{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T10:45:18Z","timestamp":1777718718052,"version":"3.51.4"},"reference-count":61,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T00:00:00Z","timestamp":1723075200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Research and Graduate Studies (DRG) at Ajman University, Ajman, UAE","award":["2022-IRG-ENIT-7"],"award-info":[{"award-number":["2022-IRG-ENIT-7"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Stroke is a major cause of mortality worldwide, disrupts cerebral blood flow, leading to severe brain damage. Hemiplegia, a common consequence, results in motor task loss on one side of the body. Many stroke survivors face long-term motor impairments and require great rehabilitation. Electroencephalograms (EEGs) provide a non-invasive method to monitor brain activity and have been used in brain\u2013computer interfaces (BCIs) to help in rehabilitation. Motor imagery (MI) tasks, detected through EEG, are pivotal for developing BCIs that assist patients in regaining motor purpose. However, interpreting EEG signals for MI tasks remains challenging due to their complexity and low signal-to-noise ratio. The main aim of this study is to focus on optimizing channel selection in EEG-based BCIs specifically for stroke rehabilitation. Determining the most informative EEG channels is crucial for capturing the neural signals related to motor impairments in stroke patients. In this paper, a binary bat algorithm (BA)-based optimization method is proposed to select the most relevant channels tailored to the unique neurophysiological changes in stroke patients. This approach is able to enhance the BCI performance by improving classification accuracy and reducing data dimensionality. We use time\u2013entropy\u2013frequency (TEF) attributes, processed through automated independent component analysis with wavelet transform (AICA-WT) denoising, to enhance signal clarity. The selected channels and features are proved through a k-nearest neighbor (KNN) classifier using public BCI datasets, demonstrating improved classification of MI tasks and the potential for better rehabilitation outcomes.<\/jats:p>","DOI":"10.3390\/a17080346","type":"journal-article","created":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T07:01:25Z","timestamp":1723100485000},"page":"346","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["EEG Channel Selection for Stroke Patient Rehabilitation Using BAT Optimizer"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1980-1791","authenticated-orcid":false,"given":"Mohammed Azmi","family":"Al-Betar","sequence":"first","affiliation":[{"name":"Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman P.O. Box 346, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4228-9298","authenticated-orcid":false,"given":"Zaid Abdi Alkareem","family":"Alyasseri","sequence":"additional","affiliation":[{"name":"Information Technology Research and Development Center (ITRDC), University of Kufa, Najaf 54001, Iraq"},{"name":"College of Engineering, University of Warith Al-Anbiyaa, Karbala 56001, Iraq"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Noor Kamal","family":"Al-Qazzaz","sequence":"additional","affiliation":[{"name":"Biomedical Engineering Department, AL-Khwarizmi College of Engineering, University of Baghdad, Baghdad 47146, Iraq"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sharif Naser","family":"Makhadmeh","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman P.O. Box 346, United Arab Emirates"},{"name":"Data Science and Artificial Intelligence Department, Faculty of Information Technology, University of Petra, Amman 1196, Jordan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9988-5619","authenticated-orcid":false,"given":"Nabeel Salih","family":"Ali","sequence":"additional","affiliation":[{"name":"Information Technology Research and Development Center (ITRDC), University of Kufa, Najaf 54001, Iraq"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6468-8500","authenticated-orcid":false,"given":"Christoph","family":"Guger","sequence":"additional","affiliation":[{"name":"G.Tec Medical Engineering GmbH, 4521 Schiedlberg, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/s10916-019-1517-9","article-title":"Multi-Feature Fusion Method Based on EEG Signal and its Application in Stroke Classification","volume":"44","author":"Li","year":"2020","journal-title":"J. Med. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"052072","DOI":"10.1088\/1742-6596\/1529\/5\/052072","article-title":"An Experimental Framework for Assessing Emotions of Stroke Patients using Electroencephalogram (EEG)","volume":"1529","author":"Khairunizam","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Carino-Escobar, R.I., Carrillo-Mora, P., Vald\u00e9s-Cristerna, R., Rodriguez-Barragan, M.A., Hernandez-Arenas, C., Quinza\u00f1os-Fresnedo, J., Galicia-Alvarado, M.A., and Cantillo-Negrete, J. (2019). Longitudinal analysis of stroke patients\u2019 brain rhythms during an intervention with a brain-computer interface. Neural Plast., 2019.","DOI":"10.1155\/2019\/7084618"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/S1474-4422(13)70264-3","article-title":"Connectivity-based approaches in stroke and recovery of function","volume":"13","author":"Grefkes","year":"2014","journal-title":"Lancet Neurol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1161\/STROKEAHA.110.586156","article-title":"Stroke: Working toward a prioritized world agenda","volume":"41","author":"Hachinski","year":"2010","journal-title":"Stroke"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Gao, W., Cui, Z., Yu, Y., Mao, J., Xu, J., Ji, L., Kan, X., Shen, X., Li, X., and Zhu, S. (2022). Application of a Brain\u2013Computer Interface System with Visual and Motor Feedback in Limb and Brain Functional Rehabilitation after Stroke: Case Report. Brain Sci., 12.","DOI":"10.3390\/brainsci12081083"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1109\/TNSRE.2022.3217573","article-title":"Motor Imagery EEG Classification Based on Riemannian Sparse Optimization and Dempster-Shafer Fusion of Multi-Time-Frequency Patterns","volume":"31","author":"Jin","year":"2022","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Al-Qazzaz, N.K., Alyasseri, Z.A.A., Abdulkareem, K.H., Ali, N.S., Al-Mhiqani, M.N., and Guger, C. (2021). EEG feature fusion for motor imagery: A new robust framework towards stroke patients rehabilitation. Comput. Biol. Med., 137.","DOI":"10.1016\/j.compbiomed.2021.104799"},{"key":"ref_9","unstructured":"Al-Timemy, A.H., Bugmann, G., Outram, N., and Escudero, J. (2011, January 26\u201328). Reduction in classifi-cation errors for myoelectric control of hand movements with independent component analysis. Proceedings of the The 5th International Conference on Information Technology, ICIT, Bali, Indonesia."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Al-Qazzaz, N.K., Ali, S.H.B.M., Ahmad, S.A., and Escudero, J. (2017, January 10\u201313). Optimal EEG Channel Selection for Vascular Dementia Identification Using Improved Binary Gravitation Search Algorithm. Proceedings of the International Conference for Innovation in Biomedical Engineering and Life Sciences, Penang, Malaysia.","DOI":"10.1007\/978-981-10-7554-4_21"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"107059","DOI":"10.1109\/ACCESS.2021.3096430","article-title":"Multichannel optimization with hybrid spectral-entropy markers for gender identification enhancement of emotional-based EEGs","volume":"9","author":"Sabir","year":"2021","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Velasco, I., Sipols, A., De Blas, C.S., Pastor, L., and Bayona, S. (2023). Motor imagery EEG signal classification with a multivariate time series approach. Biomed. Eng. OnLine, 22.","DOI":"10.1186\/s12938-023-01079-x"},{"key":"ref_13","unstructured":"Jouzizadeh, M. (2024). EEG-Assessed Network and Signal Variability Features in Males and Females during a Visuospatial Task. [Ph.D. Thesis, Universit\u00e9 d\u2019Ottawa\/University of Ottawa]."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Al-Qazzaz, N.K., Sabir, M.K., Ali, S., Ahmad, S.A., and Grammer, K. (2019, January 23\u201327). Effective EEG channels for emotion identification over the brain regions using differential evolution algorithm. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8856854"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Dornhege, G., Mill\u00e1n, J.d.R., Hinterberger, T., McFarland, D.J., and Muller, K.R. (2007). Toward Brain-Computer Interfacing, MIT Press.","DOI":"10.7551\/mitpress\/7493.001.0001"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kingphai, K., and Moshfeghi, Y. (2023, January 22\u201326). On channel selection for EEG-based mental workload classification. Proceedings of the International Conference on Machine Learning, Optimization, and Data Science, Grasmere, UK.","DOI":"10.1007\/978-3-031-53966-4_30"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, T., Wu, Y., Ye, A., Cao, L., and Cao, Y. (2024). Two-stage sparse multi-objective evolutionary algorithm for channel selection optimization in BCIs. Front. Hum. Neurosci., 18.","DOI":"10.3389\/fnhum.2024.1400077"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"fcad149","DOI":"10.1093\/braincomms\/fcad149","article-title":"Altered directional functional connectivity underlies post-stroke cognitive recovery","volume":"5","author":"Soleimani","year":"2023","journal-title":"Brain Commun."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1038\/s41583-023-00718-5","article-title":"Brain network communication: Concepts, models and applications","volume":"24","author":"Seguin","year":"2023","journal-title":"Nat. Rev. Neurosci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mang, J., Xu, Z., Qi, Y., and Zhang, T. (2023). Favoring the cognitive-motor process in the closed-loop of BCI mediated post stroke motor function recovery: Challenges and approaches. Front. Neurorobotics, 17.","DOI":"10.3389\/fnbot.2023.1271967"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1016\/S0013-4694(97)00066-7","article-title":"EEG coherency: I: Statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales","volume":"103","author":"Nunez","year":"1997","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Al-Qazzaz, N.K., Hamid Bin Mohd Ali, S., Ahmad, S.A., Islam, M.S., and Escudero, J. (2017). Automatic artifact removal in EEG of normal and demented individuals using ICA\u2013WT during working memory tasks. Sensors, 17.","DOI":"10.3390\/s17061326"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s11517-017-1734-7","article-title":"Discrimination of stroke-related mild cognitive impairment and vascular dementia using EEG signal analysis","volume":"56","author":"Ali","year":"2018","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3322","DOI":"10.1109\/TCYB.2018.2841847","article-title":"Temporally constrained sparse group spatial patterns for motor imagery BCI","volume":"49","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Cybern."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chaudhary, S., Taran, S., Bajaj, V., and Siuly, S. (2020). A flexible analytic wavelet transform based approach for motor-imagery tasks classification in BCI applications. Comput. Methods Programs Biomed., 187.","DOI":"10.1016\/j.cmpb.2020.105325"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/S1005-8885(17)60215-2","article-title":"Recognition of motor imagery tasks for BCI using CSP and chaotic PSO twin SVM","volume":"24","author":"Duan","year":"2017","journal-title":"J. China Univ. Posts Telecommun."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1186\/s13634-015-0251-9","article-title":"A review of channel selection algorithms for EEG signal processing","volume":"2015","author":"Alotaiby","year":"2015","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_28","unstructured":"He, L., Yu, Z., Gu, Z., and Li, Y. (2009, January 17\u201319). Bhattacharyya bound based channel selection for classification of motor imageries in EEG signals. Proceedings of the 2009 Chinese Control and Decision Conference, Guilin, China."},{"key":"ref_29","unstructured":"Tam, W.K., Ke, Z., and Tong, K.Y. (September, January 30). Performance of common spatial pattern under a smaller set of EEG electrodes in brain-computer interface on chronic stroke patients: A multi-session dataset study. Proceedings of the 2011 annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"G\u00f3mez-L\u00f3pez, J.C., Escobar, J.J., D\u00edaz, A.F., Damas, M., Gil-Montoya, F., and Gonz\u00e1lez, J. (2022, January 9\u201313). Boosting the convergence of a GA-based wrapper for feature selection problems on high-dimensional data. Proceedings of the Genetic and Evolutionary Computation Conference Companion, Boston, MA, USA.","DOI":"10.1145\/3520304.3528800"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"2769","DOI":"10.1007\/s40747-021-00452-4","article-title":"Two-stage improved Grey Wolf optimization algorithm for feature selection on high-dimensional classification","volume":"8","author":"Shen","year":"2022","journal-title":"Complex Intell. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5405","DOI":"10.1007\/s11042-022-12795-2","article-title":"Automatic EEG channel selection for multiclass brain-computer interface classification using multiobjective improved firefly algorithm","volume":"82","author":"Tiwari","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/s004220100282","article-title":"Relevant EEG features for the classification of spontaneous motor-related tasks","volume":"86","author":"Cincotti","year":"2002","journal-title":"Biol. Cybern."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"108176","DOI":"10.1016\/j.asoc.2021.108176","article-title":"Brain\u2013computer interface channel selection optimization using meta-heuristics and evolutionary algorithms","volume":"115","author":"Hornero","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4002709","DOI":"10.1109\/TIM.2021.3051996","article-title":"A sliding window common spatial pattern for enhancing motor imagery classification in EEG-BCI","volume":"70","author":"Gaur","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.neucom.2021.02.051","article-title":"A binary harmony search algorithm as channel selection method for motor imagery-based BCI","volume":"443","author":"Shi","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.bspc.2016.11.018","article-title":"Automatic channel selection in EEG signals for classification of left or right hand movement in Brain Computer Interfaces using improved binary gravitation search algorithm","volume":"33","author":"Ghaemi","year":"2017","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"R1","DOI":"10.1088\/1741-2560\/4\/2\/R01","article-title":"A review of classification algorithms for EEG-based brain\u2013computer interfaces","volume":"4","author":"Lotte","year":"2007","journal-title":"J. Neural Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.inffus.2018.10.009","article-title":"Human emotion recognition using deep belief network architecture","volume":"51","author":"Hassan","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1016\/j.bbe.2020.02.002","article-title":"A comparative analysis of signal processing and classification methods for different applications based on EEG signals","volume":"40","author":"Khosla","year":"2020","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"205","DOI":"10.18100\/ijamec.270307","article-title":"Deep belief networks based brain activity classification using EEG from slow cortical potentials in stroke","volume":"4","author":"Altan","year":"2016","journal-title":"Int. J. Appl. Math. Electron. Comput."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5285","DOI":"10.1016\/j.eswa.2014.02.043","article-title":"Exploring dimensionality reduction of EEG features in motor imagery task classification","volume":"41","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"6215","DOI":"10.1016\/j.eswa.2015.03.008","article-title":"Optimizing the number of electrodes and spatial filters for Brain\u2013Computer Interfaces by means of an evolutionary multi-objective approach","volume":"42","author":"Aler","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1109\/TBME.2004.827827","article-title":"Support vector channel selection in BCI","volume":"51","author":"Lal","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1109\/TBME.2008.915728","article-title":"BCI competition III: Dataset II-ensemble of SVMs for BCI P300 speller","volume":"55","author":"Rakotomamonjy","year":"2008","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"100597","DOI":"10.1016\/j.swevo.2019.100597","article-title":"Dimensionality reduction in evolutionary algorithms-based feature selection for motor imagery brain-computer interface","volume":"52","author":"Tan","year":"2020","journal-title":"Swarm Evol. Comput."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.knosys.2012.11.005","article-title":"Genetic algorithms in feature and instance selection","volume":"39","author":"Tsai","year":"2013","journal-title":"Knowl.-Based Syst."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"3239","DOI":"10.1007\/s00521-016-2236-5","article-title":"PSO-based feature selection and neighborhood rough set-based classification for BCI multiclass motor imagery task","volume":"28","year":"2017","journal-title":"Neural Comput. Appl."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.eswa.2017.07.033","article-title":"Differential evolution algorithm as a tool for optimal feature subset selection in motor imagery EEG","volume":"90","author":"Baig","year":"2017","journal-title":"Expert Syst. Appl."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Atyabi, A., Luerssen, M., Fitzgibbon, S., and Powers, D.M. (2012, January 10\u201315). Evolutionary feature selection and electrode reduction for EEG classification. Proceedings of the 2012 IEEE Congress on Evolutionary Computation, Brisbane, QLD, Australia.","DOI":"10.1109\/CEC.2012.6256130"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.eswa.2017.09.062","article-title":"Evolutionary computation algorithms for feature selection of EEG-based emotion recognition using mobile sensors","volume":"93","author":"Nakisa","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Al-Qazzaz, N.K., Ali, S.H.M., and Ahmad, S.A. (2018, January 3\u20136). Differential Evolution Based Channel Selection Algorithm on EEG Signal for Early Detection of Vascular Dementia among Stroke Survivors. Proceedings of the 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), Sarawak, Malaysia.","DOI":"10.1109\/IECBES.2018.8626684"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Alyasseri, Z.A.A., Khader, A.T., Al-Betar, M.A., Joao, P.P., and Osama, A.A. (2018, January 8\u201313). EEG-based Person Authentication Using Multi-objective Flower Pollination Algorithm. Proceedings of the Evolutionary Computation (CEC), Rio de Janeiro, Brazil.","DOI":"10.1109\/CEC.2018.8477895"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Yang, X.S. (2010). A new metaheuristic bat-inspired algorithm. Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), Springer.","DOI":"10.1007\/978-3-642-12538-6_6"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1109\/72.761722","article-title":"Fast and robust fixed-point algorithms for independent component analysis","volume":"10","author":"Hyvarinen","year":"1999","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1016\/j.medengphy.2009.04.003","article-title":"Blind source separation to enhance spectral and non-linear features of magnetoencephalogram recordings. Application to Alzheimer\u2019s disease","volume":"31","author":"Escudero","year":"2009","journal-title":"Med. Eng. Phys."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"R15","DOI":"10.1088\/0967-3334\/26\/1\/R02","article-title":"Independent component analysis for biomedical signals","volume":"26","author":"James","year":"2005","journal-title":"Physiol. Meas."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"29015","DOI":"10.3390\/s151129015","article-title":"Selection of mother wavelet functions for multi-channel EEG signal analysis during a working memory task","volume":"15","author":"Ahmad","year":"2015","journal-title":"Sensors"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1007\/s10439-011-0312-7","article-title":"Quantitative evaluation of artifact removal in real magnetoencephalogram signals with blind source separation","volume":"39","author":"Escudero","year":"2011","journal-title":"Ann. Biomed. Eng."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1220","DOI":"10.1016\/j.clinph.2003.12.015","article-title":"Optimization of an independent component analysis approach for artifact identification and removal in magnetoencephalographic signals","volume":"115","author":"Barbati","year":"2004","journal-title":"Clin. Neurophysiol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1109\/JSEN.2011.2115236","article-title":"Automatic artifact rejection from multichannel scalp EEG by wavelet ICA","volume":"12","author":"Mammone","year":"2011","journal-title":"IEEE Sens. J."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/17\/8\/346\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:31:53Z","timestamp":1760110313000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/17\/8\/346"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,8]]},"references-count":61,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["a17080346"],"URL":"https:\/\/doi.org\/10.3390\/a17080346","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,8]]}}}