{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:15:37Z","timestamp":1757618137118,"version":"3.44.0"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T00:00:00Z","timestamp":1748304000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T00:00:00Z","timestamp":1748304000000},"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":["Evolving Systems"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s12530-025-09696-8","type":"journal-article","created":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T04:07:16Z","timestamp":1748318836000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Motor imagery task classification using spatial\u2013time\u2013frequency features of EEG signals: a deep learning approach for improved performance"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2315-411X","authenticated-orcid":false,"given":"T. K.","family":"Muhamed Jishad","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"P. V.","family":"Sudeep","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M.","family":"Sanjay","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,27]]},"reference":[{"key":"9696_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102172","volume":"63","author":"A Al-Saegh","year":"2021","unstructured":"Al-Saegh A, Dawwd SA, Abdul-Jabbar JM (2021) Deep learning for motor imagery EEG-based classification: a review. Biomed Signal Process Control 63:102172. https:\/\/doi.org\/10.1016\/j.bspc.2020.102172","journal-title":"Biomed Signal Process Control"},{"issue":"16","key":"9696_CR2","doi-asserted-by":"publisher","first-page":"12001","DOI":"10.1007\/s00521-023-08336-z","volume":"35","author":"Y An","year":"2023","unstructured":"An Y, Lam HK, Ling SH (2023) Multi-classification for EEG motor imagery signals using data evaluation-based auto-selected regularized FBCSP and convolutional neural network. Neural Comput Appl 35(16):12001\u201312027. https:\/\/doi.org\/10.1007\/s00521-023-08336-z","journal-title":"Neural Comput Appl"},{"issue":"6","key":"9696_CR3","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1109\/MSP.2013.2265316","volume":"30","author":"F Auger","year":"2013","unstructured":"Auger F, Flandrin P, Lin Y-T, McLaughlin S, Meignen S, Oberlin T, Wu H-T (2013) Time\u2013frequency reassignment and synchrosqueezing: an overview. IEEE Signal Process Mag 30(6):32\u201341. https:\/\/doi.org\/10.1109\/MSP.2013.2265316","journal-title":"IEEE Signal Process Mag"},{"key":"9696_CR4","volume-title":"Bci competition 2008-graz data set a. Institute for Knowledge Discovery (Laboratory of Brain\u2013Computer Interfaces)","author":"C Brunner","year":"2008","unstructured":"Brunner C, Leeb R, M\u00fcller-Putz G, Schl\u00f6gl A, Pfurtscheller G (2008) Bci competition 2008-graz data set a. Institute for Knowledge Discovery (Laboratory of Brain\u2013Computer Interfaces). Graz University of Technology, Graz"},{"issue":"9","key":"9696_CR5","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1038\/nrneurol.2016.113","volume":"12","author":"U Chaudhary","year":"2016","unstructured":"Chaudhary U, Birbaumer N, Ramos-Murguialday A (2016) Brain\u2013computer interfaces for communication and rehabilitation. Nat Rev Neurol 12(9):513\u2013525. https:\/\/doi.org\/10.1038\/nrneurol.2016.113","journal-title":"Nat Rev Neurol"},{"issue":"12","key":"9696_CR6","doi-asserted-by":"publisher","first-page":"4494","DOI":"10.1109\/JSEN.2019.2899645","volume":"19","author":"S Chaudhary","year":"2019","unstructured":"Chaudhary S, Taran S, Bajaj V, Sengur A (2019) Convolutional neural network based approach towards motor imagery tasks EEG signals classification. IEEE Sens J 19(12):4494\u20134500. https:\/\/doi.org\/10.1109\/JSEN.2019.2899645","journal-title":"IEEE Sens J"},{"key":"9696_CR7","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.neuroimage.2019.05.048","volume":"199","author":"MX Cohen","year":"2019","unstructured":"Cohen MX (2019) A better way to define and describe Morlet wavelets for time\u2013frequency analysis. Neuroimage 199:81\u201386. https:\/\/doi.org\/10.1016\/j.neuroimage.2019.05.048","journal-title":"Neuroimage"},{"issue":"3","key":"9696_CR8","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ab0ab5","volume":"16","author":"A Craik","year":"2019","unstructured":"Craik A, He Y, Contreras-Vidal JL (2019) Deep learning for electroencephalogram (EEG) classification tasks: a review. J Neural Eng 16(3):031001","journal-title":"J Neural Eng"},{"issue":"3","key":"9696_CR9","doi-asserted-by":"publisher","first-page":"551","DOI":"10.3390\/s19030551","volume":"19","author":"M Dai","year":"2019","unstructured":"Dai M, Zheng D, Na R, Wang S, Zhang S (2019) EEG classification of motor imagery using a novel deep learning framework. Sensors 19(3):551. https:\/\/doi.org\/10.3390\/s19030551","journal-title":"Sensors"},{"issue":"2","key":"9696_CR10","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.acha.2010.08.002","volume":"30","author":"I Daubechies","year":"2011","unstructured":"Daubechies I, Lu J, Wu H-T (2011) Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool. Appl Comput Harmon Anal 30(2):243\u2013261. https:\/\/doi.org\/10.1016\/j.acha.2010.08.002","journal-title":"Appl Comput Harmon Anal"},{"key":"9696_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12984-015-0076-7","volume":"12","author":"ST Foldes","year":"2015","unstructured":"Foldes ST, Weber DJ, Collinger JL (2015) Meg-based neurofeedback for hand rehabilitation. J Neuroeng Rehabil 12:1\u20139. https:\/\/doi.org\/10.1186\/s12984-015-0076-7","journal-title":"J Neuroeng Rehabil"},{"issue":"9","key":"9696_CR12","doi-asserted-by":"publisher","first-page":"1233","DOI":"10.3390\/brainsci12091233","volume":"12","author":"S Gao","year":"2022","unstructured":"Gao S, Yang J, Shen T, Jiang W (2022) A parallel feature fusion network combining GRU and CNN for motor imagery EEG decoding. Brain Sci 12(9):1233. https:\/\/doi.org\/10.3390\/brainsci12091233","journal-title":"Brain Sci"},{"key":"9696_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2021.3051996","volume":"70","author":"P Gaur","year":"2021","unstructured":"Gaur P, Gupta H, Chowdhury A, McCreadie K, Pachori RB, Wang H (2021) A sliding window common spatial pattern for enhancing motor imagery classification in EEG-BCI. IEEE Trans Instrum Meas 70:1\u20139. https:\/\/doi.org\/10.1109\/TIM.2021.3051996","journal-title":"IEEE Trans Instrum Meas"},{"key":"9696_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-642-02091-9","volume-title":"Brain\u2013computer interfaces: revolutionizing human\u2013computer interaction","author":"B Graimann","year":"2010","unstructured":"Graimann B, Allison B, Pfurtscheller G (2010) Brain\u2013computer interfaces: a gentle introduction. In: Graimann B, Pfurtscheller G, Allison B (eds) Brain\u2013computer interfaces: revolutionizing human\u2013computer interaction. Springer, Berlin, Heidelberg, pp 1\u201327"},{"key":"9696_CR15","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"9696_CR16","doi-asserted-by":"publisher","first-page":"1554","DOI":"10.1109\/TNSRE.2023.3249831","volume":"31","author":"Y Hu","year":"2023","unstructured":"Hu Y, Liu Y, Zhang S, Zhang T, Dai B, Peng B, Yang H, Dai Y (2023) A cross-space CNN with customized characteristics for motor imagery EEG classification. IEEE Trans Neural Syst Rehabil Eng 31:1554\u20131565. https:\/\/doi.org\/10.1109\/TNSRE.2023.3249831","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"9696_CR17","unstructured":"Iandola FN, Han S, Moskewicz MW, Ashraf K, Dally WJ, Keutzer K (2016) Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size. arXiv preprint arXiv:1602.07360"},{"key":"9696_CR18","doi-asserted-by":"publisher","first-page":"1311","DOI":"10.1109\/TNSRE.2023.3243992","volume":"31","author":"H Jia","year":"2023","unstructured":"Jia H, Yu S, Yin S, Liu L, Yi C, Xue K, Li F, Yao D, Xu P, Zhang T (2023) A model combining multi branch spectral-temporal cnn, efficient channel attention, and lightgbm for mi-bci classification. IEEE Trans Neural Syst Rehabil Eng 31:1311\u20131320. https:\/\/doi.org\/10.1109\/TNSRE.2023.3243992","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"9696_CR19","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1201\/9781003146810-11","volume-title":"Analysis of medical modalities for improved diagnosis in modern healthcare","author":"TM Jishad","year":"2021","unstructured":"Jishad TM, Sanjay M (2021) Brain computer interfaces: the basics, state of the art, and future. In: Bajaj V, Sinha GR (eds) Analysis of medical modalities for improved diagnosis in modern healthcare, 1st edn. CRC Press, Boca Raton, pp 237\u2013270","edition":"1"},{"key":"9696_CR20","unstructured":"Kingma DP (2014) Adam: a method for stochastic optimization. arXiv:1412.6980"},{"issue":"6","key":"9696_CR21","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390. https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun ACM"},{"key":"9696_CR22","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/B978-0-12-800948-2.00014-5","volume-title":"The neurology of conciousness","author":"A K\u00fcbler","year":"2016","unstructured":"K\u00fcbler A, Mattia D (2016) Chapter 14\u2014brain\u2013computer interface based solutions for end-users with severe communication disorders. In: Laureys S, Gosseries O, Tononi G (eds) The neurology of conciousness, 2nd edn. Academic Press, San Diego, pp 217\u2013240","edition":"2"},{"issue":"2","key":"9696_CR23","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1109\/86.847817","volume":"8","author":"RT Lauer","year":"2000","unstructured":"Lauer RT, Peckham PH, Kilgore KL, Heetderks WJ (2000) Applications of cortical signals to neuroprosthetic control: a critical review. IEEE Trans Rehabil Eng 8(2):205\u2013208. https:\/\/doi.org\/10.1109\/86.847817","journal-title":"IEEE Trans Rehabil Eng"},{"key":"9696_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.103342","volume":"72","author":"H Li","year":"2022","unstructured":"Li H, Ding M, Zhang R, Xiu C (2022) Motor imagery EEG classification algorithm based on CNN-LSTM feature fusion network. Biomed Signal Process Control 72:103342. https:\/\/doi.org\/10.1016\/j.bspc.2021.103342","journal-title":"Biomed Signal Process Control"},{"issue":"2","key":"9696_CR25","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/abd82b","volume":"18","author":"X Liu","year":"2021","unstructured":"Liu X, Lv L, Shen Y, Xiong P, Yang J, Liu J (2021) Multiscale space\u2013time\u2013frequency feature-guided multitask learning CNN for motor imagery EEG classification. J Neural Eng 18(2):026003. https:\/\/doi.org\/10.1088\/1741-2552\/abd82b","journal-title":"J Neural Eng"},{"key":"9696_CR26","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1109\/TNSRE.2022.3156076","volume":"30","author":"C Liu","year":"2022","unstructured":"Liu C, Jin J, Daly I, Li S, Sun H, Huang Y, Wang X, Cichocki A (2022) Sincnet-based hybrid neural network for motor imagery EEG decoding. IEEE Trans Neural Syst Rehabil Eng 30:540\u2013549. https:\/\/doi.org\/10.1109\/TNSRE.2022.3156076","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"3","key":"9696_CR27","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/aab2f2","volume":"15","author":"F Lotte","year":"2018","unstructured":"Lotte F, Bougrain L, Cichocki A, Clerc M, Congedo M, Rakotomamonjy A, Yger F (2018) A review of classification algorithms for EEG-based brain\u2013computer interfaces: a 10 year update. J Neural Eng 15(3):031005. https:\/\/doi.org\/10.1088\/1741-2552\/aab2f2","journal-title":"J Neural Eng"},{"key":"9696_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103582","volume":"75","author":"W Ma","year":"2022","unstructured":"Ma W, Gong Y, Xue H, Liu Y, Lin X, Zhou G, Li Y (2022) A lightweight and accurate double-branch neural network for four-class motor imagery classification. Biomed Signal Process Control 75:103582. https:\/\/doi.org\/10.1016\/j.bspc.2022.103582","journal-title":"Biomed Signal Process Control"},{"issue":"4","key":"9696_CR29","doi-asserted-by":"publisher","first-page":"2237","DOI":"10.1002\/ima.22593","volume":"31","author":"R Mahamune","year":"2021","unstructured":"Mahamune R, Laskar SH (2021) Classification of the four-class motor imagery signals using continuous wavelet transform filter bank-based two-dimensional images. Int J Imaging Syst Technol 31(4):2237\u20132248. https:\/\/doi.org\/10.1002\/ima.22593","journal-title":"Int J Imaging Syst Technol"},{"key":"9696_CR30","doi-asserted-by":"publisher","unstructured":"Muhamed Jishad TK, Sanjay M (2021) Topography based classification for motor imagery BCI using transfer learning. In: 2021 International conference on communication, control and information sciences (ICCISc), vol 1. IEEE, pp 1\u20135. https:\/\/doi.org\/10.1109\/ICCISc52257.2021.9484938","DOI":"10.1109\/ICCISc52257.2021.9484938"},{"issue":"5","key":"9696_CR31","doi-asserted-by":"publisher","first-page":"787","DOI":"10.1016\/S1388-2457(98)00038-8","volume":"110","author":"J M\u00fcller-Gerking","year":"1999","unstructured":"M\u00fcller-Gerking J, Pfurtscheller G, Flyvbjerg H (1999) Designing optimal spatial filters for single-trial EEG classification in a movement task. Clin Neurophysiol 110(5):787\u2013798. https:\/\/doi.org\/10.1016\/S1388-2457(98)00038-8","journal-title":"Clin Neurophysiol"},{"key":"9696_CR32","doi-asserted-by":"publisher","unstructured":"Oberlin T, Meignen S, Perrier V (2014) The Fourier-based synchrosqueezing transform. In: 2014 IEEE international conference on acoustics, speech and signal processing (ICASSP). https:\/\/doi.org\/10.1109\/ICASSP.2014.6853609. IEEE, pp 315\u2013319","DOI":"10.1109\/ICASSP.2014.6853609"},{"issue":"20","key":"9696_CR33","doi-asserted-by":"publisher","first-page":"4541","DOI":"10.3390\/s19204541","volume":"19","author":"CJ Ortiz-Echeverri","year":"2019","unstructured":"Ortiz-Echeverri CJ, Salazar-Colores S, Rodr\u00edguez-Res\u00e9ndiz J, G\u00f3mez-Loenzo RA (2019) A new approach for motor imagery classification based on sorted blind source separation, continuous wavelet transform, and convolutional neural network. Sensors 19(20):4541. https:\/\/doi.org\/10.3390\/s19204541","journal-title":"Sensors"},{"issue":"3","key":"9696_CR34","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1006\/jmca.1993.1030","volume":"16","author":"G Pfurtscheller","year":"1993","unstructured":"Pfurtscheller G, Flotzinger D, Kalcher J (1993) Brain\u2013computer interface\u2014a new communication device for handicapped persons. J Microcomput Appl 16(3):293\u2013299. https:\/\/doi.org\/10.1006\/jmca.1993.1030","journal-title":"J Microcomput Appl"},{"issue":"4","key":"9696_CR35","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. https:\/\/doi.org\/10.1109\/86.895946","journal-title":"IEEE Trans Rehabil Eng"},{"key":"9696_CR36","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2020.00918","volume":"14","author":"S Roy","year":"2020","unstructured":"Roy S, Chowdhury A, McCreadie K, Prasad G (2020) Deep learning based inter-subject continuous decoding of motor imagery for practical brain\u2013computer interfaces. Front Neurosci 14:563817. https:\/\/doi.org\/10.3389\/fnins.2020.00918","journal-title":"Front Neurosci"},{"issue":"11","key":"9696_CR37","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster M, Paliwal KK (1997) Bidirectional recurrent neural networks. IEEE Trans Signal Process 45(11):2673\u20132681. https:\/\/doi.org\/10.1109\/78.650093","journal-title":"IEEE Trans Signal Process"},{"key":"9696_CR38","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1007\/s40846-020-00538-3","volume":"40","author":"N Shajil","year":"2020","unstructured":"Shajil N, Mohan S, Srinivasan P, Arivudaiyanambi J, Arasappan Murrugesan A (2020) Multiclass classification of spatially filtered motor imagery EEG signals using convolutional neural network for BCI based applications. J Med Biol Eng 40:663\u2013672. https:\/\/doi.org\/10.1007\/s40846-020-00538-3","journal-title":"J Med Biol Eng"},{"key":"9696_CR39","doi-asserted-by":"crossref","unstructured":"Sifi N, Benali R, Dib N, Messaoudene K (2024) Enhanced sleep stage classification using EEG and EOG: a novel approach for feature selection with deep learning and gaussian noise data augmentation. Arab J Sci Eng 1\u201315","DOI":"10.1007\/s13369-024-09623-0"},{"key":"9696_CR40","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"9696_CR41","doi-asserted-by":"publisher","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139. https:\/\/doi.org\/10.1109\/CVPR.2015.7298594","DOI":"10.1109\/CVPR.2015.7298594"},{"issue":"1","key":"9696_CR42","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2560\/14\/1\/016003","volume":"14","author":"YR Tabar","year":"2016","unstructured":"Tabar YR, Halici U (2016) A novel deep learning approach for classification of EEG motor imagery signals. J Neural Eng 14(1):016003. https:\/\/doi.org\/10.1088\/1741-2560\/14\/1\/016003","journal-title":"J Neural Eng"},{"key":"9696_CR43","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2012.00055","author":"M Tangermann","year":"2012","unstructured":"Tangermann M, M\u00fcller K-R, Aertsen A, Birbaumer N, Braun C, Brunner C, Leeb R, Mehring C, Miller KJ, Mueller-Putz G, Nolte G, Pfurtscheller G, Preissl H, Schalk G, Schl\u00f6gl A, Vidaurre C, Waldert S, Blankertz B (2012) Review of the BCI competition IV. Front Neurosci. https:\/\/doi.org\/10.3389\/fnins.2012.00055","journal-title":"Front Neurosci"},{"key":"9696_CR44","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2018.00312","author":"M Tariq","year":"2018","unstructured":"Tariq M, Trivailo PM, Simic M (2018) EEG-based BCI control schemes for lower-limb assistive-robots. Front Hum Neurosci. https:\/\/doi.org\/10.3389\/fnhum.2018.00312","journal-title":"Front Hum Neurosci"},{"issue":"1","key":"9696_CR45","doi-asserted-by":"publisher","first-page":"22935","DOI":"10.1002\/ima.22935","volume":"34","author":"MJ Thrikkannoor Kolathod","year":"2024","unstructured":"Thrikkannoor Kolathod MJ, Sanjay M (2024) Use of covariance matrix images for electroencephalography signal classification for multiclass motor imagery-based brain\u2013computer interface. Int J Imaging Syst Technol 34(1):22935. https:\/\/doi.org\/10.1002\/ima.22935","journal-title":"Int J Imaging Syst Technol"},{"key":"9696_CR46","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.procs.2020.01.079","volume":"165","author":"S Velliangiri","year":"2019","unstructured":"Velliangiri S, Alagumuthukrishnan S et al (2019) A review of dimensionality reduction techniques for efficient computation. Proc Comput Sci 165:104\u2013111. https:\/\/doi.org\/10.1016\/j.procs.2020.01.079","journal-title":"Proc Comput Sci"},{"issue":"1","key":"9696_CR47","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1146\/annurev.bb.02.060173.001105","volume":"2","author":"JJ Vidal","year":"1973","unstructured":"Vidal JJ (1973) Toward direct brain\u2013computer communication. Annu Rev Biophys Bioeng 2(1):157\u2013180. https:\/\/doi.org\/10.1146\/annurev.bb.02.060173.001105","journal-title":"Annu Rev Biophys Bioeng"},{"issue":"51","key":"9696_CR48","doi-asserted-by":"publisher","first-page":"17849","DOI":"10.1073\/pnas.0403504101","volume":"101","author":"JR Wolpaw","year":"2004","unstructured":"Wolpaw JR, McFarland DJ (2004) Control of a two-dimensional movement signal by a noninvasive brain\u2013computer interface in humans. Proc Natl Acad Sci 101(51):17849\u201317854. https:\/\/doi.org\/10.1073\/pnas.0403504101","journal-title":"Proc Natl Acad Sci"},{"issue":"3","key":"9696_CR49","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/0013-4694(91)90040-B","volume":"78","author":"JR Wolpaw","year":"1991","unstructured":"Wolpaw JR, McFarland DJ, Neat GW, Forneris CA (1991) An EEG-based brain\u2013computer interface for cursor control. Electroencephalogr Clin Neurophysiol 78(3):252\u2013259","journal-title":"Electroencephalogr Clin Neurophysiol"},{"key":"9696_CR50","doi-asserted-by":"publisher","first-page":"6084","DOI":"10.1109\/ACCESS.2018.2889093","volume":"7","author":"B Xu","year":"2018","unstructured":"Xu B, Zhang L, Song A, Wu C, Li W, Zhang D, Xu G, Li H, Zeng H (2018) Wavelet transform time\u2013frequency image and convolutional network-based motor imagery EEG classification. IEEE Access 7:6084\u20136093. https:\/\/doi.org\/10.1109\/ACCESS.2018.2889093","journal-title":"IEEE Access"},{"key":"9696_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107390","volume":"105","author":"M Xu","year":"2020","unstructured":"Xu M, Yao J, Zhang Z, Li R, Yang B, Li C, Li J, Zhang J (2020) Learning EEG topographical representation for classification via convolutional neural network. Pattern Recogn 105:107390. https:\/\/doi.org\/10.1016\/j.patcog.2020.107390","journal-title":"Pattern Recogn"},{"issue":"10","key":"9696_CR52","doi-asserted-by":"publisher","first-page":"8042","DOI":"10.1109\/TIE.2017.2696503","volume":"64","author":"G Yu","year":"2017","unstructured":"Yu G, Yu M, Xu C (2017) Synchroextracting transform. IEEE Trans Ind Electron 64(10):8042\u20138054. https:\/\/doi.org\/10.1109\/TIE.2017.2696503","journal-title":"IEEE Trans Ind Electron"},{"key":"9696_CR53","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/978-1-4471-6392-3_4","volume-title":"Advances in physiological computing","author":"TO Zander","year":"2014","unstructured":"Zander TO, Br\u00f6nstrup J, Lorenz R, Krol LR (2014) Towards bci-based implicit control in human\u2013computer interaction. In: Fairclough SH, Gilleade K (eds) Advances in physiological computing. Springer, London, pp 67\u201390"},{"issue":"6","key":"9696_CR54","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ab3471","volume":"16","author":"R Zhang","year":"2019","unstructured":"Zhang R, Zong Q, Dou L, Zhao X (2019) A novel hybrid deep learning scheme for four-class motor imagery classification. J Neural Eng 16(6):066004. https:\/\/doi.org\/10.1088\/1741-2552\/ab3471","journal-title":"J Neural Eng"},{"issue":"4","key":"9696_CR55","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/abed81","volume":"18","author":"C Zhang","year":"2021","unstructured":"Zhang C, Kim Y-K, Eskandarian A (2021) EEG-inception: an accurate and robust end-to-end neural network for EEG-based motor imagery classification. J Neural Eng 18(4):046014. https:\/\/doi.org\/10.1088\/1741-2552\/abed81","journal-title":"J Neural Eng"},{"key":"9696_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2023.109953","volume":"398","author":"R Zhang","year":"2023","unstructured":"Zhang R, Liu G, Wen Y, Zhou W (2023) Self-attention-based convolutional neural network and time\u2013frequency common spatial pattern for enhanced motor imagery classification. J Neurosci Methods 398:109953. https:\/\/doi.org\/10.1016\/j.jneumeth.2023.109953","journal-title":"J Neurosci Methods"},{"issue":"10","key":"9696_CR57","doi-asserted-by":"publisher","first-page":"2164","DOI":"10.1109\/TNSRE.2019.2938295","volume":"27","author":"X Zhao","year":"2019","unstructured":"Zhao X, Zhang H, Zhu G, You F, Kuang S, Sun L (2019) A multi-branch 3d convolutional neural network for EEG-based motor imagery classification. IEEE Trans Neural Syst Rehabil Eng 27(10):2164\u20132177. https:\/\/doi.org\/10.1109\/TNSRE.2019.2938295","journal-title":"IEEE Trans Neural Syst Rehabil Eng"}],"container-title":["Evolving Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12530-025-09696-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12530-025-09696-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12530-025-09696-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T15:46:33Z","timestamp":1757173593000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12530-025-09696-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,27]]},"references-count":57,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["9696"],"URL":"https:\/\/doi.org\/10.1007\/s12530-025-09696-8","relation":{},"ISSN":["1868-6478","1868-6486"],"issn-type":[{"type":"print","value":"1868-6478"},{"type":"electronic","value":"1868-6486"}],"subject":[],"published":{"date-parts":[[2025,5,27]]},"assertion":[{"value":"18 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2025","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 have no conflict of interest to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study utilised open-access data, and no new data was generated during the investigation. Thus, ethics approval and consent to participate were not required.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The data used in this study is openly accessible and regulated by the Creative Commons Attribution No Derivatives (CC BY-ND 4.0) licence. Therefore, consent for publication is not required, as the data is freely available for use and redistribution under the terms of this licence.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"67"}}