{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T13:57:35Z","timestamp":1778162255981,"version":"3.51.4"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2022,11,24]],"date-time":"2022-11-24T00:00:00Z","timestamp":1669248000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,24]],"date-time":"2022-11-24T00:00:00Z","timestamp":1669248000000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1007\/s00521-022-08027-1","type":"journal-article","created":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T14:03:52Z","timestamp":1669385032000},"page":"6623-6634","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["An optimized artificial intelligence based technique for identifying motor imagery from EEGs for advanced brain computer interface technology"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5842-930X","authenticated-orcid":false,"given":"Taslima","family":"Khanam","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siuly","family":"Siuly","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hua","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,24]]},"reference":[{"key":"8027_CR1","unstructured":"WHO (2011) Summary: world report on disability 2011. World Health Organization, 099570705."},{"key":"8027_CR2","unstructured":"AIHW (2020) People with disability in Australia 2020."},{"key":"8027_CR3","doi-asserted-by":"crossref","unstructured":"Sadiq MT, Siuly S, Rehman AU (2022) Evaluation of power spectral and machine learning techniques for the development of subject-specific BCI. Artificial Intelligence-Based Brain-Computer Interface: Elsevier, P. 99-120","DOI":"10.1016\/B978-0-323-91197-9.00001-1"},{"key":"8027_CR4","doi-asserted-by":"crossref","unstructured":"Shih JJ, Krusienski DJ, Wolpaw JR, (eds) (2012) Brain-computer interfaces in medicine. Mayo clinic proceedings; Elsevier","DOI":"10.1016\/j.mayocp.2011.12.008"},{"key":"8027_CR5","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.measurement.2016.02.059","volume":"86","author":"H Wang","year":"2016","unstructured":"Wang H, Zhang Y (2016) Detection of motor imagery EEG signals employing Na\u00efve Bayes based learning process. Measurement 86:148\u2013158","journal-title":"Measurement"},{"key":"8027_CR6","doi-asserted-by":"publisher","first-page":"2730","DOI":"10.1109\/TBME.2009.2026181","volume":"56","author":"KP Thomas","year":"2009","unstructured":"Thomas KP, Guan C, Lau CT, Vinod AP, Ang KK (2009) A new discriminative common spatial pattern method for motor imagery brain-computer interfaces. IEEE Trans Biomed Eng 56:2730","journal-title":"IEEE Trans Biomed Eng"},{"issue":"4","key":"8027_CR7","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1109\/TNSRE.2012.2184838","volume":"20","author":"S Siuly","year":"2012","unstructured":"Siuly S, Li Y (2012) Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain-computer interface. IEEE Trans Neural Syst Rehabil Eng 20(4):526\u2013358","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"3","key":"8027_CR8","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1016\/j.cmpb.2013.12.020","volume":"113","author":"Li Y Siuly","year":"2014","unstructured":"Siuly Li Y, Paul Wen P (2014) Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain-computer interface. Comput Methods Programs Biomed 113(3):767\u2013780","journal-title":"Comput Methods Programs Biomed"},{"key":"8027_CR9","doi-asserted-by":"crossref","unstructured":"Chaudhary S, Taran S, Bajaj V, Siuly S (2020) A flexible analytic wavelet transform based approach for motor-imagery tasks classification in BCI applications.","DOI":"10.1016\/j.cmpb.2020.105325"},{"issue":"4","key":"8027_CR10","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1109\/TNSRE.2012.2184838","volume":"20","author":"S Siuly","year":"2012","unstructured":"Siuly S, Li Y (2012) Improving the separability of motor imagery EEG signals using a cross correlation-based least square support vector machine for brain\u2013computer interface. IEEE Trans Neural Syst Rehabil Eng 20(4):526\u2013538","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"8027_CR11","first-page":"141","volume":"11","author":"S Siuly","year":"2016","unstructured":"Siuly S, Li Y, Zhang Y (2016) EEG signal analysis and classification. IEEE Trans Neural Syst Rehabilit Eng 11:141\u2013144","journal-title":"IEEE Trans Neural Syst Rehabilit Eng"},{"key":"8027_CR12","doi-asserted-by":"publisher","first-page":"105242","DOI":"10.1016\/j.compbiomed.2022.105242","volume":"143","author":"MT Sadiq","year":"2022","unstructured":"Sadiq MT, Aziz MZ, Almogren A, Yousaf A, Siuly S, Rehman AU (2022) Exploiting pretrained CNN models for the development of an EEG-based robust BCI framework. Comput Biol Med 143:105242","journal-title":"Comput Biol Med"},{"issue":"1","key":"8027_CR13","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.neuroimage.2005.12.003","volume":"31","author":"G Pfurtscheller","year":"2006","unstructured":"Pfurtscheller G, Brunner C, Schlogl A, Lopes da Silva FH (2006) Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks. Neuroimage 31(1):153\u20139","journal-title":"Neuroimage"},{"issue":"2","key":"8027_CR14","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1016\/j.neuroimage.2007.01.051","volume":"37","author":"B Blankertz","year":"2007","unstructured":"Blankertz B, Dornhege G, Krauledat M, Muller KR, Curio G (2007) The non-invasive Berlin brain-computer interface: fast acquisition of effective performance in untrained subjects. Neuroimage 37(2):539\u201350","journal-title":"Neuroimage"},{"issue":"11","key":"8027_CR15","doi-asserted-by":"publisher","first-page":"e80886","DOI":"10.1371\/journal.pone.0080886","volume":"8","author":"M Ahn","year":"2013","unstructured":"Ahn M, Cho H, Ahn S, Jun SC (2013) High theta and low alpha powers may be indicative of BCI-illiteracy in motor imagery. PLoS One 8(11):e80886","journal-title":"PLoS One"},{"issue":"10","key":"8027_CR16","doi-asserted-by":"publisher","first-page":"2153","DOI":"10.1109\/TNSRE.2020.3020975","volume":"28","author":"J Jin","year":"2020","unstructured":"Jin J, Liu C, Daly I, Miao Y, Li S, Wang X et al (2020) Bispectrum-based channel selection for motor imagery based brain-computer interfacing. IEEE Trans Neural Syst Rehabil Eng 28(10):2153\u201363","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"1","key":"8027_CR17","doi-asserted-by":"publisher","first-page":"33","DOI":"10.3233\/idt-200005","volume":"15","author":"P Chaudhary","year":"2021","unstructured":"Chaudhary P, Agrawal R, Gupta D, Castillo O, Khanna A (2021) Sensory motor imagery EEG classification based on non-dyadic wavelets using dynamic weighted majority ensemble classification. Int Decis Technol 15(1):33\u201343. https:\/\/doi.org\/10.3233\/idt-200005","journal-title":"Int Decis Technol"},{"key":"8027_CR18","doi-asserted-by":"publisher","first-page":"699","DOI":"10.1109\/TNSRE.2021.3071140","volume":"29","author":"Y Miao","year":"2021","unstructured":"Miao Y, Jin J, Daly I, Zuo C, Wang X, Cichocki A et al (2021) Learning common time-frequency-spatial patterns for motor imagery classification. IEEE Trans Neural Syst Rehabilitation Eng 29:699\u2013707","journal-title":"IEEE Trans Neural Syst Rehabilitation Eng"},{"key":"8027_CR19","doi-asserted-by":"publisher","first-page":"126698","DOI":"10.1109\/ACCESS.2021.3110882","volume":"9","author":"A Tiwari","year":"2021","unstructured":"Tiwari A, Chaturvedi A (2021) A novel channel selection method for BCI classification using dynamic channel relevance. IEEE Access 9:126698\u2013126716","journal-title":"IEEE Access"},{"key":"8027_CR20","doi-asserted-by":"publisher","first-page":"104546","DOI":"10.1016\/j.compbiomed.2021.104546","volume":"135","author":"MN Cherloo","year":"2021","unstructured":"Cherloo MN, Amiri HK, Daliri MR (2021) Ensemble regularized common spatio-spectral pattern (ensemble RCSSP) model for motor imagery-based EEG signal classification. Comput Biol Med 135:104546","journal-title":"Comput Biol Med"},{"issue":"3","key":"8027_CR21","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1007\/s12193-020-00358-4","volume":"15","author":"K Renuga Devi","year":"2021","unstructured":"Renuga Devi K, Hannah IH (2021) Neighborhood based decision theoretic rough set under dynamic granulation for BCI motor imagery classification. J Multimodal User Inter 15(3):301\u2013321","journal-title":"J Multimodal User Inter"},{"issue":"5","key":"8027_CR22","doi-asserted-by":"publisher","first-page":"2748","DOI":"10.12928\/telkomnika.v18i5.14899","volume":"18","author":"EC Djamal","year":"2020","unstructured":"Djamal EC, Putra RD (2020) Brain-computer interface of focus and motor imagery using wavelet and recurrent neural networks. Telkomnika (Telecommun Comput Electron Control) 18(5):2748\u20132756","journal-title":"Telkomnika (Telecommun Comput Electron Control)"},{"key":"8027_CR23","doi-asserted-by":"crossref","unstructured":"Wang J, Feng Z, Lu N (eds) (2017) Feature extraction by common spatial pattern in frequency domain for motor imagery tasks classification. In: 2017 29th Chinese control and decision conference (CCDC): IEEE.","DOI":"10.1109\/CCDC.2017.7978220"},{"issue":"23","key":"8027_CR24","doi-asserted-by":"publisher","first-page":"8646","DOI":"10.1049\/joe.2018.9075","volume":"2019","author":"H Jia","year":"2019","unstructured":"Jia H, Wang S, Zheng D, Qu X, Fan S (2019) Comparative study of motor imagery classification based on BP-NN and SVM. J Eng 2019(23):8646\u20138649","journal-title":"J Eng"},{"issue":"2","key":"8027_CR25","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1109\/TBME.2010.2082539","volume":"58","author":"F Lotte","year":"2011","unstructured":"Lotte F, Guan C (2011) Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms. IEEE Trans Biomed Eng 58(2):355\u201362","journal-title":"IEEE Trans Biomed Eng"},{"key":"8027_CR26","doi-asserted-by":"crossref","unstructured":"AlHinai N (2020) Introduction to biomedical signal processing and artificial intelligence. Biomedical signal processing and artificial intelligence in healthcare: Elsevier. pp 1\u201328.","DOI":"10.1016\/B978-0-12-818946-7.00001-9"},{"key":"8027_CR27","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci11070900","author":"I Hussain","year":"2021","unstructured":"Hussain I, Park SJ (2021) Quantitative evaluation of task-induced neurological outcome after stroke. Brain Sci. https:\/\/doi.org\/10.3390\/brainsci11070900","journal-title":"Brain Sci"},{"issue":"1","key":"8027_CR28","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1109\/MSP.2008.4408441","volume":"25","author":"B Blankertz","year":"2007","unstructured":"Blankertz B, Tomioka R, Lemm S, Kawanabe M, Muller K-R (2007) Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Process Mag 25(1):41\u201356","journal-title":"IEEE Signal Process Mag"},{"issue":"4","key":"8027_CR29","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1109\/86.895946","volume":"8","author":"H Ramoser","year":"2000","unstructured":"Ramoser H, Muller-Gerking J, Pfurtscheller G (2000) Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng 8(4):441\u2013446","journal-title":"IEEE Trans Rehabil Eng"},{"key":"8027_CR30","doi-asserted-by":"crossref","unstructured":"Ortner R, Scharinger J, Lechner A, Guger C (eds) (2015) How many people can control a motor imagery based BCI using common spatial patterns? In: 2015 7th international IEEE\/EMBS conference on neural engineering (NER): IEEE.","DOI":"10.1109\/NER.2015.7146595"},{"key":"8027_CR31","doi-asserted-by":"crossref","unstructured":"Ortner R, Scharinger J, Lechne A (2015) How many people can control a motor imagery based BCI using common spatial patterns? In: 7th annual international IEEE EMBS conference on neural engineering montpellier.","DOI":"10.1109\/NER.2015.7146595"},{"issue":"4","key":"8027_CR32","doi-asserted-by":"publisher","first-page":"702","DOI":"10.1109\/TNSRE.2015.2398573","volume":"23","author":"LF Nicolas-Alonso","year":"2015","unstructured":"Nicolas-Alonso LF, Corralejo R, Gomez-Pilar J, Alvarez D, Hornero R (2015) Adaptive stacked generalization for multiclass motor imagery-based brain computer interfaces. IEEE Trans Neural Syst Rehabil Eng 23(4):702\u201312","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"8027_CR33","doi-asserted-by":"crossref","unstructured":"Rathipriya N, Deepajothi S, Rajendran T (eds) (2013) Classification of motor imagery ecog signals using support vector machine for brain computer interface. In: 2013 fifth international conference on advanced computing (ICoAC): IEEE.","DOI":"10.1109\/ICoAC.2013.6921928"},{"key":"8027_CR34","doi-asserted-by":"crossref","unstructured":"Mondini V, Mangia AL, Cappello A (2016) EEG-based BCI system using adaptive features extraction and classification procedures. Computational intelligence and neuroscience.","DOI":"10.1155\/2016\/4562601"},{"key":"8027_CR35","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3091399","author":"HD Milan\u00e9s","year":"2021","unstructured":"Milan\u00e9s HD, Codorni\u00fa RT, Baracaldo RL, Zamora RS, Rodriguez DD, Albuern YL et al (2021) Shallow convolutional network excel for classifying motor imagery EEG in BCI applications. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2021.3091399","journal-title":"IEEE Access"},{"key":"8027_CR36","doi-asserted-by":"crossref","unstructured":"Abougharbia J, Attallah O, Tamazin M, Nasser A (2019) A novel BCI system based on hybrid features for classifying motor imagery tasks. In: 2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA): IEEE.","DOI":"10.1109\/IPTA.2019.8936119"},{"key":"8027_CR37","doi-asserted-by":"crossref","unstructured":"Miao Y, Yin F, Zuo C, Wang X, Jin J (2019) Improved RCSP and AdaBoost-based classification for motor-imagery BCI. In: 2019 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA); 2019: IEEE.","DOI":"10.1109\/CIVEMSA45640.2019.9071599"},{"key":"8027_CR38","doi-asserted-by":"crossref","unstructured":"Park Y, Chung W (2019) Optimal channel selection using covariance matrix and cross-combining region in EEG-based BCI. In: 2019 7th International Winter Conference on Brain-Computer Interface (BCI): IEEE.","DOI":"10.1109\/IWW-BCI.2019.8737257"},{"key":"8027_CR39","doi-asserted-by":"crossref","unstructured":"Dai M, Zheng D, Liu S, Zhang P (2018) Transfer kernel common spatial patterns for motor imagery brain-computer interface classification. Computat Math Methods Med.","DOI":"10.1155\/2018\/9871603"},{"key":"8027_CR40","doi-asserted-by":"publisher","first-page":"49192","DOI":"10.1109\/ACCESS.2018.2868178","volume":"6","author":"S Selim","year":"2018","unstructured":"Selim S, Tantawi MM, Shedeed HA, Badr A (2018) A csp\\am-ba-svm approach for motor imagery bci system. IEEE Access 6:49192\u201349208","journal-title":"IEEE Access"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-08027-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-08027-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-08027-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T20:30:41Z","timestamp":1677616241000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-08027-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,24]]},"references-count":40,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["8027"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-08027-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,24]]},"assertion":[{"value":"8 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 October 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 November 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they do not have any kind of conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study applied secondary data which are publicly available online, on the following link: (). All the respondents engaged in this survey signed the consent paper and, they gave permission to use their personal details with confidentiality for research purpose. Our study project does not have any direct network with any participants. So, we do not need any ethical approval for conducting this research.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}