Sci. World J."},{"key":"11_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1007\/978-3-642-22336-5_13","volume-title":"Transactions on Computational Science XII","author":"Y Liu","year":"2011","unstructured":"Liu, Y., Sourina, O., Nguyen, M.K.: Real-time EEG-based emotion recognition and its applications. In: Gavrilova, M.L., Tan, C., Sourin, A., Sourina, O. (eds.) Transactions on Computational Science XII. LNCS, vol. 6670, pp. 256\u2013277. Springer, Heidelberg (2011)"},{"key":"11_CR4","volume-title":"Electroencephalography: Basic Principles, Clinical Applications, and Related Fields","author":"E Niedermeyer","year":"2005","unstructured":"Niedermeyer, E., da Silva, F.L.: Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincott Williams & Wilkins, Philadelphia (2005)"},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Thuraisingham, R.A., Tran, Y., Craig, A., Nguyen, H.: Frequency analysis of eyes open and eyes closed EEG signals using the Hilbert-Huang Transform, pp. 2865\u20132868 (2012)","DOI":"10.1109\/EMBC.2012.6346561"},{"key":"11_CR6","unstructured":"AKBEN SB Online EEG eye state detection in time domain by using local amplitude increase"},{"key":"11_CR7","first-page":"89","volume":"12","author":"M Sakaia","year":"2010","unstructured":"Sakaia, M., Weia, D., Kongb, W., Daib, G., Hub, H.: Detection of change in alpha wave following eye closure based on KM2O-langevin equation. Int. J. Bioelectromag. 12, 89\u201393 (2010)","journal-title":"Int. J. Bioelectromag."},{"key":"11_CR8","doi-asserted-by":"publisher","first-page":"1034","DOI":"10.1109\/TBME.2004.827072","volume":"51","author":"G Schalk","year":"2004","unstructured":"Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R.: BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Trans. Biomed. Eng. 51, 1034\u20131043 (2004)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"11_CR9","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1109\/TNSRE.2012.2190299","volume":"20","author":"D Huang","year":"2012","unstructured":"Huang, D., Qian, K., Fei, D., Jia, W., Chen, X., Bai, O.: Electroencephalography (EEG)-based brain\u2013computer interface (BCI): a 2-D virtual wheelchair control based on event-related desynchronization\/synchronization and state control. IEEE Trans. Neural Syst. Rehabil. Eng. 20, 379\u2013388 (2012)","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"11_CR10","volume-title":"Bioelectrical Signal Processing in Cardiac and Neurological Applications","author":"L S\u00f6rnmo","year":"2005","unstructured":"S\u00f6rnmo, L., Laguna, P.: Bioelectrical Signal Processing in Cardiac and Neurological Applications. Academic Press, London (2005)"},{"key":"11_CR11","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.neuroimage.2009.02.006","volume":"46","author":"L Koessler","year":"2009","unstructured":"Koessler, L., Maillard, L., Benhadid, A., Vignal, J.P., Felblinger, J., Vespignani, H., Braun, M.: Automated cortical projection of EEG sensors: anatomical correlation via the international 10\u201310 system. Neuroimage 46, 64\u201372 (2009)","journal-title":"Neuroimage"},{"key":"11_CR12","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.psychres.2010.04.058","volume":"186","author":"SM Snyder","year":"2011","unstructured":"Snyder, S.M., Hall, J.R., Cornwell, S.L., Falk, J.D.: Addition of EEG improves accuracy of a logistic model that uses neuropsychological and cardiovascular factors to identify dementia and MCI. Psychiatry Res. 186, 97\u2013102 (2011)","journal-title":"Psychiatry Res."},{"key":"11_CR13","doi-asserted-by":"publisher","first-page":"1464","DOI":"10.1109\/5.58325","volume":"78","author":"T Kohonen","year":"1990","unstructured":"Kohonen, T.: The self-organizing map. Proc. IEEE 78, 1464\u20131480 (1990)","journal-title":"Proc. IEEE"},{"key":"11_CR14","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1109\/JOE.2013.2294279","volume":"40","author":"B Chakraborty","year":"2015","unstructured":"Chakraborty, B., Menezes, A., Dandapath, S., Fernandes, W.A., Karisiddaiah, S., Haris, K., Gokul, G.: Application of hybrid techniques (self-organizing map and fuzzy algorithm) using backscatter data for segmentation and fine-scale roughness characterization of seepage-related seafloor along the western continental margin of India. IEEE J. Oceanic Eng. 40, 3\u201314 (2015)","journal-title":"IEEE J. Oceanic Eng."},{"key":"11_CR15","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.ress.2015.02.011","volume":"139","author":"H Yu","year":"2015","unstructured":"Yu, H., Khan, F., Garaniya, V.: Risk-based fault detection using self-organizing map. Reliab. Eng. Syst. Saf. 139, 82\u201396 (2015)","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Rigamonti, M., Baraldi, P., Zio, E., Alessi, A., Astigarraga, D., Galarza, A.: A self-organizing map-based monitoring system for insulated gate bipolar transistors operating in fully electric vehicle, vol. 6 (2015)","DOI":"10.36001\/phmconf.2015.v7i1.2686"},{"key":"11_CR17","unstructured":"Merkevi\u010dius, E., Gar\u0161va, G., Simutis, R.: Forecasting of credit classes with the self-organizing maps 33 (2015)"},{"key":"11_CR18","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-28518-4","volume-title":"Advances in Self-Organizing Maps and Learning Vector Quantization","author":"E Mer\u00e9nyi","year":"2016","unstructured":"Mer\u00e9nyi, E., Mendenhall, M.J., O\u2019Driscoll, P.: Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing. Springer, Switzerland (2016)"},{"key":"11_CR19","doi-asserted-by":"crossref","unstructured":"Mans, R., Schonenberg, M., Song, M., van der Aalst, W., Bakker, P.: Process Mining in Healthcare (2015)","DOI":"10.1007\/978-3-319-16071-9"},{"key":"11_CR20","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1016\/S0893-6080(02)00079-5","volume":"15","author":"B Hammer","year":"2002","unstructured":"Hammer, B., Villmann, T.: Generalized relevance learning vector quantization. Neural Netw. 15, 1059\u20131068 (2002)","journal-title":"Neural Netw."},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Murugappan, M., Murugappan, S.: Human emotion recognition through short time Electroencephalogram (EEG) signals using Fast Fourier Transform (FFT), pp. 289\u2013294 (2013)","DOI":"10.1109\/CSPA.2013.6530058"},{"key":"11_CR22","doi-asserted-by":"publisher","first-page":"E215","DOI":"10.1161\/01.CIR.101.23.e215","volume":"101","author":"AL Goldberger","year":"2000","unstructured":"Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101, E215\u2013E220 (2000)","journal-title":"Circulation"}],"container-title":["Communications in Computer and Information Science","Engineering Applications of Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-44188-7_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T22:25:12Z","timestamp":1718749512000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-44188-7_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"ISBN":["9783319441870","9783319441887"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-44188-7_11","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2016]]},"assertion":[{"value":"19 August 2016","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Engineering Applications of Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Aberdeen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2016","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 September 2016","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 September 2016","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eann2016","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}