{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T17:52:18Z","timestamp":1760205138398,"version":"3.37.3"},"reference-count":80,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T00:00:00Z","timestamp":1626739200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T00:00:00Z","timestamp":1626739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100010016","name":"Nottingham Trent University","doi-asserted-by":"publisher","award":["PhD studentship 2019"],"award-info":[{"award-number":["PhD studentship 2019"]}],"id":[{"id":"10.13039\/100010016","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002888","name":"Beijing Municipal Commission of Education","doi-asserted-by":"publisher","award":["KM201710005026"],"award-info":[{"award-number":["KM201710005026"]}],"id":[{"id":"10.13039\/501100002888","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Basic Research Program of China","doi-asserted-by":"publisher","award":["2014CB744600"],"award-info":[{"award-number":["2014CB744600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","award":["4182005"],"award-info":[{"award-number":["4182005"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Brain Inf."],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Neuronal signals generally represent activation of the neuronal networks and give insights into brain functionalities. They are considered as fingerprints of actions and their processing across different structures of the brain. These recordings generate a large volume of data that are susceptible to noise and artifacts. Therefore, the review of these data to ensure high quality by automatically detecting and removing the artifacts is imperative. Toward this aim, this work proposes a custom-developed automatic artifact removal toolbox named, SANTIA (SigMate Advanced: a Novel Tool for Identification of Artifacts in Neuronal Signals). Developed in Matlab, SANTIA is an open-source toolbox that applies neural network-based machine learning techniques to label and train models to detect artifacts from the invasive neuronal signals known as local field potentials.<\/jats:p>","DOI":"10.1186\/s40708-021-00135-3","type":"journal-article","created":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T15:31:15Z","timestamp":1626795075000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["SANTIA: a Matlab-based open-source toolbox for artifact detection and removal from extracellular neuronal signals"],"prefix":"10.1186","volume":"8","author":[{"given":"Marcos","family":"Fabietti","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2037-8348","authenticated-orcid":false,"given":"Mufti","family":"Mahmud","sequence":"additional","affiliation":[]},{"given":"Ahmad","family":"Lotfi","sequence":"additional","affiliation":[]},{"given":"M. Shamim","family":"Kaiser","sequence":"additional","affiliation":[]},{"given":"Alberto","family":"Averna","sequence":"additional","affiliation":[]},{"given":"David J.","family":"Guggenmos","sequence":"additional","affiliation":[]},{"given":"Randolph J.","family":"Nudo","sequence":"additional","affiliation":[]},{"given":"Michela","family":"Chiappalone","sequence":"additional","affiliation":[]},{"given":"Jianhui","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,20]]},"reference":[{"issue":"1","key":"135_CR1","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.jneumeth.2003.10.009","volume":"134","author":"A Delorme","year":"2004","unstructured":"Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134(1):9\u201321","journal-title":"J Neurosci Methods"},{"issue":"1","key":"135_CR2","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/S0165-0270(02)00045-6","volume":"117","author":"U Egert","year":"2002","unstructured":"Egert U, Knott T, Schwarz C, Nawrot M, Brandt A, Rotter S, Diesmann M (2002) MEA-tools: an open source toolbox for the analysis of multi-electrode data with MATLAB. J Neurosci Methods 117(1):33\u201342","journal-title":"J Neurosci Methods"},{"key":"135_CR3","doi-asserted-by":"publisher","first-page":"34518","DOI":"10.7554\/eLife.34518","volume":"7","author":"P Yger","year":"2018","unstructured":"Yger P, Spampinato GL, Esposito E, Lefebvre B, Deny S, Gardella C, Stimberg M, Jetter F, Zeck G, Picaud S et al (2018) A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo. Elife 7:34518","journal-title":"Elife"},{"key":"135_CR4","doi-asserted-by":"publisher","first-page":"57","DOI":"10.3389\/fninf.2019.00057","volume":"13","author":"VA Unakafova","year":"2019","unstructured":"Unakafova VA, Gail A (2019) Comparing open-source toolboxes for processing and analysis of spike and local field potentials data. Front Neuroinform 13:57","journal-title":"Front Neuroinform"},{"key":"135_CR5","doi-asserted-by":"publisher","DOI":"10.1155\/2011\/879716","author":"F Tadel","year":"2011","unstructured":"Tadel F, Baillet S, Mosher JC, Pantazis D, Leahy RM (2011) Brainstorm: a user-friendly application for MEG\/EEG analysis. Comput Intell Neurosci. https:\/\/doi.org\/10.1155\/2011\/879716","journal-title":"Comput Intell Neurosci"},{"issue":"8","key":"135_CR6","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.1016\/j.neunet.2008.05.007","volume":"21","author":"J Cui","year":"2008","unstructured":"Cui J, Xu L, Bressler SL, Ding M, Liang H (2008) BSMART: a Matlab\/C toolbox for analysis of multichannel neural time series. Neural Netw 21(8):1094\u20131104","journal-title":"Neural Netw"},{"issue":"1","key":"135_CR7","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.jneumeth.2010.06.020","volume":"192","author":"H Bokil","year":"2010","unstructured":"Bokil H, Andrews P, Kulkarni JE, Mehta S, Mitra PP (2010) Chronux: a platform for analyzing neural signals. J Neurosci Methods 192(1):146\u2013151","journal-title":"J Neurosci Methods"},{"key":"135_CR8","unstructured":"Yegenoglu A et al (2015) Elephant\u2014open-source tool for the analysis of electrophysiological data sets. In: Proc. Bernstein conference, pp 134\u2013135"},{"key":"135_CR9","doi-asserted-by":"publisher","DOI":"10.1155\/2011\/156869","author":"R Oostenveld","year":"2011","unstructured":"Oostenveld R, Fries P, Maris E, Schoffelen J-M (2011) FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci. https:\/\/doi.org\/10.1155\/2011\/156869","journal-title":"Comput Intell Neurosci"},{"issue":"2","key":"135_CR10","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1016\/j.jneumeth.2006.01.017","volume":"155","author":"L Hazan","year":"2006","unstructured":"Hazan L, Zugaro M, Buzs\u00e1ki G (2006) Klusters, NeuroScope, NDManager: a free software suite for neurophysiological data processing and visualization. J Neurosci Methods 155(2):207\u2013216","journal-title":"J Neurosci Methods"},{"key":"135_CR11","doi-asserted-by":"publisher","first-page":"10","DOI":"10.3389\/fninf.2014.00010","volume":"8","author":"S Garcia","year":"2014","unstructured":"Garcia S, Guarino D, Jaillet F, Jennings TR, Pr\u00f6pper R, Rautenberg PL, Rodgers C, Sobolev A, Wachtler T, Yger P et al (2014) Neo: an object model for handling electrophysiology data in multiple formats. Front Neuroinform 8:10","journal-title":"Front Neuroinform"},{"key":"135_CR12","doi-asserted-by":"publisher","first-page":"196","DOI":"10.12688\/wellcomeopenres.15533.1","volume":"4","author":"MN Islam","year":"2019","unstructured":"Islam MN, Martin SK, Aggleton JP, O\u2019Mara SM (2019) NeuroChaT: a toolbox to analyse the dynamics of neuronal encoding in freely-behaving rodents in vivo. Wellcome Open Res 4:196","journal-title":"Wellcome Open Res"},{"issue":"6","key":"135_CR13","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1016\/j.neunet.2010.05.002","volume":"23","author":"LL Bologna","year":"2010","unstructured":"Bologna LL et al (2010) Investigating neuronal activity by SPYCODE multi-channel data analyzer. Neural Netw 23(6):685\u2013697","journal-title":"Neural Netw"},{"issue":"4","key":"135_CR14","doi-asserted-by":"publisher","first-page":"2062","DOI":"10.1007\/978-3-642-03882-2_547","volume":"25","author":"M Mahmud","year":"2009","unstructured":"Mahmud M, Girardi S, Maschietto M, Rahman MM, Bertoldo A, Vassanelli S (2009) Slow stimulus artifact removal through peak-valley detection of neuronal signals recorded from somatosensory cortex by high resolution brain\u2013chip interface. IFMBE Proc 25(4):2062\u20132065","journal-title":"IFMBE Proc"},{"key":"135_CR15","doi-asserted-by":"crossref","unstructured":"Mahmud M, Bertoldo A, Girardi S, Maschietto M, Vassanelli S (2010) Sigmate: a MATLAB-based neuronal signal processing tool. In: Proc. IEEE EMBC, pp 1352\u20131355","DOI":"10.1109\/IEMBS.2010.5626747"},{"key":"135_CR16","doi-asserted-by":"publisher","unstructured":"Mahmud M, Bertoldo A, Girardi S, Maschietto M, Pasqualotto E, Vassanelli S (2011) SigMate: a comprehensive software package for extracellular neuronal signal processing and analysis. In: Proc. NER, pp 88\u201391. https:\/\/doi.org\/10.1109\/NER.2011.5910495","DOI":"10.1109\/NER.2011.5910495"},{"issue":"1","key":"135_CR17","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.jneumeth.2012.03.009","volume":"207","author":"M Mahmud","year":"2012","unstructured":"Mahmud M, Bertoldo A, Girardi S, Maschietto M, Vassanelli S (2012) SigMate: a Matlab-based automated tool for extracellular neuronal signal processing and analysis. J Neurosci Methods 207(1):97\u2013112. https:\/\/doi.org\/10.1016\/j.jneumeth.2012.03.009","journal-title":"J Neurosci Methods"},{"key":"135_CR18","doi-asserted-by":"publisher","unstructured":"Mahmud M, Girardi S, Maschietto M, Vassanelli S (2012) An automated method to remove artifacts induced by microstimulation in local field potentials recorded from rat somatosensory cortex. In: Proc. BRC, pp 1\u20134. https:\/\/doi.org\/10.1109\/BRC.2012.6222169","DOI":"10.1109\/BRC.2012.6222169"},{"key":"135_CR19","doi-asserted-by":"publisher","unstructured":"Mahmud M, Girardi S, Maschietto M, Pasqualotto E, Vassanelli S (2011) An automated method to determine angular preferentiality using LFPs recorded from rat barrel cortex by brain\u2013chip interface under mechanical whisker stimulation. In: Proc. EMBC, pp 2307\u20132310. https:\/\/doi.org\/10.1109\/IEMBS.2011.6090580","DOI":"10.1109\/IEMBS.2011.6090580"},{"key":"135_CR20","unstructured":"Mahmud M, Girardi S, Maschietto M, Rahman MM, Vassanelli S (2009) Noise characterization of electrophysiological signals recorded from high resolution brain\u2013chip interface. In: Proc. ISBB, pp 84\u201387"},{"key":"135_CR21","doi-asserted-by":"publisher","unstructured":"Mahmud M, Bertoldo A, Maschietto M, Girardi S, Vassanelli S (2010) Automatic detection of layer activation order in information processing pathways of rat barrel cortex under mechanical whisker stimulation. In: Proc. EMBC, pp 6095\u20136098. https:\/\/doi.org\/10.1109\/IEMBS.2010.5627639","DOI":"10.1109\/IEMBS.2010.5627639"},{"key":"135_CR22","doi-asserted-by":"publisher","unstructured":"Mahmud M, Maschietto M, Girardi S, Vassanelli S (2012) A Matlab based tool for cortical layer activation order detection through latency calculation in local field potentials recorded from rat barrel cortex by brain\u2013chip interface. In: Proc. BRC, pp 1\u20134. https:\/\/doi.org\/10.1109\/BRC.2012.6222170","DOI":"10.1109\/BRC.2012.6222170"},{"issue":"1","key":"135_CR23","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.jneumeth.2010.11.024","volume":"196","author":"M Mahmud","year":"2011","unstructured":"Mahmud M, Pasqualotto E, Bertoldo A, Girardi S, Maschietto M, Vassanelli S (2011) An automated method for detection of layer activation order in information processing pathway of rat barrel cortex under mechanical whisker stimulation. J Neurosci Methods 196(1):141\u2013150. https:\/\/doi.org\/10.1016\/j.jneumeth.2010.11.024","journal-title":"J Neurosci Methods"},{"issue":"5","key":"135_CR24","doi-asserted-by":"publisher","first-page":"935","DOI":"10.1007\/s12559-016-9399-3","volume":"8","author":"M Mahmud","year":"2016","unstructured":"Mahmud M et al (2016) An automated method for characterization of evoked single-trial local field potentials recorded from rat barrel cortex under mechanical whisker stimulation. Cogn Comput 8(5):935\u2013945. https:\/\/doi.org\/10.1007\/s12559-016-9399-3","journal-title":"Cogn Comput"},{"key":"135_CR25","doi-asserted-by":"publisher","unstructured":"Mahmud M, Travalin D, Bertoldo A, Girardi S, Maschietto M, Vassanelli S (2010) A contour based automatic method to classify local field potentials recorded from rat barrel cortex. In: Proc. CIBEC, pp 163\u2013166. https:\/\/doi.org\/10.1109\/CIBEC.2010.5716087","DOI":"10.1109\/CIBEC.2010.5716087"},{"key":"135_CR26","doi-asserted-by":"publisher","unstructured":"Mahmud M, Travalin D, Hussain A, Girardi S, Maschietto M, Felderer F, Vassanelli S (2012) Single LFP sorting for high-resolution brain-chip interfacing. In Proc. BICS, 7366 LNAI, pp 329\u2013337. https:\/\/doi.org\/10.1007\/978-3-642-31561-9_37","DOI":"10.1007\/978-3-642-31561-9_37"},{"key":"135_CR27","doi-asserted-by":"publisher","unstructured":"Mahmud M, Travalin D, Hussain A (2012) Decoding network activity from LFPS: a computational approach. In: Proc. ICONIP, 7663 LNCS, pp 584\u2013591. https:\/\/doi.org\/10.1007\/978-3-642-34475-6_70","DOI":"10.1007\/978-3-642-34475-6_70"},{"key":"135_CR28","doi-asserted-by":"publisher","DOI":"10.5405\/jmbe.923","author":"M Mahmud","year":"2012","unstructured":"Mahmud M, Travalin D, Bertoldo A, Girardi S, Maschietto M, Vassanelli S (2012) An automated classification method for single sweep local field potentials recorded from rat barrel cortex under mechanical whisker stimulation. J Med Biol Eng. https:\/\/doi.org\/10.5405\/jmbe.923","journal-title":"J Med Biol Eng"},{"key":"135_CR29","doi-asserted-by":"crossref","unstructured":"Fabietti M, Mahmud M, Lotfi A (2020) Machine learning in analysing invasively recorded neuronal signals: available open access data sources. In: Proc. brain informatics, pp 151\u2013162","DOI":"10.1007\/978-3-030-59277-6_14"},{"key":"135_CR30","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1007\/978-1-4614-6675-8_782","volume-title":"Encyclopedia of computational neuroscience","author":"A Destexhe","year":"2013","unstructured":"Destexhe A, Goldberg JA (2013)\u00a0LFP analysis: overview.\u00a0In: Jaeger D, Jung R (eds) Encyclopedia of computational neuroscience. Springer, New York, NY, pp 52\u201355.\u00a0https:\/\/doi.org\/10.1007\/978-1-4614-6675-8_782"},{"key":"135_CR31","doi-asserted-by":"publisher","first-page":"108485","DOI":"10.1016\/j.jneumeth.2019.108485","volume":"330","author":"KB Boroujeni","year":"2020","unstructured":"Boroujeni KB, Tiesinga P, Womelsdorf T (2020) Adaptive spike-artifact removal from local field potentials uncovers prominent beta and gamma band neuronal synchronization. J Neurosci Methods 330:108485","journal-title":"J Neurosci Methods"},{"issue":"1","key":"135_CR32","doi-asserted-by":"publisher","first-page":"015002","DOI":"10.1117\/1.NPh.3.1.015002","volume":"3","author":"S Mikulovic","year":"2016","unstructured":"Mikulovic S, Pupe S, Peixoto HM, Do Nascimento GC, Kullander K, Tort AB, Le\u00e3o RN (2016) On the photovoltaic effect in local field potential recordings. Neurophotonics 3(1):015002","journal-title":"Neurophotonics"},{"issue":"1","key":"135_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-24629-z","volume":"8","author":"AB Tort","year":"2018","unstructured":"Tort AB, Ponsel S, Jessberger J, Yanovsky Y, Branka\u010dk J, Draguhn A (2018) Parallel detection of theta and respiration-coupled oscillations throughout the mouse brain. Sci Rep 8(1):1\u201314","journal-title":"Sci Rep"},{"issue":"12","key":"135_CR34","doi-asserted-by":"publisher","first-page":"2217","DOI":"10.1109\/TNSRE.2016.2613412","volume":"25","author":"X Qian","year":"2016","unstructured":"Qian X, Chen Y, Feng Y, Ma B, Hao H, Li L (2016) A method for removal of deep brain stimulation artifact from local field potentials. IEEE Trans Neural Syst Rehabilitat Eng 25(12):2217\u20132226","journal-title":"IEEE Trans Neural Syst Rehabilitat Eng"},{"key":"135_CR35","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1016\/j.cnp.2018.03.002","volume":"3","author":"J Brogger","year":"2018","unstructured":"Brogger J, Eichele T, Aanestad E, Olberg H, Hjelland I, Aurlien H (2018) Visual EEG reviewing times with score EEG. Clin Neurophysiol Pract 3:59\u201364","journal-title":"Clin Neurophysiol Pract"},{"key":"135_CR36","doi-asserted-by":"publisher","first-page":"105613","DOI":"10.1016\/j.asoc.2019.105613","volume":"83","author":"SW Yahaya","year":"2019","unstructured":"Yahaya SW, Lotfi A, Mahmud M (2019) A consensus novelty detection ensemble approach for anomaly detection in activities of daily living. Appl Soft Comput 83:105613","journal-title":"Appl Soft Comput"},{"key":"135_CR37","doi-asserted-by":"crossref","unstructured":"Fabietti M, Mahmud M, Lotfi A, Averna A, Guggenmo D, Nudo R, Chiappalone M (2020) Neural network-based artifact detection in local field potentials recorded from chronically implanted neural probes. In: Proc. IJCNN, pp 1\u20138","DOI":"10.1109\/IJCNN48605.2020.9207320"},{"key":"135_CR38","doi-asserted-by":"crossref","unstructured":"Fabietti M, Mahmud M, Lotfi A, Averna A, Guggenmos D, Nudo R, Chiappalone M (2020) Adaptation of convolutional neural networks for multi-channel artifact detection in chronically recorded local field potentials. In: 2020 IEEE symposium series on computational intelligence (SSCI). IEEE, pp 1607\u20131613","DOI":"10.1109\/SSCI47803.2020.9308165"},{"key":"135_CR39","doi-asserted-by":"crossref","unstructured":"Fabietti M, Mahmud M, Lotfi A (2020) Effectiveness of employing multimodal signals in removing artifacts from neuronal signals: an empirical analysis. In: Proc. brain informatics. Springer, pp 183\u2013193","DOI":"10.1007\/978-3-030-59277-6_17"},{"key":"135_CR40","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.patrec.2021.02.006","volume":"145","author":"SW Yahaya","year":"2021","unstructured":"Yahaya SW, Lotfi A, Mahmud M (2021) Towards a data-driven adaptive anomaly detection system for human activity. Pattern Recogn Lett 145:200\u2013207","journal-title":"Pattern Recogn Lett"},{"key":"135_CR41","doi-asserted-by":"crossref","unstructured":"Farhin F, Sultana I, Islam N, Kaiser MS, Rahman MS, Mahmud M (2020) Attack detection in internet of things using software defined network and fuzzy neural network. In: 2020 joint 9th international conference on informatics, electronics & vision (ICIEV) and 2020 4th international conference on imaging, vision & pattern recognition (icIVPR). IEEE, pp 1\u20136","DOI":"10.1109\/ICIEVicIVPR48672.2020.9306666"},{"key":"135_CR42","doi-asserted-by":"crossref","unstructured":"Zaman S, Alhazmi K, Aseeri M, Ahmed MR, Khan RT, Kaiser MS, Mahmud M (2021) Security threats and artificial intelligence based countermeasures for internet of things networks: a comprehensive survey. IEEE Access","DOI":"10.1109\/ACCESS.2021.3089681"},{"key":"135_CR43","doi-asserted-by":"crossref","unstructured":"Fabietti M, Mahmud M, Lotfi A, Averna A, Guggenmos D, Nudo R, Chiappalone M (2021) Signal power affects artefact detection accuracy in chronically recorded local field potentials: preliminary results. In: 2021 10th international IEEE\/EMBS conference on neural engineering (NER). IEEE, pp 166\u2013169","DOI":"10.1109\/NER49283.2021.9441403"},{"key":"135_CR44","doi-asserted-by":"crossref","unstructured":"Tahura S, Samiul SH, Kaiser MS, Mahmud M (2021) Anomaly detection in electroencephalography signal using deep learning model. In: Proceedings of international conference on trends in computational and cognitive engineering. Springer, pp 205\u2013217","DOI":"10.1007\/978-981-33-4673-4_18"},{"issue":"6","key":"135_CR45","doi-asserted-by":"publisher","first-page":"2063","DOI":"10.1109\/TNNLS.2018.2790388","volume":"29","author":"M Mahmud","year":"2018","unstructured":"Mahmud M, Kaiser MS, Hussain A, Vassanelli S (2018) Applications of deep learning and reinforcement learning to biological data. IEEE Trans Neural Netw Learn Syst 29(6):2063\u20132079. https:\/\/doi.org\/10.1109\/TNNLS.2018.2790388","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"1","key":"135_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12559-020-09773-x","volume":"13","author":"M Mahmud","year":"2021","unstructured":"Mahmud M, Kaiser MS, McGinnity MT, Hussain A (2021) Deep learning in mining biological data. Cogn Comput 13(1):1\u201333. https:\/\/doi.org\/10.1007\/s12559-020-09773-x","journal-title":"Cogn Comput"},{"key":"135_CR47","doi-asserted-by":"crossref","unstructured":"Noor MBT, Zenia NZ, Kaiser MS, Mahmud M, Al\u00a0Mamun S (2019) Detecting neurodegenerative disease from MRI: a brief review on a deep learning perspective. In: Proc. brain informatics, pp 115\u2013125","DOI":"10.1007\/978-3-030-37078-7_12"},{"key":"135_CR48","doi-asserted-by":"crossref","unstructured":"Miah Y, Prima CNE, Seema SJ, Mahmud M, Kaiser MS (2021) Performance comparison of machine learning techniques in identifying dementia from open access clinical datasets. In: Proc. ICACIn, pp 79\u201389","DOI":"10.1007\/978-981-15-6048-4_8"},{"key":"135_CR49","doi-asserted-by":"crossref","unstructured":"Zohora MF, Tania MH, Kaiser MS, Mahmud M (2020) Forecasting the risk of type II diabetes using reinforcement learning. In: Proc. ICIEV. IEEE, pp 1\u20136","DOI":"10.1109\/ICIEVicIVPR48672.2020.9306653"},{"key":"135_CR50","doi-asserted-by":"crossref","unstructured":"Sharpe R, Mahmud M (2020) Effect of the gamma entrainment frequency in pertinence to mood, memory and cognition. In: Proc. brain informatics, pp 50\u201361","DOI":"10.1007\/978-3-030-59277-6_5"},{"key":"135_CR51","doi-asserted-by":"crossref","unstructured":"Satu MS, Rahman S, Khan MI, Abedin MZ, Kaiser MS, Mahmud M (2020) Towards improved detection of cognitive performance using bidirectional multilayer long-short term memory neural network. In: Proc. brain informatics, pp 297\u2013306","DOI":"10.1007\/978-3-030-59277-6_27"},{"key":"135_CR52","doi-asserted-by":"crossref","unstructured":"Rahman S, Sharma T, Mahmud M (2020) Improving alcoholism diagnosis: comparing instance-based classifiers against neural networks for classifying EEG signal. In: Proc. brain informatics, pp 239\u2013250","DOI":"10.1007\/978-3-030-59277-6_22"},{"key":"135_CR53","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40708-020-00112-2","volume":"7","author":"MBT Noor","year":"2020","unstructured":"Noor MBT, Zenia NZ, Kaiser MS, Al Mamun S, Mahmud M (2020) Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer\u2019s disease, Parkinson\u2019s disease and schizophrenia. Brain Inform 7:1\u201321","journal-title":"Brain Inform"},{"key":"135_CR54","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-020-09774-w","author":"VNM Aradhya","year":"2021","unstructured":"Aradhya VNM, Mahmud M, Guru DS, Agarwal B, Kaiser MS (2021) One shot cluster based approach for the detection of COVID-19 from chest X-ray images. Cogn Comput. https:\/\/doi.org\/10.1007\/s12559-020-09774-w","journal-title":"Cogn Comput"},{"issue":"5","key":"135_CR55","doi-asserted-by":"publisher","first-page":"1011","DOI":"10.1007\/s12559-020-09751-3","volume":"12","author":"N Dey","year":"2020","unstructured":"Dey N, Rajinikanth V, Fong SJ, Kaiser MS, Mahmud M (2020) Social group optimization-assisted Kapur\u2019s entropy and morphological segmentation for automated detection of covid-19 infection from computed tomography images. Cogn Comput 12(5):1011\u20131023","journal-title":"Cogn Comput"},{"key":"135_CR56","doi-asserted-by":"crossref","unstructured":"Al\u00a0Banna MH, Ghosh T, Taher KA, Kaiser MS, Mahmud M (2020) A monitoring system for patients of autism spectrum disorder using artificial intelligence. In: Proc. brain informatics, pp 251\u2013262","DOI":"10.1007\/978-3-030-59277-6_23"},{"key":"135_CR57","doi-asserted-by":"crossref","unstructured":"Sumi AI, Zohora MF, Mahjabeen M, Faria TJ, Mahmud M, Kaiser MS (2018) fASSERT: a fuzzy assistive system for children with autism using internet of things. In: Proc. brain informatics, pp 403\u2013412","DOI":"10.1007\/978-3-030-05587-5_38"},{"key":"135_CR58","doi-asserted-by":"crossref","unstructured":"Tonni SI, Aka TA, Antik MM, Taher KA, Mahmud M, Kaiser MS (2021) Artificial intelligence based driver vigilance system for accident prevention. In: 2021 international conference on information and communication technology for sustainable development (ICICT4SD). IEEE, pp 412\u2013416","DOI":"10.1109\/ICICT4SD50815.2021.9396916"},{"key":"135_CR59","doi-asserted-by":"crossref","unstructured":"Al\u00a0Nahian MJ, Ghosh T, Uddin MN, Islam MM, Mahmud M, Kaiser M (2020) Towards artificial intelligence driven emotion aware fall monitoring framework suitable for elderly people with neurological disorder. In: Proc. brain informatics, pp 275\u2013286","DOI":"10.1007\/978-3-030-59277-6_25"},{"key":"135_CR60","doi-asserted-by":"crossref","unstructured":"Jesmin S, Kaiser MS, Mahmud M (2020) Artificial and internet of healthcare things based Alzheimer care during COVID 19. In: Proc. brain informatics, pp 263\u2013274","DOI":"10.1007\/978-3-030-59277-6_24"},{"key":"135_CR61","doi-asserted-by":"crossref","unstructured":"Nahiduzzaman M, Tasnim M, Newaz NT, Kaiser MS, Mahmud M (2020) Machine learning based early fall detection for elderly people with neurological disorder using multimodal data fusion. In: Proc. brain informatics, pp 204\u2013214","DOI":"10.1007\/978-3-030-59277-6_19"},{"key":"135_CR62","doi-asserted-by":"publisher","first-page":"13814","DOI":"10.1109\/ACCESS.2021.3050193","volume":"9","author":"MS Kaiser","year":"2021","unstructured":"Kaiser MS, Mahmud M, Noor MBT, Zenia NZ, Al Mamun S, Mahmud KA, Azad S, Aradhya VM, Stephan P, Stephan T et al (2021) iWorksafe: towards healthy workplaces during COVID-19 with an intelligent phealth app for industrial settings. IEEE Access 9:13814\u201313828","journal-title":"IEEE Access"},{"key":"135_CR63","doi-asserted-by":"crossref","unstructured":"Orojo O, Tepper J, McGinnity TM, Mahmud M (2019) A multi-recurrent network for crude oil price prediction. In: Proc. IEEE SSCI, pp 2953\u20132958","DOI":"10.1109\/SSCI44817.2019.9002841"},{"key":"135_CR64","doi-asserted-by":"crossref","unstructured":"Ali HM, Kaiser MS, Mahmud M (2019) Application of convolutional neural network in segmenting brain regions from MRI data. In: International conference on brain informatics, pp. 136\u2013146","DOI":"10.1007\/978-3-030-37078-7_14"},{"key":"135_CR65","unstructured":"Ruiz J, Mahmud M, Modasshir M, Shamim Kaiser M (2020) Alzheimer\u2019s disease neuroimaging initiative, f.t.: 3D DenseNet ensemble in 4-way classification of Alzheimer\u2019s disease. In: Proc. brain informatics, pp 85\u201396"},{"key":"135_CR66","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1007\/s12559-019-09706-3","volume":"12","author":"G Rabby","year":"2020","unstructured":"Rabby G, Azad S, Mahmud M, Zamli KZ, Rahman MM (2020) Teket: a tree-based unsupervised keyphrase extraction technique. Cogn Comput 12:811\u2013833","journal-title":"Cogn Comput"},{"key":"135_CR67","doi-asserted-by":"crossref","unstructured":"Watkins J, Fabietti M, Mahmud M (2020) Sense: a student performance quantifier using sentiment analysis. In: Proc. IJCNN, pp 1\u20136","DOI":"10.1109\/IJCNN48605.2020.9207721"},{"issue":"1","key":"135_CR68","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40708-020-00121-1","volume":"7","author":"L Chen","year":"2020","unstructured":"Chen L, Yan J, Chen J, Sheng Y, Xu Z, Mahmud M (2020) An event based topic learning pipeline for neuroimaging literature mining. Brain Inform 7(1):1\u201314","journal-title":"Brain Inform"},{"issue":"1","key":"135_CR69","doi-asserted-by":"publisher","first-page":"14","DOI":"10.15761\/TBR.1000104","volume":"1","author":"O Bukhtiyarova","year":"2016","unstructured":"Bukhtiyarova O, Soltani S, Chauvette S, Timofeev I (2016) Supervised semi-automatic detection of slow waves in non-anaesthetized mice with the use of neural network approach. Transl Brain Rhythmicity 1(1):14\u201318","journal-title":"Transl Brain Rhythmicity"},{"key":"135_CR70","doi-asserted-by":"publisher","first-page":"11218","DOI":"10.1016\/B0-08-043076-7\/00572-6","volume-title":"International encyclopedia of the social & behavioral sciences","author":"LN Kanal","year":"2001","unstructured":"Kanal LN (2001) Perceptrons. In: Smelser NJ, Baltes PB (eds) International encyclopedia of the social & behavioral sciences. Pergamon, Oxford, pp 11218\u201311221. https:\/\/doi.org\/10.1016\/B0-08-043076-7\/00572-6"},{"key":"135_CR71","doi-asserted-by":"crossref","unstructured":"Fabietti M, Mahmud M, Lotfi A, Averna A, Guggenmo D, Nudo R, Chiappalone M (2020) Artifact detection in chronically recorded local field potentials using long-short term memory neural network. In: Proc. AICT, pp 1\u20136","DOI":"10.1109\/AICT50176.2020.9368638"},{"key":"135_CR72","doi-asserted-by":"crossref","unstructured":"Fabietti M, Mahmud M, Lotfi A, Averna A, Guggenmo D, Nudo R, Chiappalone M (2020) Adaptation of convolutional neural networks for multi-channel artifact detection in chronically recorded local field potentials. In: Proc. SSCI, pp 1\u20137","DOI":"10.1109\/SSCI47803.2020.9308165"},{"issue":"2","key":"135_CR73","first-page":"59","volume":"1","author":"V Arulmozhi","year":"2011","unstructured":"Arulmozhi V (2011) Classification task by using matlab neural network tool box\u2014a beginner\u2019s view. Int J Wisdom Based Comput 1(2):59\u201360","journal-title":"Int J Wisdom Based Comput"},{"key":"135_CR74","doi-asserted-by":"publisher","DOI":"10.7910\/DVN\/MIBZLZ","author":"K Furth","year":"2017","unstructured":"Furth K (2017) Replication Data for: neuronal correlates of ketamine and walking induced gamma oscillations in the medial prefrontal cortex and mediodorsal thalamus. Harvard Dataverse. https:\/\/doi.org\/10.7910\/DVN\/MIBZLZ","journal-title":"Harvard Dataverse"},{"issue":"11","key":"135_CR75","doi-asserted-by":"publisher","first-page":"0186732","DOI":"10.1371\/journal.pone.0186732","volume":"12","author":"KE Furth","year":"2017","unstructured":"Furth KE, McCoy AJ, Dodge C, Walters JR, Buonanno A, Delaville C (2017) Neuronal correlates of ketamine and walking induced gamma oscillations in the medial prefrontal cortex and mediodorsal thalamus. PLoS ONE 12(11):0186732","journal-title":"PLoS ONE"},{"issue":"5","key":"135_CR76","doi-asserted-by":"publisher","first-page":"2879","DOI":"10.1093\/cercor\/bhz281","volume":"30","author":"A Averna","year":"2020","unstructured":"Averna A et al (2020) Differential effects of open- and closed-loop intracortical microstimulation on firing patterns of neurons in distant cortical areas. Cereb Cortex 30(5):2879\u20132896. https:\/\/doi.org\/10.1093\/cercor\/bhz281","journal-title":"Cereb Cortex"},{"key":"135_CR77","doi-asserted-by":"publisher","DOI":"10.3389\/conf.fninf.2013.09.00068","author":"J Teeters","year":"2013","unstructured":"Teeters J, Sommer FT (2013) epHDF-a standard for storing electrophysiology data in HDF5. F1000Research. https:\/\/doi.org\/10.3389\/conf.fninf.2013.09.00068","journal-title":"F1000Research"},{"key":"135_CR78","doi-asserted-by":"crossref","unstructured":"Fabietti M, Mahmud M, Lotfi A (2020) Effectiveness of employing multimodal signals in removing artifacts from neuronal signals: an empirical analysis. In: Proc. brain informatics, pp 183\u2013193","DOI":"10.1007\/978-3-030-59277-6_17"},{"issue":"2","key":"135_CR79","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1049\/iet-spr.2018.5111","volume":"13","author":"R Ghosh","year":"2018","unstructured":"Ghosh R, Sinha N, Biswas SK (2018) Automated eye blink artefact removal from EEG using support vector machine and autoencoder. IET Signal Process 13(2):141\u2013148","journal-title":"IET Signal Process"},{"key":"135_CR80","doi-asserted-by":"crossref","unstructured":"Leite NMN, Pereira ET, Gurjao EC, Veloso LR (2018) Deep convolutional autoencoder for EEG noise filtering. In: Proc. BIBM, pp 2605\u20132612","DOI":"10.1109\/BIBM.2018.8621080"}],"container-title":["Brain Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40708-021-00135-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40708-021-00135-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40708-021-00135-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T00:06:33Z","timestamp":1672877193000},"score":1,"resource":{"primary":{"URL":"https:\/\/braininformatics.springeropen.com\/articles\/10.1186\/s40708-021-00135-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,20]]},"references-count":80,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["135"],"URL":"https:\/\/doi.org\/10.1186\/s40708-021-00135-3","relation":{},"ISSN":["2198-4018","2198-4026"],"issn-type":[{"type":"print","value":"2198-4018"},{"type":"electronic","value":"2198-4026"}],"subject":[],"published":{"date-parts":[[2021,7,20]]},"assertion":[{"value":"2 May 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 June 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 July 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","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":"14"}}