{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T20:08:39Z","timestamp":1743106119644,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030881627"},{"type":"electronic","value":"9783030881634"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-88163-4_27","type":"book-chapter","created":{"date-parts":[[2021,10,10]],"date-time":"2021-10-10T04:08:05Z","timestamp":1633838885000},"page":"311-322","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Energy-Time Profiling for Machine Learning Methods to EEG Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2164-4991","authenticated-orcid":false,"given":"Juan Carlos","family":"G\u00f3mez-L\u00f3pez","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4258-0264","authenticated-orcid":false,"given":"Juan Jos\u00e9","family":"Escobar","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0415-1821","authenticated-orcid":false,"given":"Jes\u00fas","family":"Gonz\u00e1lez","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4105-565X","authenticated-orcid":false,"given":"Francisco","family":"Gil-Montoya","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2998-220X","authenticated-orcid":false,"given":"Julio","family":"Ortega","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5094-5668","authenticated-orcid":false,"given":"Mike","family":"Burmester","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2599-8076","authenticated-orcid":false,"given":"Miguel","family":"Damas","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,10,9]]},"reference":[{"key":"27_CR1","unstructured":"Dertat, A.: Applied deep learning - part 4: convolutional neural networks. https:\/\/medium.com\/@ardendertat"},{"key":"27_CR2","doi-asserted-by":"publisher","unstructured":"Amra, I., Maghari, A.: Students performance prediction using KNN and na\u00efve bayesian. In: 2017 8th International Conference on Information Technology. ICIT 2017, Amman, Jordan, pp. 909\u2013913. IEEE, October 2017. https:\/\/doi.org\/10.1109\/ICITECH.2017.8079967","DOI":"10.1109\/ICITECH.2017.8079967"},{"issue":"4","key":"27_CR3","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1088\/1741-2560\/10\/4\/046014","volume":"10","author":"J Asensio-Cubero","year":"2013","unstructured":"Asensio-Cubero, J., Gan, J.Q., Palaniappan, R.: Multiresolution analysis over simple graphs for brain computer interfaces. J. Neural Eng. 10(4), 21\u201326 (2013). https:\/\/doi.org\/10.1088\/1741-2560\/10\/4\/046014","journal-title":"J. Neural Eng."},{"key":"27_CR4","doi-asserted-by":"crossref","unstructured":"Bellman, R.E.: Adaptive Control Processes: A Guided Tour. Princeton University Press (1961)","DOI":"10.1515\/9781400874668"},{"key":"27_CR5","doi-asserted-by":"publisher","unstructured":"Crellin, G.L.: The philosophy and mathematics of bayes\u2019 equation. IEEE Trans. Reliab. $${\\bf R-21}$$, 131\u2013135 (1972). https:\/\/doi.org\/10.1109\/TR.1972.5215975","DOI":"10.1109\/TR.1972.5215975"},{"key":"27_CR6","unstructured":"Cournapeau, D.: Machine learning in Python. https:\/\/scikit-learn.org\/stable\/. Accessed 15 Sept 2020"},{"key":"27_CR7","unstructured":"Chollet, F.: The Python deep learning API. https:\/\/keras.io\/. Accessed 25 Feb 2021"},{"key":"27_CR8","doi-asserted-by":"publisher","unstructured":"Friedl, M.A., Brodley, C.E.: Decision tree classification of land cover from remotely sensed data. Remote Sens. Environ. 61, 399\u2013409 (1997). https:\/\/doi.org\/10.1016\/S0034-4257(97)00049-7","DOI":"10.1016\/S0034-4257(97)00049-7"},{"key":"27_CR9","unstructured":"Google Brain Team: An end-to-end open source machine learning platform. https:\/\/www.tensorflow.org\/. Accessed 25 Feb 2021"},{"key":"27_CR10","doi-asserted-by":"crossref","unstructured":"Gurney, K.: An Introduction to Neural Networks. CRC Press (1997)","DOI":"10.4324\/9780203451519"},{"key":"27_CR11","doi-asserted-by":"publisher","unstructured":"Hengl, T., Nussbaum, M., Wright, M.N., Heuvelink, G., Gr\u00e4ler, B.: Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ 6, e5518 (2018). https:\/\/doi.org\/10.7717\/peerj.5518","DOI":"10.7717\/peerj.5518"},{"key":"27_CR12","first-page":"605","volume":"3","author":"SB Imandoust","year":"2013","unstructured":"Imandoust, S.B., Bolandraftar, M.: Application of k-nearest neighbor (KNN) approach for predicting economic events theoretical background. Int. J. Eng. Res. Appl. 3, 605\u2013610 (2013)","journal-title":"Int. J. Eng. Res. Appl."},{"key":"27_CR13","unstructured":"Joyce, J.: Bayes\u2019 theorem. https:\/\/stanford.library.sydney.edu.au\/archives\/sum2016\/entries\/bayes-theorem\/#4"},{"key":"27_CR14","doi-asserted-by":"publisher","unstructured":"Karamizadeh, S., Abdullah, S.M., Halimi, M., Shayan, J., Rajabi, M.J.: Advantage and drawback of support vector machine functionality. In: 2014 International Conference on Computer, Communications, and Control Technology, Langkawi, Malaysia. I4CT 2014, pp. 63\u201365. IEEE, September 2014. https:\/\/doi.org\/10.1109\/I4CT.2014.6914146","DOI":"10.1109\/I4CT.2014.6914146"},{"key":"27_CR15","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/j.protcy.2016.03.039","volume":"23","author":"SP Krupal","year":"2016","unstructured":"Krupal, S.P., Trupti, P.S.: Support vector machine - a large margin classifier to diagnose skin illnesses. Procedia Technol. 23, 369\u2013375 (2016). https:\/\/doi.org\/10.1016\/j.protcy.2016.03.039","journal-title":"Procedia Technol."},{"key":"27_CR16","doi-asserted-by":"publisher","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature, 436\u2013444 (2015). https:\/\/doi.org\/10.1038\/nature14539","DOI":"10.1038\/nature14539"},{"key":"27_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0234178","volume":"15","author":"J Le\u00f3n","year":"2020","unstructured":"Le\u00f3n, J., et al.: Deep learning for EEG-based motor imagery classification: accuracy-cost trade-off. PLoS ONE 15, 1\u201330 (2020). https:\/\/doi.org\/10.1371\/journal.pone.0234178","journal-title":"PLoS ONE"},{"key":"27_CR18","doi-asserted-by":"publisher","unstructured":"Li, L.L.C.: Research and improvement of a spam filter based on naive bayes. In: 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, China. IHMSC 2015, vol. 2, pp. 361\u2013364. IEEE, November 2015. https:\/\/doi.org\/10.1109\/IHMSC.2015.208","DOI":"10.1109\/IHMSC.2015.208"},{"key":"27_CR19","unstructured":"Milgram, J., Cheriet, M., Sabourin, R.: \u201cone against one\u201d or \u201cone against all\u201d: which one is better for handwriting recognition with SVMS? In: Tenth International Workshop on Frontiers in Handwriting Recognition, La Baule, France. IWFHR 2006. IEEE, October 2006"},{"key":"27_CR20","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","volume":"27","author":"H Peng","year":"2005","unstructured":"Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27, 1226\u20131238 (2005). https:\/\/doi.org\/10.1109\/TPAMI.2005.159","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"27_CR21","doi-asserted-by":"publisher","first-page":"26853","DOI":"10.1080\/01621459.1988.10478680","volume":"81","author":"Y Sakamoto","year":"1986","unstructured":"Sakamoto, Y., Ishiguro, M., Kitagawa, G.: Akaike information criterion statistics. D. Reidel 81, 26853 (1986). https:\/\/doi.org\/10.1080\/01621459.1988.10478680","journal-title":"D. Reidel"},{"key":"27_CR22","doi-asserted-by":"publisher","unstructured":"Shastry, K.A., Sanjay, H.A.: Machine Learning for Bioinformatics, pp. 25\u201339. Springer Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-15-2445-5_3","DOI":"10.1007\/978-981-15-2445-5_3"},{"key":"27_CR23","series-title":"Lecture Notes in Electrical Engineering","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1007\/978-981-15-2317-5_47","volume-title":"InECCE2019","author":"MNAH Sha\u2019abani","year":"2020","unstructured":"Sha\u2019abani, M.N.A.H., Fuad, N., Jamal, N., Ismail, M.F.: kNN and SVM classification for EEG: a review. In: Kasruddin Nasir, A.N., et al. (eds.) InECCE2019. LNEE, vol. 632, pp. 555\u2013565. Springer, Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-15-2317-5_47"},{"key":"27_CR24","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1016\/j.csbj.2017.07.004","volume":"15","author":"Z Yin","year":"2017","unstructured":"Yin, Z., Lan, H., Tan, G., Lu, M., Vasilakos, A.V., Liu, W.: Computing platforms for big biological data analytics: perspectives and challenges. Comput. Struct. Biotechnol. J. 15, 403\u2013411 (2017). https:\/\/doi.org\/10.1016\/j.csbj.2017.07.004","journal-title":"Comput. Struct. Biotechnol. J."}],"container-title":["Lecture Notes in Computer Science","Bioengineering and Biomedical Signal and Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-88163-4_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T15:37:52Z","timestamp":1673451472000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-88163-4_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030881627","9783030881634"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-88163-4_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"9 October 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BIOMESIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Bioengineering and Biomedical Signal and Image Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Meloneras","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 July 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"biomesip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/biomesip.ugr.es\/index.html","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":"121","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":"41","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":"5","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":"34% - 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":"3.1","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":"2.1","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)"}}]}}