{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T16:13:13Z","timestamp":1771258393770,"version":"3.50.1"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,8,5]],"date-time":"2023-08-05T00:00:00Z","timestamp":1691193600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,8,5]],"date-time":"2023-08-05T00:00:00Z","timestamp":1691193600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100008127","name":"Universiti Malaysia Sarawak","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008127","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004768","name":"Universiti Teknikal Malaysia Melaka","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004768","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008127","name":"Universiti Malaysia Sarawak","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100008127","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Brain Inf."],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This paper aims to design distraction descriptor, elicited through the object variation, to refine the granular knowledge incrementally, using the proposed probability-based incremental update strategy in Incremental Fuzzy-Rough Nearest Neighbour (IncFRNN) technique. Most of the brainprint authentication models were tested in well-controlled environments to minimize the influence of ambient disturbance on the EEG signals. These settings significantly contradict the real-world situations. Thus, making use of the distraction is wiser than eliminating it. The proposed probability-based incremental update strategy is benchmarked with the ground truth (actual class) incremental update strategy. Besides, the proposed technique is also benchmarked with First-In-First-Out (FIFO) incremental update strategy in K-Nearest Neighbour (KNN). The experimental results have shown equivalence discriminatory performance in both high distraction and quiet conditions. This has proven that the proposed distraction descriptor is able to utilize the unique EEG response towards ambient distraction to complement person authentication modelling in uncontrolled environment. The proposed probability-based IncFRNN technique has significantly outperformed the KNN technique for both with and without defining the window size threshold. Nevertheless, its performance is slightly worse than the actual class incremental update strategy since the ground truth represents the gold standard. In overall, this study demonstrated a more practical brainprint authentication model with the proposed distraction descriptor and the probability-based incremental update strategy. However, the EEG distraction descriptor may vary due to intersession variability. Future research may focus on the intersession variability to enhance the robustness of the brainprint authentication model.<\/jats:p>","DOI":"10.1186\/s40708-023-00200-z","type":"journal-article","created":{"date-parts":[[2023,8,5]],"date-time":"2023-08-05T06:01:41Z","timestamp":1691215301000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Distraction descriptor for brainprint authentication modelling using probability-based Incremental Fuzzy-Rough Nearest Neighbour"],"prefix":"10.1186","volume":"10","author":[{"given":"Siaw-Hong","family":"Liew","sequence":"first","affiliation":[]},{"given":"Yun-Huoy","family":"Choo","sequence":"additional","affiliation":[]},{"given":"Yin Fen","family":"Low","sequence":"additional","affiliation":[]},{"given":"Fadilla \u2018Atyka","family":"Nor Rashid","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,5]]},"reference":[{"key":"200_CR1","unstructured":"Suraj Z (2004) An introduction to rough set theory and its application. Chair of Computer Science Foundations, University of Information Technology and Management"},{"key":"200_CR2","doi-asserted-by":"crossref","unstructured":"Papadakis SE, Kaburlasos VG, Papakostas GA (2012) Fuzzy Lattice Reasoning (FLR) classifier for human facial expression recognition. In: World Scientific Proceedings Series on Computer Engineering and Information Science. 633\u2013638","DOI":"10.1142\/9789814417747_0101"},{"issue":"March","key":"200_CR3","doi-asserted-by":"publisher","first-page":"22917","DOI":"10.1109\/ACCESS.2023.3253026","volume":"11","author":"CA Fidas","year":"2023","unstructured":"Fidas CA, Lyras D (2023) A review of EEG-based user authentication: trends and future research directions. IEEE Access 11(March):22917\u201322934. https:\/\/doi.org\/10.1109\/ACCESS.2023.3253026","journal-title":"IEEE Access"},{"issue":"3","key":"200_CR4","doi-asserted-by":"publisher","first-page":"27","DOI":"10.25046\/aj060304","volume":"6","author":"MM Hasan","year":"2021","unstructured":"Hasan MM et al (2021) Electroencephalogram based medical biometrics using machine learning: assessment of different color stimuli. Adv Sci Technol Eng Syst J 6(3):27\u201334. https:\/\/doi.org\/10.25046\/aj060304","journal-title":"Adv Sci Technol Eng Syst J"},{"issue":"2","key":"200_CR5","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1049\/iet-bmt.2017.0044","volume":"7","author":"SH Liew","year":"2018","unstructured":"Liew SH, Choo YH, Low YF, Mohd Yusoh ZI (2018) EEG-based biometric authentication modelling using incremental fuzzy-rough nearest neighbour technique. IET Biometrics 7(2):145\u2013152. https:\/\/doi.org\/10.1049\/iet-bmt.2017.0044","journal-title":"IET Biometrics"},{"key":"200_CR6","doi-asserted-by":"publisher","unstructured":"Hasan MM, Sohag MHA, Ahmad M, EEG (2016) Biometrics based on small intra-individual and large inter-individual difference of extracted features. In: 2nd International Conference on Electrical, Computer and Telecommunication Engineering, p 1\u20134. https:\/\/doi.org\/10.1109\/ICECTE.2016.7879629.","DOI":"10.1109\/ICECTE.2016.7879629"},{"issue":"6","key":"200_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3230632","volume":"51","author":"Q Gui","year":"2019","unstructured":"Gui Q, Ruiz-Blondet MV, Laszlo S, Jin Z (2019) A survey on brain biometrics. ACM Comput Surv 51(6):1\u201338. https:\/\/doi.org\/10.1145\/3230632","journal-title":"ACM Comput Surv"},{"key":"200_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/5229576","volume":"2021","author":"S Zhang","year":"2021","unstructured":"Zhang S, Sun L, Mao X, Hu C, Liu P (2021) Review on EEG-based authentication technology. Comput Intell Neurosci 2021:1\u201320. https:\/\/doi.org\/10.1155\/2021\/5229576","journal-title":"Comput Intell Neurosci"},{"key":"200_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cose.2020.101788","volume":"93","author":"A Jalaly Bidgoly","year":"2020","unstructured":"Jalaly Bidgoly A, Jalaly Bidgoly H, Arezoumand Z (2020) A survey on methods and challenges in EEG based authentication. Comput Secur 93:1\u201316. https:\/\/doi.org\/10.1016\/j.cose.2020.101788","journal-title":"Comput Secur"},{"key":"200_CR10","doi-asserted-by":"publisher","first-page":"107118","DOI":"10.1016\/j.comnet.2020.107118","volume":"170","author":"C Wang","year":"2020","unstructured":"Wang C, Wang Y, Chen Y, Liu H, Liu J (2020) User authentication on mobile devices: approaches, threats and trends. Comput Networks 170:107118. https:\/\/doi.org\/10.1016\/j.comnet.2020.107118","journal-title":"Comput Networks"},{"issue":"1","key":"200_CR11","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1109\/JBHI.2014.2328317","volume":"19","author":"V Mihajlovi","year":"2015","unstructured":"Mihajlovi V, Grundlehner B (2015) Wearable, wireless EEG solutions in daily life applications: what are we missing? IEEE J Biomed Heal Inform 19(1):6\u201321","journal-title":"IEEE J Biomed Heal Inform"},{"issue":"6","key":"200_CR12","doi-asserted-by":"publisher","first-page":"958","DOI":"10.1109\/THMS.2017.2682115","volume":"47","author":"S Yang","year":"2017","unstructured":"Yang S, Deravi F (2017) On the usability of electroencephalographic signals for biometric recognition: a survey. IEEE Trans Human-Machine Syst 47(6):958\u2013969. https:\/\/doi.org\/10.1109\/THMS.2017.2682115","journal-title":"IEEE Trans Human-Machine Syst"},{"issue":"1","key":"200_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3372043","volume":"53","author":"O Landau","year":"2020","unstructured":"Landau O, Puzis R, Nissim N (2020) Mind your mind: EEG-based brain-computer interfaces and their security in cyber space. ACM Comput Surv 53(1):1\u201338. https:\/\/doi.org\/10.1145\/3372043","journal-title":"ACM Comput Surv"},{"key":"200_CR14","doi-asserted-by":"crossref","unstructured":"Liew SH, Choo YH, Mohd Yusoh ZI, Low YF (2016) Incrementing FRNN model with simple heuristic update for brainwaves person authentication. In: IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), p 115\u2013120","DOI":"10.1109\/IECBES.2016.7843426"},{"issue":"3","key":"200_CR15","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1049\/iet-bmt.2014.0040","volume":"4","author":"M Abo-Zahhad","year":"2015","unstructured":"Abo-Zahhad M, Ahmed SM, Abbas SN (2015) State-of-the-art methods and future perspectives for personal recognition based on electroencephalogram signals. IET Biometrics 4(3):179\u2013190. https:\/\/doi.org\/10.1049\/iet-bmt.2014.0040","journal-title":"IET Biometrics"},{"key":"200_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fncom.2016.00085","volume":"10","author":"J Virgili-gom\u00e1","year":"2016","unstructured":"Virgili-gom\u00e1 J, Gil R, Guilera T, Batalla I, Soler-gonz\u00e1lez J, Garc\u00eda R (2016) Method for improving EEG based emotion recognition by combining it with synchronized biometric and eye tracking technologies in a non-invasive and low cost way. Front Comput Neurosci 10:1\u201314. https:\/\/doi.org\/10.3389\/fncom.2016.00085","journal-title":"Front Comput Neurosci"},{"key":"200_CR17","doi-asserted-by":"publisher","first-page":"604","DOI":"10.4236\/jbise.2014.78061","volume":"7","author":"S Valenzi","year":"2014","unstructured":"Valenzi S, Islam T, Jurica P, Cichocki A (2014) Individual classification of emotions using EEG. J Biomed Sci Eng 7:604\u2013620. https:\/\/doi.org\/10.4236\/jbise.2014.78061","journal-title":"J Biomed Sci Eng"},{"issue":"January","key":"200_CR18","doi-asserted-by":"publisher","first-page":"897","DOI":"10.1016\/j.neuroimage.2018.08.063","volume":"183","author":"I BabuHenrySamuel","year":"2018","unstructured":"BabuHenrySamuel I, Wang C, Hu Z, Ding M (2018) The frequency of alpha oscillations: task-dependent modulation and its functional significance. Neuroimage 183(January):897\u2013906. https:\/\/doi.org\/10.1016\/j.neuroimage.2018.08.063","journal-title":"Neuroimage"},{"key":"200_CR19","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/S0165-0173(98)00056-3","volume":"29","author":"W Klimesch","year":"1999","unstructured":"Klimesch W (1999) EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Rev 29:169\u2013195. https:\/\/doi.org\/10.1016\/S0165-0173(98)00056-3","journal-title":"Brain Res Rev"},{"key":"200_CR20","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1007\/s004220050457","volume":"79","author":"M Doppelmayr","year":"1998","unstructured":"Doppelmayr M, Klimesch W, Pachinger T, Ripper B (1998) Individual differences in brain dynamics: important implications for the calculation of event-related band power. Biol Cybern 79:49\u201357. https:\/\/doi.org\/10.1007\/s004220050457","journal-title":"Biol Cybern"},{"key":"200_CR21","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/S0301-0511(02)00055-8","volume":"61","author":"CEM Van Beijsterveldt","year":"2002","unstructured":"Van Beijsterveldt CEM, Van Baal GCM (2002) Twin and family studies of the human electroencephalogram: a review and a meta-analysis. Biol Psychol 61:111\u2013138","journal-title":"Biol Psychol"},{"key":"200_CR22","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1016\/j.ijpsycho.2013.05.007","volume":"89","author":"M Gola","year":"2013","unstructured":"Gola M, Magnuski M, Szumska I, Wrobel A (2013) EEG beta band activity is related to attention and attentional deficits in the visual performance of elderly subjects. Int J Psychophysiol 89:334\u2013341","journal-title":"Int J Psychophysiol"},{"key":"200_CR23","unstructured":"Wrobel A (2014) Attentional activation in corticothalamic loops of the visual system. In New Visual Neurosciences, p 339\u2013350"},{"issue":"1","key":"200_CR24","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.ijpsycho.2011.11.006","volume":"85","author":"J Kami\u0144ski","year":"2012","unstructured":"Kami\u0144ski J, Brzezicka A, Gola M, Wr\u00f3bel A (2012) Beta band oscillations engagement in human alertness process. Int J Psychophysiol 85(1):125\u2013128. https:\/\/doi.org\/10.1016\/j.ijpsycho.2011.11.006","journal-title":"Int J Psychophysiol"},{"issue":"4","key":"200_CR25","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1145\/775075.775077","volume":"13","author":"C Giraud-Carrier","year":"2000","unstructured":"Giraud-Carrier C (2000) A note on the utility of incremental learning. AI Commun 13(4):215\u2013223. https:\/\/doi.org\/10.1145\/775075.775077","journal-title":"AI Commun"},{"issue":"5","key":"200_CR26","first-page":"605","volume":"3","author":"SB Imandoust","year":"2013","unstructured":"Imandoust SB, Bolandraftar M (2013) Application of K-Nearest Neighbor (KNN) approach for predicting economic events\u202f: theoretical background. Int J Eng Res Appl 3(5):605\u2013610","journal-title":"Int J Eng Res Appl"},{"key":"200_CR27","doi-asserted-by":"publisher","unstructured":"F\u00f6rster K, Monteleone S, Calatroni A, Roggen D, Tr\u00f6ster G (2010) Incremental KNN classifier exploiting correct-error teacher for activity recognition. In: Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010, p 445\u2013450. https:\/\/doi.org\/10.1109\/ICMLA.2010.72.","DOI":"10.1109\/ICMLA.2010.72"},{"issue":"1","key":"200_CR28","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.ijar.2012.06.008","volume":"54","author":"Y Qu","year":"2013","unstructured":"Qu Y, Shen Q, Mac Parthal\u00e1in N, Shang C, Wu W (2013) Fuzzy similarity-based nearest-neighbour classification as alternatives to their fuzzy-rough parallels. Int J Approx Reason 54(1):184\u2013195. https:\/\/doi.org\/10.1016\/j.ijar.2012.06.008","journal-title":"Int J Approx Reason"},{"issue":"2\u20133","key":"200_CR29","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1080\/03081079008935107","volume":"17","author":"D Dubois","year":"1990","unstructured":"Dubois D, Prade H (1990) Rough fuzzy sets and fuzzy rough sets. Int J Gen Syst 17(2\u20133):191\u2013209","journal-title":"Int J Gen Syst"},{"issue":"4","key":"200_CR30","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1109\/TPAMI.2005.77","volume":"27","author":"J Dong","year":"2005","unstructured":"Dong J, Krzyzak A, Suen CY (2005) Fast SVM training algorithm with decomposition on very large data sets. IEEE Trans Pattern Anal Mach Intell 27(4):603\u2013618","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"200_CR31","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.eswa.2018.10.027","volume":"120","author":"X Wang","year":"2019","unstructured":"Wang X, Xing Y (2019) An online support vector machine for the open-ended environment. Expert Syst Appl 120:72\u201386. https:\/\/doi.org\/10.1016\/j.eswa.2018.10.027","journal-title":"Expert Syst Appl"},{"issue":"6","key":"200_CR32","first-page":"1171","volume":"10","author":"F Han","year":"2012","unstructured":"Han F, Li H, Wen C, Zhao W (2012) A new incremental support vector machine algorithm. J Electr Eng 10(6):1171\u20131178","journal-title":"J Electr Eng"},{"key":"200_CR33","doi-asserted-by":"publisher","unstructured":"Lawal IA (2019) Incremental SVM learning: review. In: Studies in Big Data, vol. 41, Springer, Cham, p 279\u2013296. doi: https:\/\/doi.org\/10.1007\/978-3-319-89803-2_12.","DOI":"10.1007\/978-3-319-89803-2_12"},{"issue":"1","key":"200_CR34","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/s12555-011-0099-1","volume":"11","author":"S Kotsiantis","year":"2013","unstructured":"Kotsiantis S (2013) Increasing the accuracy of incremental Naive Bayes classifier using instance based learning. Int J Control Autom Syst 11(1):159\u2013166. https:\/\/doi.org\/10.1007\/s12555-011-0099-1","journal-title":"Int J Control Autom Syst"},{"issue":"2","key":"200_CR35","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1109\/TKDE.2011.220","volume":"25","author":"H Chen","year":"2013","unstructured":"Chen H, Li T, Ruan D, Lin J, Hu C (2013) A rough-set-based incremental approach for updating approximations under dynamic maintenance environments. IEEE Trans Knowl Data Eng 25(2):274\u2013284","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"42","key":"200_CR36","doi-asserted-by":"publisher","first-page":"5871","DOI":"10.1016\/j.tcs.2011.05.040","volume":"412","author":"R Jensen","year":"2011","unstructured":"Jensen R, Cornelis C (2011) Fuzzy-rough nearest neighbour classification and prediction. Theor Comput Sci 412(42):5871\u20135884","journal-title":"Theor Comput Sci"},{"key":"200_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-398537-8.00008-0","volume-title":"Prediction quality assessment","author":"M Kukar","year":"2014","unstructured":"Kukar M (2014) Prediction quality assessment. Elsevier Inc., Amsterdam. https:\/\/doi.org\/10.1016\/B978-0-12-398537-8.00008-0"},{"key":"200_CR38","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.neuroimage.2014.01.049","volume":"92","author":"S Haegens","year":"2014","unstructured":"Haegens S, Cousijn H, Wallis G, Harrison PJ, Nobre AC (2014) Inter- and intra-individual variability in alpha peak frequency. Neuroimage 92:46\u201355. https:\/\/doi.org\/10.1016\/j.neuroimage.2014.01.049","journal-title":"Neuroimage"},{"issue":"7","key":"200_CR39","doi-asserted-by":"publisher","first-page":"1157","DOI":"10.1016\/j.neurobiolaging.2007.10.003","volume":"30","author":"J Horv\u00e1th","year":"2009","unstructured":"Horv\u00e1th J, Czigler I, Birk\u00e1s E, Winkler I, Gervai J (2009) Age-related differences in distraction and reorientation in an auditory task. Neurobiol Aging 30(7):1157\u20131172. https:\/\/doi.org\/10.1016\/j.neurobiolaging.2007.10.003","journal-title":"Neurobiol Aging"},{"issue":"3","key":"200_CR40","first-page":"254","volume":"46","author":"American Clinical Neurophysiology Society","year":"2008","unstructured":"American Clinical Neurophysiology Society (2008) Guideline 9B: guidelines on visual evoked potentials. Am J Electroneurodiagnostic Technol 46(3):254","journal-title":"Am J Electroneurodiagnostic Technol"},{"key":"200_CR41","unstructured":"Anonymous (2010) Levels of Noise, American Academy of Audiology. https:\/\/audiology-web.s3.amazonaws.com\/migrated\/NoiseChart_Poster-8.5x11.pdf_5399b289427535.32730330.pdf. Accessed 21 Apr 2021"},{"issue":"2","key":"200_CR42","first-page":"1","volume":"2","author":"M Teplan","year":"2002","unstructured":"Teplan M (2002) Fundamentals of EEG measurement. Meas Sci Rev 2(2):1\u201311","journal-title":"Meas Sci Rev"},{"key":"200_CR43","unstructured":"Bos DPO (2007) EEG-based emotion recognition. The influence of visual and auditory stimuli. Psychology."},{"key":"200_CR44","doi-asserted-by":"crossref","unstructured":"Li Y Wong KM (2012) Signal classification by power spectral density: an approach via Riemannian Geometry. In: 2012 IEEE Statistical Signal Processing Workshop (SSP), p 900\u2013903","DOI":"10.1109\/SSP.2012.6319854"},{"key":"200_CR45","doi-asserted-by":"crossref","unstructured":"Nakanishi I, Baba S, Miyamoto C, Wave AB (2009) EEG based biometric authentication using new spectral features. In: International Symposium on Intelligent Signal Processing and Communication Systems, p 651\u2013654","DOI":"10.1109\/ISPACS.2009.5383756"},{"key":"200_CR46","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.brainresbull.2011.06.012","volume":"86","author":"YF Low","year":"2011","unstructured":"Low YF, Strauss DJ (2011) A performance study of the wavelet-phase stability (WPS) in auditory selective attention. Brain Res Bull 86:110\u2013117. https:\/\/doi.org\/10.1016\/j.brainresbull.2011.06.012","journal-title":"Brain Res Bull"},{"issue":"1","key":"200_CR47","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.jneumeth.2007.06.026","volume":"166","author":"R Srinivasan","year":"2007","unstructured":"Srinivasan R, Winter WR, Ding J, Nunez PL (2007) EEG and MEG coherence: measures of functional connectivity at distinct spatial scales of neocortical dynamics. J Neurosci Methods 166(1):41\u201352. https:\/\/doi.org\/10.1016\/j.jneumeth.2007.06.026","journal-title":"J Neurosci Methods"},{"key":"200_CR48","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.bspc.2013.12.003","volume":"10","author":"M-F Shayan","year":"2014","unstructured":"Shayan M-F, Mohamed M-T, Martyn H, Hill CM, White PR (2014) Signal processing techniques applied to human sleep EEG signals\u2014a review. Biomed Signal Process Control 10:21\u201333","journal-title":"Biomed Signal Process Control"},{"key":"200_CR49","unstructured":"Shen S, Chi M (2016) Aim low: correlation-based Feature Selection for model-based reinforcement learning. In: Proceedings of the 9th International Conference on Educational Data Mining, EDM 2016, p 507\u2013512"},{"issue":"03","key":"200_CR50","doi-asserted-by":"publisher","first-page":"152","DOI":"10.4236\/jilsa.2013.53017","volume":"05","author":"Q-G Wang","year":"2013","unstructured":"Wang Q-G, Li X, Qin Q (2013) Feature selection for time series modeling. J Intell Learn Syst Appl 05(03):152\u2013164. https:\/\/doi.org\/10.4236\/jilsa.2013.53017","journal-title":"J Intell Learn Syst Appl"},{"issue":"8","key":"200_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/math11081921","volume":"11","author":"MH Kabir","year":"2023","unstructured":"Kabir MH, Mahmood S, Al Shiam A, Musa Miah AS, Shin J, Molla MKI (2023) Investigating feature selection techniques to enhance the performance of EEG-based motor imagery tasks classification. Mathematics 11(8):1\u201319. https:\/\/doi.org\/10.3390\/math11081921","journal-title":"Mathematics"},{"key":"200_CR52","unstructured":"Hall MA, Smith LA (1999) Feature selection for machine learning\u2014Comparing a correlation-based filter approach to the wrapper. In: International In Proceedings of the twelfth international Florida artificial intelligence research society conference, p 235\u2013239"},{"issue":"8","key":"200_CR53","doi-asserted-by":"publisher","first-page":"10273","DOI":"10.3390\/s130810273","volume":"13","author":"NH Liu","year":"2013","unstructured":"Liu NH, Chiang CY, Chu HC (2013) Recognizing the degree of human attention using EEG signals from mobile sensors. Sensors (Switzerland) 13(8):10273\u201310286. https:\/\/doi.org\/10.3390\/s130810273","journal-title":"Sensors (Switzerland)"},{"key":"200_CR54","doi-asserted-by":"crossref","unstructured":"Geng X, Kate SM (2015) Incremental learning. In: Encyclopedia of Biometrics. Springer US, p 912\u2013917","DOI":"10.1007\/978-1-4899-7488-4_304"},{"key":"200_CR55","doi-asserted-by":"crossref","unstructured":"Hassani K, Lee W (2014) An incremental framework for classification of EEG signals using quantum particle swarm optimization. In: IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), p 40\u201345","DOI":"10.1109\/CIVEMSA.2014.6841436"},{"issue":"8","key":"200_CR56","first-page":"33","volume":"12","author":"AB Hassanat","year":"2014","unstructured":"Hassanat AB, Abbadi MA, Alhasanat AA (2014) Solving the problem of the K parameter in the KNN classifier using an ensemble learning approach. Int J Comput Sci Inf Secur 12(8):33\u201339","journal-title":"Int J Comput Sci Inf Secur"},{"issue":"2","key":"200_CR57","doi-asserted-by":"publisher","first-page":"171","DOI":"10.3923\/jas.2014.171.176","volume":"14","author":"C-M Ma","year":"2014","unstructured":"Ma C-M, Yang W-S, Cheng B-W (2014) How the parameters of K-nearest neighbor algorithm impact on the best classification accuracy-in case of Parkinson dataset. J Appl Sci 14(2):171\u2013176","journal-title":"J Appl Sci"},{"key":"200_CR58","doi-asserted-by":"publisher","unstructured":"Yazdani A, Roodaki A, Rezatofighi SH, Misaghian K, Setarehdan SK, (2008) Fisher linear discriminant based person identification using visual evoked potentials. In: 2008 9th International Conference on Signal Processing, p 1677\u20131680. https:\/\/doi.org\/10.1109\/ICOSP.2008.4697459.","DOI":"10.1109\/ICOSP.2008.4697459"},{"key":"200_CR59","unstructured":"Witten IH, Frank E (2000) WEKA machine learning algorithms in Java. In: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann Publishers, p 265\u2013320."}],"container-title":["Brain Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40708-023-00200-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40708-023-00200-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40708-023-00200-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T17:39:13Z","timestamp":1700329153000},"score":1,"resource":{"primary":{"URL":"https:\/\/braininformatics.springeropen.com\/articles\/10.1186\/s40708-023-00200-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,5]]},"references-count":59,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["200"],"URL":"https:\/\/doi.org\/10.1186\/s40708-023-00200-z","relation":{},"ISSN":["2198-4018","2198-4026"],"issn-type":[{"value":"2198-4018","type":"print"},{"value":"2198-4026","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,5]]},"assertion":[{"value":"20 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 August 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Written informed consent was obtained from each participant before participation in this study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Written informed consent for publication was obtained from each participant before participation in this study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"21"}}