{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T14:51:30Z","timestamp":1769698290900,"version":"3.49.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,12,29]],"date-time":"2024-12-29T00:00:00Z","timestamp":1735430400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,29]],"date-time":"2024-12-29T00:00:00Z","timestamp":1735430400000},"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":[[2025,2]]},"DOI":"10.1007\/s00521-024-10789-9","type":"journal-article","created":{"date-parts":[[2024,12,29]],"date-time":"2024-12-29T05:13:27Z","timestamp":1735449207000},"page":"5057-5076","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Determination of the common electrodes for users and increasing the classification accuracy of motor imagery EEG"],"prefix":"10.1007","volume":"37","author":[{"given":"Ali","family":"\u00d6zkahraman","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6124-2394","authenticated-orcid":false,"given":"Tamer","family":"\u00d6lmez","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7660-3236","authenticated-orcid":false,"given":"Z\u00fcmray","family":"Dokur","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,29]]},"reference":[{"key":"10789_CR1","unstructured":"Y\u00fcksel A (2017) Classification methods for motor imagery based brain computer interfaces. PhD Dissertations. Istanbul Technical University. Institute of Science and Technology."},{"key":"10789_CR2","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.1007\/s00521-022-07787-0","volume":"35","author":"N Korhan","year":"2023","unstructured":"Korhan N, Dokur Z, Olmez T (2023) Generating ten BCI commands using four simple motor imageries and classification by divergence-based DNN. Neural Comput Appl 35:1303\u20131322. https:\/\/doi.org\/10.1007\/s00521-022-07787-0","journal-title":"Neural Comput Appl"},{"key":"10789_CR3","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1007\/s10462-019-09694-8","volume":"53","author":"MZ Baig","year":"2020","unstructured":"Baig MZ, Aslam N, Shum HPH (2020) Filtering techniques for channel selection in motor imagery EEG applications: a survey. Artif Intell Rev 53:1207\u20131232. https:\/\/doi.org\/10.1007\/s10462-019-09694-8","journal-title":"Artif Intell Rev"},{"key":"10789_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107881","volume":"113","author":"Z Dokur","year":"2021","unstructured":"Dokur Z, Olmez T (2021) Classification of motor imagery electroencephalogram signals by using a divergence based convolutional neural network. Appl Soft Comput 113:107881. https:\/\/doi.org\/10.1016\/j.asoc.2021.107881","journal-title":"Appl Soft Comput"},{"key":"10789_CR5","doi-asserted-by":"publisher","unstructured":"Qi F et al. (2021) Spatiotemporal-filtering-based channel selection for single-trial EEG classification. IEEE Trans Cybernet 51(2). https:\/\/doi.org\/10.1109\/TCYB.2019.2963709","DOI":"10.1109\/TCYB.2019.2963709"},{"key":"10789_CR6","doi-asserted-by":"publisher","unstructured":"Dai C, Pi D, Becker SI (2020) Shapelet-transformed multi-channel EEG channel selection. ACM Trans Intell Syst Technol 11(5). Article 58. https:\/\/doi.org\/10.1145\/3397850","DOI":"10.1145\/3397850"},{"key":"10789_CR7","doi-asserted-by":"publisher","unstructured":"Qiu Z, Jin J, Zhang Y, Wang X (2015) Generic channels selection in motor imagery-based BCI. In: Proceedings of the fifth international conference on cognitive neuro dynamics. Paper 57. https:\/\/doi.org\/10.1007\/978-981-10-0207-6_57","DOI":"10.1007\/978-981-10-0207-6_57"},{"key":"10789_CR8","doi-asserted-by":"publisher","unstructured":"Kirar JS, Agrawal RK (2020) A combination of spectral graph theory and quantum genetic algorithm to find relevant set of electrodes for motor imagery classification. Appl Soft Comput J, 97. https:\/\/doi.org\/10.1016\/j.asoc.2019.105519","DOI":"10.1016\/j.asoc.2019.105519"},{"key":"10789_CR9","doi-asserted-by":"publisher","unstructured":"Atyabi A, Luerssen M, Fitzgibbon S, Powers DMW (2012) Dimension reduction in eeg data using particle swarm optimization. In; WCCI 2012 IEEE world congress on computational intelligence. https:\/\/doi.org\/10.1109\/CEC.2012.6256487","DOI":"10.1109\/CEC.2012.6256487"},{"key":"10789_CR10","doi-asserted-by":"publisher","unstructured":"Gaur P, McCreadie K, Pachori RB, Wang H, Prasad G (2021) An automatic subject specific channel selection method for enhancing motor imagery classification in EEG-BCI using correlation. Biomed Signal Process Control, 68. https:\/\/doi.org\/10.1016\/j.bspc.2021.102574","DOI":"10.1016\/j.bspc.2021.102574"},{"key":"10789_CR11","doi-asserted-by":"publisher","unstructured":"Atyabi A, Luerssen M, Fitzgibbon S, Powers DMW (2012) Evolutionary feature selection and electrode reduction for EEG classification. In: WCCI 2012 IEEE world congress on computational intelligence. https:\/\/doi.org\/10.1109\/CEC.2012.6256130","DOI":"10.1109\/CEC.2012.6256130"},{"key":"10789_CR12","doi-asserted-by":"publisher","unstructured":"Feng JK et al. (2019) An optimized channel selection method based on multi frequency CSP-Rank for motor imagery-based BCI system. Comput Intell Neurosci. Article ID 8068357. https:\/\/doi.org\/10.1155\/2019\/8068357","DOI":"10.1155\/2019\/8068357"},{"key":"10789_CR13","doi-asserted-by":"publisher","unstructured":"Arvaneh M, Guan C, Ang KK, Quek C (2011) Optimizing the channel selection and classification accuracy in EEG-based BCI. IEEE Trans Biomed Eng, 58(6). https:\/\/doi.org\/10.1109\/TBME.2011.2131142","DOI":"10.1109\/TBME.2011.2131142"},{"key":"10789_CR14","doi-asserted-by":"publisher","unstructured":"Mu W et al. (2022) EEG channel selection methods for motor imagery in brain computer interface. In: 10th International winter conference on brain-computer interface. https:\/\/doi.org\/10.1109\/BCI53720.2022.9734929","DOI":"10.1109\/BCI53720.2022.9734929"},{"key":"10789_CR15","doi-asserted-by":"publisher","DOI":"10.1109\/SMC.2014.6974191","author":"HV Shenoy","year":"2014","unstructured":"Shenoy HV, Vinod AP (2014) An iterative optimization technique for robust channel selection in motor imagery based brain computer interface. IEEE Int Conf Syst Man Cybernet. https:\/\/doi.org\/10.1109\/SMC.2014.6974191","journal-title":"IEEE Int Conf Syst Man Cybernet"},{"key":"10789_CR16","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1016\/j.neucom.2013.05.005","volume":"121","author":"L He","year":"2013","unstructured":"He L, Hu Y, Li Y, Li D (2013) Channel selection by Rayleigh coefficient maximization based genetic algorithm for classifying single-trial motor imagery EEG. Neurocomputing 121:423\u2013433. https:\/\/doi.org\/10.1016\/j.neucom.2013.05.005","journal-title":"Neurocomputing"},{"key":"10789_CR17","doi-asserted-by":"publisher","DOI":"10.1109\/SSP.2016.7551798","author":"T Tanaka","year":"2016","unstructured":"Tanaka T, Uehara T, Tanaka Y (2016) Dimensionality reduction of sample covariance matrices by graph Fourier transform for motor imagery brain\u2013machine interface. IEEE Stat Signal Process Workshop (SSP). https:\/\/doi.org\/10.1109\/SSP.2016.7551798","journal-title":"IEEE Stat Signal Process Workshop (SSP)"},{"key":"10789_CR18","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.neucom.2021.02.051","volume":"443","author":"B Shi","year":"2021","unstructured":"Shi B, Wang Q, Yin S, Yue Z, Huai Y, Wang J (2021) A binary harmony search algorithm as channel selection method for motor imagery-based BCI. Neurocomputing 443:12\u201325. https:\/\/doi.org\/10.1016\/j.neucom.2021.02.051","journal-title":"Neurocomputing"},{"key":"10789_CR19","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.artmed.2012.02.001","volume":"55","author":"J Yang","year":"2012","unstructured":"Yang J et al (2012) Channel selection and classification of electroencephalogram signals: an artificial neural network and genetic algorithm-based approach. Artif Intell Med 55:117\u2013126. https:\/\/doi.org\/10.1016\/j.artmed.2012.02.001","journal-title":"Artif Intell Med"},{"issue":"10","key":"10789_CR20","doi-asserted-by":"publisher","first-page":"2153","DOI":"10.1109\/TNSRE.2020.3020975","volume":"28","author":"J Jin","year":"2020","unstructured":"Jin J et al (2020) Bispectrum-based channel selection for motor imagery based brain-computer interfacing. IEEE Trans Neural Syst Rehabil Eng 28(10):2153\u20132163. https:\/\/doi.org\/10.1109\/TNSRE.2020.3020975","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"10789_CR21","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/j.neunet.2019.07.008","volume":"118","author":"J Jin","year":"2019","unstructured":"Jin J, Miao Y, Daly I, Zuo C, Hu D, Cichocki A (2019) Correlation-based channel selection and regularized feature optimization for MI-based BCI. Neural Netw 118:262\u2013270. https:\/\/doi.org\/10.1016\/j.neunet.2019.07.008","journal-title":"Neural Netw"},{"key":"10789_CR22","doi-asserted-by":"publisher","first-page":"111514","DOI":"10.1109\/ACCESS.2020.3003056","volume":"8","author":"Y Park","year":"2020","unstructured":"Park Y, Chung W (2020) Optimal channel selection using correlation coefficient for CSP based EEG classification. IEEE Access 8:111514\u2013111521. https:\/\/doi.org\/10.1109\/ACCESS.2020.3003056","journal-title":"IEEE Access"},{"key":"10789_CR23","doi-asserted-by":"publisher","first-page":"1663","DOI":"10.1016\/j.patcog.2007.10.023","volume":"41","author":"S Sun","year":"2008","unstructured":"Sun S, Zhang C, Lu Y (2008) The random electrode selection ensemble for EEG signal classification. Pattern Recogn 41:1663\u20131675. https:\/\/doi.org\/10.1016\/j.patcog.2007.10.023","journal-title":"Pattern Recogn"},{"key":"10789_CR24","doi-asserted-by":"publisher","unstructured":"Wankar RV, Shah P, Sutar R (2017) Feature extraction and selection methods for motor imagery EEG signals: a review. In: International conference on intelligent computing and control (I2C2). https:\/\/doi.org\/10.1109\/I2C2.2017.8321831","DOI":"10.1109\/I2C2.2017.8321831"},{"key":"10789_CR25","doi-asserted-by":"publisher","first-page":"1498","DOI":"10.1016\/j.ijleo.2013.09.013","volume":"125","author":"X Yu","year":"2014","unstructured":"Yu X, Chum P, Sim K-B (2014) Analysis the effect of PCA for feature reduction in non-stationary EEG based motor imagery of BCI system. Optik 125:1498\u20131502. https:\/\/doi.org\/10.1016\/j.ijleo.2013.09.013","journal-title":"Optik"},{"key":"10789_CR26","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.ijleo.2017.10.085","volume":"155","author":"S-M Park","year":"2018","unstructured":"Park S-M, Kim J-Y, Sim K-B (2018) EEG electrode selection method based on BPSO with channel impact factor for acquisition of significant brain signal. Optik 155:89\u201396. https:\/\/doi.org\/10.1016\/j.ijleo.2017.10.085","journal-title":"Optik"},{"key":"10789_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/app11188761","volume":"11","author":"A Naebi","year":"2021","unstructured":"Naebi A, Feng Z, Hosseinpour F, Abdollahi G (2021) Dimension reduction using new bond graph algorithm and deep learning pooling on EEG signals for BCI. Appl Sci 11:1\u201342. https:\/\/doi.org\/10.3390\/app11188761","journal-title":"Appl Sci"},{"key":"10789_CR28","doi-asserted-by":"publisher","unstructured":"Wei Q, Wang Y, Lu Z (2012) Cultural-based multi-objective particle swarm optimization for EEG channel reduction in multi-class brain-computer interfaces. Appl Mech Mater 249\u2013240:1027\u20131032. https:\/\/doi.org\/10.4028\/www.scientific.net\/AMM.239-240.1027","DOI":"10.4028\/www.scientific.net\/AMM.239-240.1027"},{"key":"10789_CR29","unstructured":"BCI Competitions III-3a (2005). http:\/\/www.bbci.de\/competition\/iii\/."},{"key":"10789_CR30","unstructured":"BCI Competitions IV-2a (2008), http:\/\/www.bbci.de\/competition\/iv\/."},{"key":"10789_CR31","doi-asserted-by":"publisher","first-page":"9780367259686","DOI":"10.1201\/9780429290800","volume-title":"Biomedical signal processing","author":"A Cohen","year":"2019","unstructured":"Cohen A (2019) Biomedical signal processing, vol 2. CRC Press, ISBN, p 9780367259686"},{"key":"10789_CR32","first-page":"145","volume":"8","author":"S Makeig","year":"1996","unstructured":"Makeig S, Bell AJ, Jung T-P, Sejnowski TJ (1996) Independent component analysis of electroencephalographic data. Adv Neural Inf Process Syst 8:145\u2013151","journal-title":"Adv Neural Inf Process Syst"},{"issue":"2","key":"10789_CR33","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1109\/TASSP.1984.1164317","volume":"32","author":"D Griffin","year":"1984","unstructured":"Griffin D, Lim J (1984) Signal estimation from modified short-time Fourier transform. IEEE Trans Acoust Speech Signal Process 32(2):236\u2013243. https:\/\/doi.org\/10.1109\/TASSP.1984.1164317","journal-title":"IEEE Trans Acoust Speech Signal Process"},{"key":"10789_CR34","doi-asserted-by":"publisher","unstructured":"Arizumi N, Tetiana Aksenova T (2019) Fast Continuous Wavelet Transform for Brain Computer Interface using piecewise polynomials. In: 2019 IEEE international symposium on signal processing and information technology (ISSPIT), https:\/\/doi.org\/10.1109\/ISSPIT47144.2019.9001739.","DOI":"10.1109\/ISSPIT47144.2019.9001739"},{"key":"10789_CR35","doi-asserted-by":"publisher","unstructured":"Sun Z, Fan C, Jia T, Li Q, Wu X (2023) EEG channel selection based on neuron proportion with snn for motor imagery classification. In: International conference on neuromorphic computing (ICNC). https:\/\/doi.https:\/\/doi.org\/10.1109\/ICNC59488.2023","DOI":"10.1109\/ICNC59488.2023"},{"key":"10789_CR36","doi-asserted-by":"publisher","unstructured":"Tong L, Qian Y, Peng L, Wang C, Hou Z-G (2023) A learnable EEG channel selection method for MI-BCI using efficient channel attention. Front Neurosci. https:\/\/doi.org\/10.3389\/fnins.2023.1276067.","DOI":"10.3389\/fnins.2023.1276067"},{"key":"10789_CR37","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ad2496","volume":"21","author":"Z Sun","year":"2024","unstructured":"Sun Z, Fan C, Jia T, Qing LQ, Wu X (2024) EEG channel selection based on neuron proportion with SNN for motor imagery classification. J Neural Eng 21:016029. https:\/\/doi.org\/10.1088\/1741-2552\/ad2496","journal-title":"J Neural Eng"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10789-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-024-10789-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-024-10789-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,8]],"date-time":"2025-02-08T19:34:40Z","timestamp":1739043280000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-024-10789-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,29]]},"references-count":37,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["10789"],"URL":"https:\/\/doi.org\/10.1007\/s00521-024-10789-9","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,29]]},"assertion":[{"value":"12 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 December 2024","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 there is no conflict of interests regarding the publication of this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}