{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T07:42:03Z","timestamp":1777621323551,"version":"3.51.4"},"reference-count":78,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T00:00:00Z","timestamp":1712534400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T00:00:00Z","timestamp":1712534400000},"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":["Sci. China Inf. Sci."],"published-print":{"date-parts":[[2024,5]]},"DOI":"10.1007\/s11432-023-3876-1","type":"journal-article","created":{"date-parts":[[2024,7,4]],"date-time":"2024-07-04T11:02:21Z","timestamp":1720090941000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["State-sensitive convolutional sparse coding for potential biomarker identification in brain signals"],"prefix":"10.1007","volume":"67","author":[{"given":"Puli","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Qi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,4,8]]},"reference":[{"key":"3876_CR1","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1038\/nature05278","volume":"444","author":"L Marshall","year":"2006","unstructured":"Marshall L, Helgad\u00f3ttir H, M\u00f6lle M, et al. Boosting slow oscillations during sleep potentiates memory. Nature, 2006, 444: 610\u2013613","journal-title":"Nature"},{"key":"3876_CR2","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.neuron.2016.02.028","volume":"90","author":"M Lundqvist","year":"2016","unstructured":"Lundqvist M, Rose J, Herman P, et al. Gamma and beta bursts underlie working memory. Neuron, 2016, 90: 152\u2013164","journal-title":"Neuron"},{"key":"3876_CR3","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1523\/JNEUROSCI.1887-19.2019","volume":"40","author":"J R Wessel","year":"2020","unstructured":"Wessel J R. \u03b2-bursts reveal the trial-to-trial dynamics of movement initiation and cancellation. J Neurosci, 2020, 40: 411\u2013423","journal-title":"J Neurosci"},{"key":"3876_CR4","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1176\/ajp.2007.164.3.483","volume":"164","author":"F Ferrarelli","year":"2007","unstructured":"Ferrarelli F, Huber R, Peterson M J, et al. Reduced sleep spindle activity in schizophrenia patients. Am J Psychiatry, 2007, 164: 483\u2013492","journal-title":"Am J Psychiatry"},{"key":"3876_CR5","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.biopsych.2011.08.008","volume":"71","author":"E J Wamsley","year":"2012","unstructured":"Wamsley E J, Tucker M A, Shinn A K, et al. Reduced sleep spindles and spindle coherence in schizophrenia: mechanisms of impaired memory consolidation? Biol Psychiatry, 2012, 71: 154\u2013161","journal-title":"Biol Psychiatry"},{"key":"3876_CR6","doi-asserted-by":"publisher","first-page":"1049","DOI":"10.1093\/brain\/awh425","volume":"128","author":"\u00c9 Limoges","year":"2005","unstructured":"Limoges \u00c9, Mottron L, Bolduc C, et al. Atypical sleep architecture and the autism phenotype. Brain, 2005, 128: 1049\u20131061","journal-title":"Brain"},{"key":"3876_CR7","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1109\/TCDS.2022.3159285","volume":"15","author":"X Sun","year":"2022","unstructured":"Sun X, Qi Y, Wang Y, et al. Convolutional multiple instance learning for sleep spindle detection with label refinement. IEEE Trans Cogn Dev Syst, 2022, 15: 272\u2013284","journal-title":"IEEE Trans Cogn Dev Syst"},{"key":"3876_CR8","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1093\/sleep\/5.1.47","volume":"5","author":"M Shibagaki","year":"1982","unstructured":"Shibagaki M, Kiyono S, Watanabe K. Spindle evolution in normal and mentally retarded children: a review. Sleep, 1982, 5: 47\u201357","journal-title":"Sleep"},{"key":"3876_CR9","doi-asserted-by":"publisher","first-page":"880","DOI":"10.1109\/TNSRE.2013.2282153","volume":"21","author":"S Li","year":"2013","unstructured":"Li S, Zhou W, Yuan Q, et al. Seizure prediction using spike rate of intracranial EEG. IEEE Trans Neural Syst Rehabil Eng, 2013, 21: 880\u2013886","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"3876_CR10","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1109\/JBHI.2021.3102247","volume":"26","author":"K Fukumori","year":"2021","unstructured":"Fukumori K, Yoshida N, Sugano H, et al. Epileptic spike detection using neural networks with linear-phase convolutions. IEEE J Biomed Health Inform, 2021, 26: 1045\u20131056","journal-title":"IEEE J Biomed Health Inform"},{"key":"3876_CR11","doi-asserted-by":"publisher","first-page":"2814","DOI":"10.1109\/JBHI.2020.2972286","volume":"24","author":"A Chahid","year":"2020","unstructured":"Chahid A, Albalawi F, Alotaiby T N, et al. QuPWM: feature extraction method for epileptic spike classification. IEEE J Biomed Health Inform, 2020, 24: 2814\u20132824","journal-title":"IEEE J Biomed Health Inform"},{"key":"3876_CR12","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1177\/1535759720957308","volume":"20","author":"J Jacobs","year":"2020","unstructured":"Jacobs J, Zijlmans M. HFO to measure seizure propensity and improve prognostication in patients with epilepsy. Epilepsy Curr, 2020, 20: 338\u2013347","journal-title":"Epilepsy Curr"},{"key":"3876_CR13","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1016\/j.clinph.2010.09.021","volume":"122","author":"M Zijlmans","year":"2011","unstructured":"Zijlmans M, Jacobs J, Kahn Y U, et al. Ictal and interictal high frequency oscillations in patients with focal epilepsy. Clin NeuroPhysiol, 2011, 122: 664\u2013671","journal-title":"Clin NeuroPhysiol"},{"key":"3876_CR14","doi-asserted-by":"crossref","unstructured":"Bai D, Liu T, Han X, et al. Application research on optimization algorithm of sEMG gesture recognition based on light CNN+LSTM model. Cyborg Bionic Syst, 2021, 2021","DOI":"10.34133\/2021\/9794610"},{"key":"3876_CR15","doi-asserted-by":"publisher","first-page":"e29086","DOI":"10.7554\/eLife.29086","volume":"6","author":"H Shin","year":"2017","unstructured":"Shin H, Law R, Tsutsui S, et al. The rate of transient beta frequency events predicts behavior across tasks and species. eLife, 2017, 6: e29086","journal-title":"eLife"},{"key":"3876_CR16","doi-asserted-by":"publisher","first-page":"117245","DOI":"10.1016\/j.neuroimage.2020.117245","volume":"222","author":"B Brady","year":"2020","unstructured":"Brady B, Power L, Bardouille T. Age-related trends in neuromagnetic transient beta burst characteristics during a sensori-motor task and rest in the Cam-CAN open-access dataset. NeuroImage, 2020, 222: 117245","journal-title":"NeuroImage"},{"key":"3876_CR17","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.neuroimage.2015.03.001","volume":"112","author":"J B Caplan","year":"2015","unstructured":"Caplan J B, Bottomley M, Kang P, et al. Distinguishing rhythmic from non-rhythmic brain activity during rest in healthy neurocognitive aging. NeuroImage, 2015, 112: 341\u2013352","journal-title":"NeuroImage"},{"key":"3876_CR18","doi-asserted-by":"publisher","first-page":"116331","DOI":"10.1016\/j.neuroimage.2019.116331","volume":"206","author":"J Q Kosciessa","year":"2020","unstructured":"Kosciessa J Q, Grandy T H, Garrett D D, et al. Single-trial characterization of neural rhythms: potential and challenges. NeuroImage, 2020, 206: 116331","journal-title":"NeuroImage"},{"key":"3876_CR19","doi-asserted-by":"publisher","first-page":"1670","DOI":"10.1152\/jn.00315.2021","volume":"126","author":"M S Fabus","year":"2021","unstructured":"Fabus M S, Quinn A J, Warnaby C E, et al. Automatic decomposition of electrophysiological data into distinct nonsinusoidal oscillatory modes. J NeuroPhysiol, 2021, 126: 1670\u20131684","journal-title":"J NeuroPhysiol"},{"key":"3876_CR20","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1098\/rspa.1998.0193","volume":"454","author":"N E Huang","year":"1998","unstructured":"Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc Lond A, 1998, 454: 903\u2013995","journal-title":"Proc R Soc Lond A"},{"key":"3876_CR21","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1152\/jn.00273.2019","volume":"122","author":"S Cole","year":"2019","unstructured":"Cole S, Voytek B. Cycle-by-cycle analysis of neural oscillations. J NeuroPhysiol, 2019, 122: 849\u2013861","journal-title":"J NeuroPhysiol"},{"key":"3876_CR22","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1007\/s00422-013-0574-2","volume":"108","author":"M Abeles","year":"2014","unstructured":"Abeles M. Revealing instances of coordination among multiple cortical areas. Biol Cybern, 2014, 108: 665\u2013675","journal-title":"Biol Cybern"},{"key":"3876_CR23","doi-asserted-by":"publisher","first-page":"1810","DOI":"10.1152\/jn.00956.2015","volume":"115","author":"I Tal","year":"2016","unstructured":"Tal I, Abeles M. Temporal accuracy of human cortico-cortical interactions. J NeuroPhysiol, 2016, 115: 1810\u20131820","journal-title":"J NeuroPhysiol"},{"key":"3876_CR24","doi-asserted-by":"publisher","first-page":"610","DOI":"10.1162\/neco_a_01054","volume":"30","author":"I Tal","year":"2018","unstructured":"Tal I, Abeles M. Imaging the spatiotemporal dynamics of cognitive processes at high temporal resolution. Neural Comput, 2018, 30: 610\u2013630","journal-title":"Neural Comput"},{"key":"3876_CR25","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/S0893-6080(00)00026-5","volume":"13","author":"A Hyv\u00e4rinen","year":"2000","unstructured":"Hyv\u00e4rinen A, Oja E. Independent component analysis: algorithms and applications. Neural Netw, 2000, 13: 411\u2013430","journal-title":"Neural Netw"},{"key":"3876_CR26","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.neuroimage.2015.11.047","volume":"126","author":"D Vidaurre","year":"2016","unstructured":"Vidaurre D, Quinn A J, Baker A P, et al. Spectrally resolved fast transient brain states in electrophysiological data. NeuroImage, 2016, 126: 81\u201395","journal-title":"NeuroImage"},{"key":"3876_CR27","doi-asserted-by":"publisher","first-page":"116818","DOI":"10.1016\/j.neuroimage.2020.116818","volume":"215","author":"R Becker","year":"2020","unstructured":"Becker R, Vidaurre D, Quinn A J, et al. Transient spectral events in resting state MEG predict individual task responses. NeuroImage, 2020, 215: 116818","journal-title":"NeuroImage"},{"key":"3876_CR28","doi-asserted-by":"publisher","first-page":"118850","DOI":"10.1016\/j.neuroimage.2021.118850","volume":"247","author":"N Coquelet","year":"2022","unstructured":"Coquelet N, de Tiege X, Roshchupkina L, et al. Microstates and power envelope hidden Markov modeling probe bursting brain activity at different timescales. Neurolmage, 2022, 247: 118850","journal-title":"Neurolmage"},{"key":"3876_CR29","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1016\/j.neuroimage.2017.11.062","volume":"180","author":"C M Michel","year":"2018","unstructured":"Michel C M, Koenig T. EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: a review. NeuroImage, 2018, 180: 577\u2013593","journal-title":"NeuroImage"},{"key":"3876_CR30","doi-asserted-by":"crossref","unstructured":"Jost P, Vandergheynst P, Lesage S, et al. Motif: an efficient algorithm for learning translation invariant dictionaries. In: Proceedings of IEEE International Conference on Acoustics Speech and Signal Processing, Toulouse, 2006. 857\u2013860","DOI":"10.1109\/ICASSP.2006.1661411"},{"key":"3876_CR31","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/TBME.2015.2499241","volume":"63","author":"A J Brockmeier","year":"2016","unstructured":"Brockmeier A J, Principe J C. Learning recurrent waveforms within EEGs. IEEE Trans Biomed Eng, 2016, 63: 43\u201354","journal-title":"IEEE Trans Biomed Eng"},{"key":"3876_CR32","unstructured":"Jas M, Tour T D, Simsekli U, et al. Learning the morphology of brain signals using alpha-stable convolutional sparse coding. In: Proceedings of Advances in Neural Information Processing Systems, Long Beach, 2017. 1099\u20131108"},{"key":"3876_CR33","unstructured":"Tour T D, Moreau T, Jas M, et al. Multivariate convolutional sparse coding for electromagnetic brain signals. In: Proceedings of Advances in Neural Information Processing Systems, Montreal, 2018. 1\u201311"},{"key":"3876_CR34","doi-asserted-by":"publisher","first-page":"4885","DOI":"10.1073\/pnas.1604135113","volume":"113","author":"M A Sherman","year":"2016","unstructured":"Sherman M A, Lee S, Law R, et al. Neural mechanisms of transient neocortical beta rhythms: converging evidence from humans, computational modeling, monkeys, and mice. Proc Natl Acad Sci USA, 2016, 113: 4885\u20134894","journal-title":"Proc Natl Acad Sci USA"},{"key":"3876_CR35","volume-title":"Imaging Brain Function with EEG: Advanced Temporal and Spatial Analysis of Electroencephalographic Signals","author":"W Freeman","year":"2012","unstructured":"Freeman W, Quiroga R Q. Imaging Brain Function with EEG: Advanced Temporal and Spatial Analysis of Electroencephalographic Signals. New York: Springer Science & Business Media, 2012"},{"key":"3876_CR36","doi-asserted-by":"publisher","first-page":"119809","DOI":"10.1016\/j.neuroimage.2022.119809","volume":"267","author":"L Power","year":"2023","unstructured":"Power L, Allain C, Moreau T, et al. Using convolutional dictionary learning to detect task-related neuromagnetic transients and ageing trends in a large open-access dataset. NeuroImage, 2023, 267: 119809","journal-title":"NeuroImage"},{"key":"3876_CR37","unstructured":"Allain C, Gramfort A, Moreau T. Dripp: driven point processes to model stimuli induced patterns in M\/EEG signals. In: Proceedings of the 10th International Conference on Learning Representations, 2022. 1\u201325"},{"key":"3876_CR38","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1109\/JBHI.2018.2832538","volume":"23","author":"Y Jiao","year":"2018","unstructured":"Jiao Y, Zhang Y, Chen X, et al. Sparse group representation model for motor imagery EEG classification. IEEE J Biomed Health Inform, 2018, 23: 631\u2013641","journal-title":"IEEE J Biomed Health Inform"},{"key":"3876_CR39","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1109\/JBHI.2018.2796588","volume":"23","author":"J Huang","year":"2018","unstructured":"Huang J, Zhu Q, Hao X, et al. Identifying resting-state multifrequency biomarkers via tree-guided group sparse learning for schizophrenia classification. IEEE J Biomed Health Inform, 2018, 23: 342\u2013350","journal-title":"IEEE J Biomed Health Inform"},{"key":"3876_CR40","doi-asserted-by":"crossref","unstructured":"Zhang Q, Li B X. Discriminative k-SVD for dictionary learning in face recognition. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, 2010. 2691\u20132698","DOI":"10.1109\/CVPR.2010.5539989"},{"key":"3876_CR41","doi-asserted-by":"crossref","unstructured":"Jiang Z L, Lin Z, Davis L S. Learning a discriminative dictionary for sparse coding via label consistent k-SVD. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2011. 1697\u20131704","DOI":"10.1109\/CVPR.2011.5995354"},{"key":"3876_CR42","doi-asserted-by":"publisher","first-page":"132105","DOI":"10.1007\/s11432-021-3493-7","volume":"66","author":"G Cheng","year":"2023","unstructured":"Cheng G, Lai P, Gao D, et al. Class attention network for image recognition. Sci China Inf Sci, 2023, 66: 132105","journal-title":"Sci China Inf Sci"},{"key":"3876_CR43","doi-asserted-by":"publisher","first-page":"2519","DOI":"10.1109\/TBME.2018.2806958","volume":"65","author":"A Iqbal","year":"2018","unstructured":"Iqbal A, Seghouane A K, Adali T. Shared and subject-specific dictionary learning (ShSSDL) algorithm for multisubject fMRI data analysis. IEEE Trans Biomed Eng, 2018, 65: 2519\u20132528","journal-title":"IEEE Trans Biomed Eng"},{"key":"3876_CR44","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1109\/TIP.2013.2290593","volume":"23","author":"S H Gao","year":"2014","unstructured":"Gao S H, Tsang I W H, Ma Y. Learning category-specific dictionary and shared dictionary for fine-grained image categorization. IEEE Trans Image Process, 2014, 23: 623\u2013634","journal-title":"IEEE Trans Image Process"},{"key":"3876_CR45","doi-asserted-by":"publisher","first-page":"2036","DOI":"10.1109\/TMI.2015.2418734","volume":"34","author":"S Zhao","year":"2015","unstructured":"Zhao S, Han J, Lv J, et al. Supervised dictionary learning for inferring concurrent brain networks. IEEE Trans Med Imag, 2015, 34: 2036\u20132045","journal-title":"IEEE Trans Med Imag"},{"key":"3876_CR46","unstructured":"Grosse R, Raina R, Kwong H, et al. Shift-invariant sparse coding for audio classification. In: Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence, Vancouver, 2007. 149\u2013158"},{"key":"3876_CR47","unstructured":"Pachitariu M, Packer A M, Pettit N, et al. Extracting regions of interest from biological images with convolutional sparse block coding. In: Proceedings of Advances in Neural Information Processing Systems, 2013. 1\u20139"},{"key":"3876_CR48","unstructured":"Kavukcuoglu K, Sermanet P, Boureau Y L, et al. Learning convolutional feature hierarchies for visual recognition. In: Proceedings of Advances in Neural Information Processing Systems, Vancouver, 2010. 1\u20139"},{"key":"3876_CR49","doi-asserted-by":"crossref","unstructured":"Zeiler M D, Krishnan D, Taylor G W, et al. Deconvolutional networks. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, 2010. 2528\u20132535","DOI":"10.1109\/CVPR.2010.5539957"},{"key":"3876_CR50","series-title":"Dissertation for Ph.D. Degree","volume-title":"Learning and exploiting recurrent patterns in neural data","author":"A J Brockmeier","year":"2014","unstructured":"Brockmeier A J. Learning and exploiting recurrent patterns in neural data. Dissertation for Ph.D. Degree. Gainesville: University of Florida, 2014"},{"key":"3876_CR51","doi-asserted-by":"publisher","first-page":"026016","DOI":"10.1088\/1741-2552\/abd577","volume":"18","author":"S Akella","year":"2021","unstructured":"Akella S, Mohebi A, Principe J C, et al. Marked point process representation of oscillatory dynamics underlying working memory. J Neural Eng, 2021, 18: 026016","journal-title":"J Neural Eng"},{"key":"3876_CR52","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780195301069.001.0001","volume-title":"Rhythms of the Brain","author":"G Buzsaki","year":"2006","unstructured":"Buzsaki G. Rhythms of the Brain. New York: Oxford University Press, 2006"},{"key":"3876_CR53","doi-asserted-by":"publisher","first-page":"1248","DOI":"10.3389\/fnins.2019.01248","volume":"13","author":"C A Loza","year":"2019","unstructured":"Loza C A, Reddy C G, Akella S, et al. Discrimination of movement-related cortical potentials exploiting unsupervised learned representations from ECoGs. Front Neurosci, 2019, 13: 1248","journal-title":"Front Neurosci"},{"key":"3876_CR54","doi-asserted-by":"crossref","unstructured":"Ramirez I, Sprechmann P, Sapiro G. Classification and clustering via dictionary learning with structured incoherence and shared features. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, 2010. 3501\u20133508","DOI":"10.1109\/CVPR.2010.5539964"},{"key":"3876_CR55","doi-asserted-by":"crossref","unstructured":"Kong S, Wang D. A dictionary learning approach for classification: separating the particularity and the commonality. In: Proceedings of European Conference on Computer Vision, Heraklion, 2012. 186\u2013199","DOI":"10.1007\/978-3-642-33718-5_14"},{"key":"3876_CR56","doi-asserted-by":"crossref","unstructured":"Ramirez I, Lecumberry F, Sapiro G. Universal priors for sparse modeling. In: Proceedings of IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Aruba, 2009. 197\u2013200","DOI":"10.1109\/CAMSAP.2009.5413302"},{"key":"3876_CR57","doi-asserted-by":"crossref","unstructured":"Chalasani R, Pr incipe J C, Ramakrishnan N. A fast proximal method for convolutional sparse coding. In: Proceedings of International Joint Conference on Neural Networks, Dallas, 2013. 1\u20135","DOI":"10.1109\/IJCNN.2013.6706854"},{"key":"3876_CR58","doi-asserted-by":"publisher","first-page":"3397","DOI":"10.1109\/78.258082","volume":"41","author":"S G Mallat","year":"1993","unstructured":"Mallat S G, Zhang Z F. Matching pursuits with time-frequency dictionaries. IEEE Trans Signal Process, 1993, 41: 3397\u20133415","journal-title":"IEEE Trans Signal Process"},{"key":"3876_CR59","doi-asserted-by":"publisher","first-page":"1856","DOI":"10.1109\/TPAMI.2005.230","volume":"27","author":"D Charalampidis","year":"2005","unstructured":"Charalampidis D. A modified k-means algorithm for circular invariant clustering. IEEE Trans Pattern Anal Machine Intell, 2005, 27: 1856\u20131865","journal-title":"IEEE Trans Pattern Anal Machine Intell"},{"key":"3876_CR60","doi-asserted-by":"publisher","first-page":"046043","DOI":"10.1088\/1741-2552\/ac86a3","volume":"19","author":"Y Huang","year":"2022","unstructured":"Huang Y, Zhang X, Shen X, et al. Extracting synchronized neuronal activity from local field potentials based on a marked point process framework. J Neural Eng, 2022, 19: 046043","journal-title":"J Neural Eng"},{"key":"3876_CR61","doi-asserted-by":"publisher","first-page":"95","DOI":"10.3389\/fnsys.2017.00095","volume":"11","author":"C A Loza","year":"2017","unstructured":"Loza C A, Okun M S, Pr\u00edncipe J C. A marked point process framework for extracellular electrical potentials. Front Syst Neurosci, 2017, 11: 95","journal-title":"Front Syst Neurosci"},{"key":"3876_CR62","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1016\/S1525-5050(03)00105-7","volume":"4","author":"M Winterhalder","year":"2003","unstructured":"Winterhalder M, Maiwald T, Voss H U, et al. The seizure prediction characteristic: a general framework to assess and compare seizure prediction methods. Epilepsy Behav, 2003, 4: 318\u2013325","journal-title":"Epilepsy Behav"},{"key":"3876_CR63","doi-asserted-by":"publisher","first-page":"2616","DOI":"10.1093\/brain\/awg265","volume":"126","author":"R Aschenbrenner-Scheibe","year":"2003","unstructured":"Aschenbrenner-Scheibe R. How well can epileptic seizures be predicted? An evaluation of a nonlinear method. Brain, 2003, 126: 2616\u20132626","journal-title":"Brain"},{"key":"3876_CR64","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.yebeh.2008.01.011","volume":"13","author":"J R Hughes","year":"2008","unstructured":"Hughes J R. Gamma, fast, and ultrafast waves of the brain: their relationships with epilepsy and behavior. Epilepsy Behav, 2008, 13: 25\u201331","journal-title":"Epilepsy Behav"},{"key":"3876_CR65","doi-asserted-by":"publisher","first-page":"1908","DOI":"10.1111\/epi.16296","volume":"60","author":"W J E M Zweiphenning","year":"2019","unstructured":"Zweiphenning W J E M, Keijzer H M, van Diessen E, et al. Increased gamma and decreased fast ripple connections of epileptic tissue: a high-frequency directed network approach. Epilepsia, 2019, 60: 1908\u20131920","journal-title":"Epilepsia"},{"key":"3876_CR66","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/S1567-4231(03)03009-0","volume":"3","author":"P Kahane","year":"2003","unstructured":"Kahane P, Minotti L, Hoffmann D, et al. Invasive EEG in the definition of the seizure onset zone: depth electrodes. Handbook Clin Neurophys, 2003, 3: 109\u2013133","journal-title":"Handbook Clin Neurophys"},{"key":"3876_CR67","doi-asserted-by":"publisher","first-page":"5321","DOI":"10.1073\/pnas.1401752111","volume":"111","author":"S P Burns","year":"2014","unstructured":"Burns S P, Santaniello S, Yaffe R B, et al. Network dynamics of the brain and influence of the epileptic seizure onset zone. Proc Natl Acad Sci USA, 2014, 111: 5321\u20135330","journal-title":"Proc Natl Acad Sci USA"},{"key":"3876_CR68","doi-asserted-by":"publisher","first-page":"1066","DOI":"10.1093\/brain\/aww019","volume":"139","author":"P J Karoly","year":"2016","unstructured":"Karoly P J, Freestone D R, Boston R, et al. Interictal spikes and epileptic seizures: their relationship and underlying rhythmicity. Brain, 2016, 139: 1066\u20131078","journal-title":"Brain"},{"key":"3876_CR69","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1109\/JBHI.2021.3100297","volume":"26","author":"T Dissanayake","year":"2021","unstructured":"Dissanayake T, Fernando T, Denman S, et al. Geometric deep learning for subject independent epileptic seizure prediction using scalp EEG signals. IEEE J Biomed Health Inform, 2021, 26: 527\u2013538","journal-title":"IEEE J Biomed Health Inform"},{"key":"3876_CR70","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1137\/080716542","volume":"2","author":"A Beck","year":"2009","unstructured":"Beck A, Teboulle M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imag Sci, 2009, 2: 183\u2013202","journal-title":"SIAM J Imag Sci"},{"key":"3876_CR71","doi-asserted-by":"publisher","first-page":"4830","DOI":"10.1523\/JNEUROSCI.2208-16.2017","volume":"37","author":"S R Cole","year":"2017","unstructured":"Cole S R, van der Meij R, Peterson E J, et al. Nonsinusoidal beta oscillations reflect cortical pathophysiology in Parkinson\u2019s disease. J Neurosci, 2017, 37: 4830\u20134840","journal-title":"J Neurosci"},{"key":"3876_CR72","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1523\/ENEURO.0151-19.2019","volume":"6","author":"N Jackson","year":"2019","unstructured":"Jackson N, Cole S R, Voytek B, et al. Characteristics of waveform shape in Parkinson\u2019s disease detected with scalp electroencephalography. eNeuro, 2019, 6: 1\u201311","journal-title":"eNeuro"},{"key":"3876_CR73","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.sleep.2020.07.044","volume":"75","author":"Y Y Weng","year":"2020","unstructured":"Weng Y Y, Lei X, Yu J. Sleep spindle abnormalities related to Alzheimer\u2019s disease: a systematic mini-review. Sleep Med, 2020, 75: 37\u201344","journal-title":"Sleep Med"},{"key":"3876_CR74","doi-asserted-by":"publisher","first-page":"1434","DOI":"10.1038\/s41467-023-37088-6","volume":"14","author":"Y Liang","year":"2023","unstructured":"Liang Y, Liang J, Song C, et al. Complexity of cortical wave patterns of the wake mouse cortex. Nat Commun, 2023, 14: 1434","journal-title":"Nat Commun"},{"key":"3876_CR75","doi-asserted-by":"publisher","first-page":"2933","DOI":"10.1109\/TNSRE.2023.3250958","volume":"31","author":"X Sun","year":"2023","unstructured":"Sun X, Qi Y, Ma X, et al. Consformer: consciousness detection using transformer networks with correntropy-based measures. IEEE Trans Neural Syst Rehabil Eng, 2023, 31: 2933\u20132943","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"3876_CR76","doi-asserted-by":"publisher","first-page":"1","DOI":"10.34133\/2022\/9794641","volume":"2022","author":"N Babaei","year":"2022","unstructured":"Babaei N, Hannani N, Dabanloo N J, et al. A systematic review of the use of commercial wearable activity trackers for monitoring recovery in individuals undergoing total hip replacement surgery. Cyborg Bionic Syst, 2022, 2022: 1\u201316","journal-title":"Cyborg Bionic Syst"},{"key":"3876_CR77","unstructured":"Li W H, Qi Y, Pan G. Online neural sequence detection with hierarchical dirichlet point process. In: Proceedings of Advances in Neural Information Processing Systems, 2022. 6654\u20136665"},{"key":"3876_CR78","doi-asserted-by":"publisher","first-page":"1863","DOI":"10.1162\/neco_a_01306","volume":"32","author":"C Qian","year":"2020","unstructured":"Qian C, Sun X, Wang Y, et al. Binless kernel machine: modeling spike train transformation for cognitive neural prostheses. Neural Comput, 2020, 32: 1863\u20131900","journal-title":"Neural Comput"}],"container-title":["Science China Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-023-3876-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11432-023-3876-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-023-3876-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T21:02:31Z","timestamp":1750366951000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11432-023-3876-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,8]]},"references-count":78,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,5]]}},"alternative-id":["3876"],"URL":"https:\/\/doi.org\/10.1007\/s11432-023-3876-1","relation":{},"ISSN":["1674-733X","1869-1919"],"issn-type":[{"value":"1674-733X","type":"print"},{"value":"1869-1919","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,8]]},"assertion":[{"value":"9 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 September 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 April 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"152401"}}