{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,2]],"date-time":"2025-11-02T14:38:40Z","timestamp":1762094320715,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031451690"},{"type":"electronic","value":"9783031451706"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-45170-6_60","type":"book-chapter","created":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T13:03:02Z","timestamp":1699966982000},"page":"577-586","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Fuzzy Rule-Based Approach Towards Cognitive Load Measurement During Mental Task Using fNIRS"],"prefix":"10.1007","author":[{"given":"Subashis","family":"Karmakar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chiranjib","family":"Koley","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aniruddha","family":"Sinha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanjoy Kumar","family":"Saha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tandra","family":"Pal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,4]]},"reference":[{"key":"60_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105535","volume":"195","author":"EA Aydin","year":"2020","unstructured":"Aydin, E.A.: Subject-specific feature selection for near infrared spectroscopy based brain-computer interfaces. Comput. Methods Prog. Biomed. 195, 105535 (2020)","journal-title":"Comput. Methods Prog. Biomed."},{"issue":"11","key":"60_CR2","doi-asserted-by":"publisher","first-page":"4053","DOI":"10.1364\/BOE.5.004053","volume":"5","author":"WB Baker","year":"2014","unstructured":"Baker, W.B., Parthasarathy, A.B., Busch, D.R., Mesquita, R.C., Greenberg, J.H., Yodh, A.G.: Modified beer-lambert law for blood flow. Biomed. Opt. Express 5(11), 4053\u20134075 (2014)","journal-title":"Biomed. Opt. Express"},{"issue":"2\u20133","key":"60_CR3","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/0098-3004(84)90020-7","volume":"10","author":"JC Bezdek","year":"1984","unstructured":"Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(2\u20133), 191\u2013203 (1984)","journal-title":"Comput. Geosci."},{"key":"60_CR4","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/978-3-642-21852-1_2","volume-title":"Foundations of Augmented Cognition. Directing the Future of Adaptive Systems","author":"SC Bunce","year":"2011","unstructured":"Bunce, S.C., Izzetoglu, K., Ayaz, H., Shewokis, P., Izzetoglu, M., Pourrezaei, K., Onaral, B.: Implementation of fNIRS for monitoring levels of expertise and mental workload. In: Schmorrow, D.D., Fidopiastis, C.M. (eds.) FAC 2011. LNCS (LNAI), vol. 6780, pp. 13\u201322. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-21852-1_2"},{"key":"60_CR5","doi-asserted-by":"crossref","unstructured":"De, A., Bhattacharjee, T., Konar, A., Ralescu, A.L., Nagar, A.K.: A type-2 fuzzy set induced classification of cognitive load in inter-individual working memory performance based on hemodynamic response. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1\u20137. IEEE (2017)","DOI":"10.1109\/SSCI.2017.8285186"},{"key":"60_CR6","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.cogsys.2020.05.001","volume":"64","author":"A Hadrani","year":"2020","unstructured":"Hadrani, A., Guennoun, K., Saadane, R., Wahbi, M.: Fuzzy rough sets: survey and proposal of an enhanced knowledge representation model based on automatic noisy sample detection. Cogn. Syst. Res. 64, 37\u201356 (2020). https:\/\/doi.org\/10.1016\/j.cogsys.2020.05.001","journal-title":"Cogn. Syst. Res."},{"key":"60_CR7","doi-asserted-by":"publisher","first-page":"935","DOI":"10.3389\/fnhum.2013.00935","volume":"7","author":"C Herff","year":"2014","unstructured":"Herff, C., Heger, D., Fortmann, O., Hennrich, J., Putze, F., Schultz, T.: Mental workload during n-back task-quantified in the prefrontal cortex using fnirs. Front. Hum. Neurosci. 7, 935 (2014)","journal-title":"Front. Hum. Neurosci."},{"issue":"3","key":"60_CR8","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.neubiorev.2009.07.008","volume":"34","author":"S Lloyd-Fox","year":"2010","unstructured":"Lloyd-Fox, S., Blasi, A., Elwell, C.: Illuminating the developing brain: the past, present and future of functional near infrared spectroscopy. Neurosci. Biobehav. Rev. 34(3), 269\u2013284 (2010)","journal-title":"Neurosci. Biobehav. Rev."},{"issue":"1","key":"60_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12938-018-0613-2","volume":"17","author":"HD Nguyen","year":"2018","unstructured":"Nguyen, H.D., Yoo, S.H., Bhutta, M.R., Hong, K.S.: Adaptive filtering of physiological noises in fNIRS data. Biomed. Eng. Online 17(1), 1\u201323 (2018)","journal-title":"Biomed. Eng. Online"},{"issue":"4","key":"60_CR10","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1109\/TEVC.2003.815377","volume":"7","author":"T Pal","year":"2003","unstructured":"Pal, T., Pal, N.R.: Sogarg: a self-organized genetic algorithm-based rule generation scheme for fuzzy controllers. IEEE Trans. Evol. Comput. 7(4), 397\u2013415 (2003)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"60_CR11","doi-asserted-by":"crossref","unstructured":"Priyono, A., Ridwan, M., Alias, A.J., Rahmat, R.A.O., Hassan, A., Ali, M.A.M.: Generation of fuzzy rules with subtractive clustering. Jurnal Teknologi, 143\u2013153 (2005)","DOI":"10.11113\/jt.v43.782"},{"issue":"6","key":"60_CR12","doi-asserted-by":"publisher","first-page":"1688","DOI":"10.1109\/78.678493","volume":"46","author":"IW Selesnick","year":"1998","unstructured":"Selesnick, I.W., Burrus, C.S.: Generalized digital butterworth filter design. IEEE Trans. Signal Process. 46(6), 1688\u20131694 (1998)","journal-title":"IEEE Trans. Signal Process."},{"issue":"10","key":"60_CR13","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1109\/TNSRE.2016.2628057","volume":"25","author":"J Shin","year":"2017","unstructured":"Shin, J., et al.: Open access dataset for EEG+NIRS single-trial classification. IEEE Trans. Neural Syst. Rehabil. Eng. 25(10), 1735\u20131745 (2017). https:\/\/doi.org\/10.1109\/TNSRE.2016.2628057","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"issue":"1","key":"60_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.3","volume":"5","author":"J Shin","year":"2018","unstructured":"Shin, J., Von L\u00fchmann, A., Kim, D.W., Mehnert, J., Hwang, H.J., M\u00fcller, K.R.: Simultaneous acquisition of EEG and NIRS during cognitive tasks for an open access dataset. Sci. Data 5(1), 1\u201316 (2018)","journal-title":"Sci. Data"},{"key":"60_CR15","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.cogsys.2020.08.007","volume":"64","author":"JP Vasconez","year":"2020","unstructured":"Vasconez, J.P., Viscaino, M., Guevara, L., Cheein, F.A.: A fuzzy-based driver assistance system using human cognitive parameters and driving style information. Cogn. Syst. Res. 64, 174\u2013190 (2020). https:\/\/doi.org\/10.1016\/j.cogsys.2020.08.007","journal-title":"Cogn. Syst. Res."},{"key":"60_CR16","doi-asserted-by":"crossref","unstructured":"Vermeij, A., Van Beek, A.H., Olde Rikkert, M.G., Claassen, J.A., Kessels, R.P.: Effects of aging on cerebral oxygenation during working-memory performance: a functional near-infrared spectroscopy study. Plos One (2012)","DOI":"10.1371\/journal.pone.0046210"},{"issue":"5","key":"60_CR17","doi-asserted-by":"publisher","first-page":"04021016","DOI":"10.1061\/(ASCE)CP.1943-5487.0000984","volume":"35","author":"Q Zhu","year":"2021","unstructured":"Zhu, Q., Shi, Y., Du, J.: Wayfinding information cognitive load classification based on functional near-infrared spectroscopy. J. Comput. Civ. Eng. 35(5), 04021016 (2021)","journal-title":"J. Comput. Civ. Eng."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-45170-6_60","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T13:09:19Z","timestamp":1699967359000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-45170-6_60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031451690","9783031451706"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-45170-6_60","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"4 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PReMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition and Machine Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"premi2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.isical.ac.in\/~premi23\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EquinOCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"311","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":"91","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":"29% - 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","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":"3","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)"}}]}}