{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T07:40:27Z","timestamp":1763624427256,"version":"3.45.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,9]],"date-time":"2025-07-09T00:00:00Z","timestamp":1752019200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the scholarships from China scholarship Council","award":["202106060035"],"award-info":[{"award-number":["202106060035"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s11517-025-03388-w","type":"journal-article","created":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T09:19:56Z","timestamp":1752139196000},"page":"3447-3460","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Brain region localization: a rapid Parkinson\u2019s disease detection method based on EEG signals"],"prefix":"10.1007","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7849-4950","authenticated-orcid":false,"given":"Mingliang","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenghao","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Timo","family":"Hamalainen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengyu","family":"Cong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,9]]},"reference":[{"issue":"9996","key":"3388_CR1","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1016\/S0140-6736(14)61393-3","volume":"386","author":"LV Kalia","year":"2015","unstructured":"Kalia LV, Lang AE (2015) Parkinson\u2019s disease. The Lancet 386(9996):896\u2013912. https:\/\/doi.org\/10.1016\/S0140-6736(14)61393-3","journal-title":"The Lancet"},{"key":"3388_CR2","doi-asserted-by":"publisher","first-page":"1225","DOI":"10.1007\/s00521-016-2756-z","volume":"30","author":"R Yuvaraj","year":"2018","unstructured":"Yuvaraj R, Rajendra Acharya U, Hagiwara Y (2018) A novel Parkinson\u2019s disease diagnosis index using higher-order spectra features in EEG signals. Neural Comput Appl 30:1225\u20131235. https:\/\/doi.org\/10.1007\/s00521-016-2756-z","journal-title":"Neural Comput Appl"},{"issue":"10","key":"3388_CR3","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1111\/cns.13429","volume":"26","author":"M Ugrumov","year":"2020","unstructured":"Ugrumov M (2020) Development of early diagnosis of Parkinson\u2019s disease: illusion or reality? CNS Neurosci Therap 26(10):997\u20131009. https:\/\/doi.org\/10.1111\/cns.13429","journal-title":"CNS Neurosci Therap"},{"issue":"7","key":"3388_CR4","doi-asserted-by":"publisher","first-page":"e12472","DOI":"10.1111\/EXSY.12472","volume":"39","author":"AA Bhurane","year":"2022","unstructured":"Bhurane AA, Dhok S, Sharma M, Yuvaraj R, Murugappan M, Rajendra Acharya U (2022) Diagnosis of Parkinson\u2019s disease from electroencephalography signals using linear and self-similarity features. Expert Syst 39(7):e12472. https:\/\/doi.org\/10.1111\/EXSY.12472","journal-title":"Expert Syst"},{"key":"3388_CR5","doi-asserted-by":"publisher","unstructured":"Hamidi A, Mohamed-Pour K, Yousefi M (2024) Forged channel: a breakthrough approach for accurate Parkinson\u2019s disease classification using leave-one-subject-out cross-validation. In: 2024 32nd International conference on electrical engineering (ICEE), pp 1\u20135. https:\/\/doi.org\/10.1109\/ICEE63041.2024.10667765","DOI":"10.1109\/ICEE63041.2024.10667765"},{"key":"3388_CR6","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1007\/s11571-013-9247-z","volume":"7","author":"C-X Han","year":"2013","unstructured":"Han C-X, Wang J, Yi G-S, Che Y-Q (2013) Investigation of EEG abnormalities in the early stage of Parkinson\u2019s disease. Cognit Neurodyn 7:351\u2013359. https:\/\/doi.org\/10.1007\/s11571-013-9247-z","journal-title":"Cognit Neurodyn"},{"key":"3388_CR7","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.parkreldis.2020.08.001","volume":"79","author":"MdF Anjum","year":"2020","unstructured":"Anjum MdF, Dasgupta S, Mudumbai R, Singh A, Cavanagh JF, Narayanan NS (2020) Linear predictive coding distinguishes spectral EEG features of Parkinson\u2019s disease. Parkinson Relat Disorders 79:79\u201385. https:\/\/doi.org\/10.1016\/j.parkreldis.2020.08.001","journal-title":"Parkinson Relat Disorders"},{"key":"3388_CR8","doi-asserted-by":"publisher","first-page":"10927","DOI":"10.1007\/s00521-018-3689-5","volume":"32","author":"SL Oh","year":"2020","unstructured":"Oh SL, Hagiwara Y, Raghavendra U, Yuvaraj R, Arunkumar N, Murugappan M, Rajendra Acharya U (2020) A deep learning approach for Parkinson\u2019s disease diagnosis from EEG signals. Neural Comput Appl 32:10927\u201310933. https:\/\/doi.org\/10.1007\/s00521-018-3689-5","journal-title":"Neural Comput Appl"},{"issue":"2","key":"3388_CR9","doi-asserted-by":"publisher","first-page":"e0263159","DOI":"10.1371\/journal.pone.0263159","volume":"17","author":"S Shaban","year":"2022","unstructured":"Shaban S et al (2022) Resting-state electroencephalography based deep-learning for the detection of Parkinson\u2019s disease. PLOS One 17(2):e0263159. https:\/\/doi.org\/10.1371\/journal.pone.0263159","journal-title":"PLOS One"},{"key":"3388_CR10","doi-asserted-by":"publisher","unstructured":"Yang C-Y, Huang Y-Z (2022) Parkinson\u2019s disease classification using machine learning approaches and resting-state EEG. J Med Biol Eng 42. https:\/\/doi.org\/10.1007\/s40846-022-00695-7","DOI":"10.1007\/s40846-022-00695-7"},{"key":"3388_CR11","doi-asserted-by":"publisher","unstructured":"Li K, Ao B, Wu X, Wen Q, Ul\u00a0Haq E, Yin J (2023) Parkinson\u2019s disease detection and classification using EEG based on deep CNN-LSTM model. Biotechnol Genetic Eng Rev 1\u201320. https:\/\/doi.org\/10.1080\/02648725.2023.2200333","DOI":"10.1080\/02648725.2023.2200333"},{"key":"3388_CR12","doi-asserted-by":"publisher","first-page":"106946","DOI":"10.1016\/j.bspc.2024.106946","volume":"100","author":"K Kumar","year":"2025","unstructured":"Kumar K, Ghosh R (2025) A multi-modal Parkinson\u2019s disease diagnosis system from EEG signals and online handwritten tasks using grey wolf optimization based deep learning model. Biomedi Signal Process Control 100:106946. https:\/\/doi.org\/10.1016\/j.bspc.2024.106946","journal-title":"Biomedi Signal Process Control"},{"key":"3388_CR13","doi-asserted-by":"publisher","first-page":"168","DOI":"10.33093\/jiwe.2025.4.1.13","volume":"4","author":"T Hasib","year":"2025","unstructured":"Hasib T, Vijayakumar V, Kannan R (2025) Early identification of Parkinson\u2019s disease using time frequency analysis on EEG signals. J Inf Web Eng 4:168\u2013183. https:\/\/doi.org\/10.33093\/jiwe.2025.4.1.13","journal-title":"J Inf Web Eng"},{"key":"3388_CR14","doi-asserted-by":"publisher","first-page":"107703","DOI":"10.1109\/ACCESS.2023.3319248","volume":"11","author":"SQA Rizvi","year":"2023","unstructured":"Rizvi SQA, Wang G, Khan A, Hasan MK, Ghazal TM, Khan AUR (2023) Classifying Parkinson\u2019s disease using resting state electroencephalogram signals and UEN-PDNet. IEEE Access 11:107703\u2013107724. https:\/\/doi.org\/10.1109\/ACCESS.2023.3319248","journal-title":"IEEE Access"},{"key":"3388_CR15","doi-asserted-by":"publisher","first-page":"107031","DOI":"10.1016\/j.compbiomed.2023.107031","volume":"161","author":"M Nour","year":"2023","unstructured":"Nour M, Senturk U, Polat K (2023) Diagnosis and classification of Parkinson\u2019s disease using ensemble learning and 1D-PDCOVNN. Comput Biol Med 161:107031. https:\/\/doi.org\/10.1016\/j.compbiomed.2023.107031","journal-title":"Comput Biol Med"},{"issue":"15","key":"3388_CR16","doi-asserted-by":"publisher","first-page":"17017","DOI":"10.1109\/JSEN.2021.3080135","volume":"21","author":"SK Khare","year":"2021","unstructured":"Khare SK, Bajaj V, Rajendra Acharya U (2021) PDCNNet: an automatic framework for the detection of Parkinson\u2019s disease using EEG signals. IEEE Sens J 21(15):17017\u201317024. https:\/\/doi.org\/10.1109\/JSEN.2021.3080135","journal-title":"IEEE Sens J"},{"issue":"14","key":"3388_CR17","doi-asserted-by":"publisher","first-page":"1740","DOI":"10.3390\/electronics10141740","volume":"10","author":"HW Loh","year":"2021","unstructured":"Loh HW, Ooi CP, Palmer E, Barua PD, Dogan S, Tuncer T, Baygin M, Rajendra Acharya U (2021) GaborPDNet: Gabor transformation and deep neural network for Parkinson\u2019s disease detection using EEG signals. Electronics 10(14):1740. https:\/\/doi.org\/10.3390\/electronics10141740","journal-title":"Electronics"},{"key":"3388_CR18","doi-asserted-by":"publisher","unstructured":"Guo Z, Xu Y, Rosenzweig J, McClelland VM, Rosenzweig I, Cvetkovic Z (2024) Subband independent component analysis for coherence enhancement. IEEE Trans Biomed Eng 71(8). https:\/\/doi.org\/10.1109\/TBME.2024.3370638","DOI":"10.1109\/TBME.2024.3370638"},{"key":"3388_CR19","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1016\/j.neuroimage.2013.10.027","volume":"86","author":"A Gramfort","year":"2013","unstructured":"Gramfort A, Luessi M, Larson E, Engemann DA, Strohmeier D, Brodbeck C, Goj R, Jas M, Brooks T, Parkkonen L, H\u00e4m\u00e4l\u00e4inen MS (2013) Mne software for processing meg and EEG data. NeuroImage 86:446\u2013460. https:\/\/doi.org\/10.1016\/j.neuroimage.2013.10.027","journal-title":"NeuroImage"},{"key":"3388_CR20","doi-asserted-by":"publisher","unstructured":"Esch L, Dinh C, Larson E, Engemann D, Jas M, Khan S, Gramfort A, H\u00e4m\u00e4l\u00e4inen MS (2019) MNE: software for acquiring, processing, and visualizing MEG\/EEG data. In: Supek S, Aine CH (eds) Magnetoencephalography: from signals to dynamic cortical networks. Springer, pp 355\u2013371. https:\/\/doi.org\/10.1007\/978-3-030-00087-5_59","DOI":"10.1007\/978-3-030-00087-5_59"},{"issue":"3","key":"3388_CR21","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1109\/72.761722","volume":"10","author":"A Hyvarinen","year":"1999","unstructured":"Hyvarinen A (1999) Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans Neural Netw 10(3):626\u2013634. https:\/\/doi.org\/10.1109\/72.761722","journal-title":"IEEE Trans Neural Netw"},{"key":"3388_CR22","doi-asserted-by":"publisher","unstructured":"Saeed S, Jonathon C (2007) EEG signal processing. John Wiley & Sons. https:\/\/doi.org\/10.1002\/9780470511923","DOI":"10.1002\/9780470511923"},{"issue":"1","key":"3388_CR23","doi-asserted-by":"publisher","first-page":"10124","DOI":"10.1038\/s41467-024-54243-9","volume":"15","author":"B Wan","year":"2024","unstructured":"Wan B, Saberi A, Paquola C, Schaare HL, Hettwer MD, Royer J, John A, Dorfschmidt L, Bayrak \u015e, Bethlehem RAI et al (2024) Microstructural asymmetry in the human cortex. Nat Commun 15(1):10124. https:\/\/doi.org\/10.1038\/s41467-024-54243-9","journal-title":"Nat Commun"},{"issue":"1","key":"3388_CR24","doi-asserted-by":"publisher","first-page":"22547","DOI":"10.1038\/s41598-022-26644-7","volume":"12","author":"M Aljalal","year":"2022","unstructured":"Aljalal M, Aldosari SA, Molinas M, AlSharabi K, Alturki FA (2022) Detection of Parkinson\u2019s disease from EEG signals using discrete wavelet transform, different entropy measures, and machine learning techniques. Sci Rep 12(1):22547. https:\/\/doi.org\/10.1038\/s41598-022-26644-7","journal-title":"Sci Rep"},{"key":"3388_CR25","doi-asserted-by":"publisher","unstructured":"Yuan Y, Xun G, Jia K, Zhang A (2018) A multi-context learning approach for EEG epileptic seizure detection. BMC Syst Biol 12(6):107. https:\/\/doi.org\/10.1186\/s12918-018-0626-2","DOI":"10.1186\/s12918-018-0626-2"},{"key":"3388_CR26","doi-asserted-by":"publisher","unstructured":"Wang X, Ristaniemi T, Cong F (2021) One and two dimensional convolutional neural networks for seizure detection using EEG signals. In: 2020 28th European signal processing conference (EUSIPCO), pp 1387\u20131391. https:\/\/doi.org\/10.23919\/Eusipco47968.2020.9287640","DOI":"10.23919\/Eusipco47968.2020.9287640"},{"issue":"2","key":"3388_CR27","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1109\/TBME.2021.3104969","volume":"69","author":"Z Guo","year":"2022","unstructured":"Guo Z, McClelland VM, Simeone O, Mills KR, Cvetkovic Z (2022) Multiscale wavelet transfer entropy with application to corticomuscular coupling analysis. IEEE Trans Biomed Eng 69(2):771\u2013782. https:\/\/doi.org\/10.1109\/TBME.2021.3104969","journal-title":"IEEE Trans Biomed Eng"},{"key":"3388_CR28","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"3388_CR29","doi-asserted-by":"publisher","first-page":"7269","DOI":"10.3390\/s22197269","volume":"22","author":"T Najafi","year":"2022","unstructured":"Najafi T, Jaafar R, Remli R, Zaidi WAW (2022) A classification model of EEG signals based on RNN-LSTM for diagnosing focal and generalized epilepsy. Sensors 22:7269. https:\/\/doi.org\/10.3390\/s22197269","journal-title":"Sensors"},{"key":"3388_CR30","doi-asserted-by":"publisher","first-page":"7386","DOI":"10.1007\/s00330-020-07575-1","volume":"31","author":"J Zhang","year":"2021","unstructured":"Zhang J et al (2021) Identifying Parkinson\u2019s disease with mild cognitive impairment by using combined MR imaging and electroencephalogram. Eur Radiol 31:7386\u20137394. https:\/\/doi.org\/10.1007\/s00330-020-07575-1","journal-title":"Eur Radiol"},{"key":"3388_CR31","doi-asserted-by":"publisher","first-page":"71840","DOI":"10.1109\/ACCESS.2023.3294618","volume":"11","author":"A Miltiadous","year":"2023","unstructured":"Miltiadous A, Gionanidis E, Tzimourta KD, Giannakeas N, Tzallas AT (2023) DICE-Net: a novel convolution-transformer architecture for Alzheimer detection in EEG signals. IEEE Access 11:71840\u201371858. https:\/\/doi.org\/10.1109\/ACCESS.2023.3294618","journal-title":"IEEE Access"},{"key":"3388_CR32","doi-asserted-by":"publisher","unstructured":"Rockhill AP, Jackson N, George J, Aron A, Swann NC (2021) UC San Diego Resting State EEG data from patients with Parkinson\u2019s disease. OpenNeuro Dataset, https:\/\/doi.org\/10.18112\/openneuro.ds002778.v1.0.4.","DOI":"10.18112\/openneuro.ds002778.v1.0.4."},{"key":"3388_CR33","doi-asserted-by":"publisher","unstructured":"Shi X, Wang T, Wang L, Liu H, Yan N (2019) Hybrid convolutional recurrent neural networks outperform CNN and RNN in task-state EEG detection for Parkinson\u2019s disease. In: 2019 Asia-pacific signal and information processing association annual summit and conference (APSIPA ASC), pp 939\u2013944. https:\/\/doi.org\/10.1109\/APSIPAASC47483.2019.9023190","DOI":"10.1109\/APSIPAASC47483.2019.9023190"},{"issue":"1","key":"3388_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.baga.2014.11.001","volume":"5","author":"A Nambu","year":"2015","unstructured":"Nambu A, Tachibana Y, Chiken S (2015) Cause of parkinsonian symptoms: firing rate, firing pattern or dynamic activity changes? Basal Ganglia 5(1):1\u20136. https:\/\/doi.org\/10.1016\/j.baga.2014.11.001","journal-title":"Basal Ganglia"},{"key":"3388_CR35","doi-asserted-by":"publisher","first-page":"71840","DOI":"10.1109\/ACCESS.2023.3294618","volume":"11","author":"A Miltiadous","year":"2023","unstructured":"Miltiadous A, Gionanidis E, Tzimourta KD, Giannakeas N, Tzallas AT (2023) DICE-Net: a novel convolution-transformer architecture for Alzheimer detection in EEG signals. IEEE Access 11:71840\u201371858. https:\/\/doi.org\/10.1109\/ACCESS.2023.3294618","journal-title":"IEEE Access"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-025-03388-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-025-03388-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-025-03388-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T07:30:58Z","timestamp":1763623858000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-025-03388-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,9]]},"references-count":35,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["3388"],"URL":"https:\/\/doi.org\/10.1007\/s11517-025-03388-w","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"type":"print","value":"0140-0118"},{"type":"electronic","value":"1741-0444"}],"subject":[],"published":{"date-parts":[[2025,7,9]]},"assertion":[{"value":"25 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}