{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T21:25:53Z","timestamp":1761168353389,"version":"build-2065373602"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Shanghai Municipal Science and Technology Plan Project","award":["No.22010502400"],"award-info":[{"award-number":["No.22010502400"]}]},{"name":"Shanghai University of Medicine & Health Sciences Mental Health Research Institute Foundation","award":["YJYPI202402"],"award-info":[{"award-number":["YJYPI202402"]}]},{"name":"Pudong New Area Science and Technology Development Fund Livelihood Research Project for Public Institutions","award":["PKJ2024-Y37"],"award-info":[{"award-number":["PKJ2024-Y37"]}]},{"name":"Shanghai Municipal Education Commission AI Program","award":["SHJWAIJK241203"],"award-info":[{"award-number":["SHJWAIJK241203"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BioMed Eng OnLine"],"DOI":"10.1186\/s12938-025-01456-8","type":"journal-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T11:17:00Z","timestamp":1761045420000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Research on drug addiction detection based on AR-TSNET with bimodal EEG\u2013NIRS"],"prefix":"10.1186","volume":"24","author":[{"given":"Xiaowen","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuelin","family":"Gu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueshan","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaojing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoou","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,21]]},"reference":[{"issue":"10","key":"1456_CR1","doi-asserted-by":"publisher","first-page":"1022","DOI":"10.3390\/brainsci14101022","volume":"14","author":"J Chen","year":"2024","unstructured":"Chen J, Yu K, Bi Y. Strategic integration: a cross-disciplinary review of the fNIRS-EEG dual-modality imaging system for delivering multimodal neuroimaging to applications. Brain Sci. 2024;14(10):1022. https:\/\/doi.org\/10.3390\/brainsci14101022.","journal-title":"Brain Sci"},{"key":"1456_CR2","unstructured":"Qiu L. EFMLNet: fusion model based on end-to-end mutual information learning for hybrid EEG-fNIRS brain\u2013computer interface applications."},{"key":"1456_CR3","doi-asserted-by":"publisher","first-page":"128646","DOI":"10.1016\/j.neucom.2024.128646","volume":"611","author":"X Qi","year":"2025","unstructured":"Qi X, Wen Y, Zhang P. MFGCN: multimodal fusion graph convolutional network for speech emotion recognition. Neurocomputing. 2025;611:128646. https:\/\/doi.org\/10.1016\/j.neucom.2024.128646.","journal-title":"Neurocomputing"},{"issue":"6","key":"1456_CR4","doi-asserted-by":"publisher","first-page":"713","DOI":"10.3390\/brainsci11060713","volume":"11","author":"MS Al-Quraishi","year":"2021","unstructured":"Al-Quraishi MS, Elamvazuthi I, Tang TB. Bimodal data fusion of simultaneous measurements of EEG and fNIRS during lower limb movements. Brain Sci. 2021;11(6):713. https:\/\/doi.org\/10.3390\/brainsci11060713.","journal-title":"Brain Sci"},{"key":"1456_CR5","unstructured":"Liu Y. Mental workload classification with concurrent electroencephalography and functional near-infrared spectroscopy."},{"key":"1456_CR6","doi-asserted-by":"publisher","first-page":"1205931","DOI":"10.3389\/fnins.2023.1205931","volume":"17","author":"L Yi","year":"2023","unstructured":"Yi L, Xie G, Li Z. Automatic depression diagnosis through hybrid EEG and near-infrared spectroscopy features using support vector machine. Front Neurosci. 2023;17:1205931. https:\/\/doi.org\/10.3389\/fnins.2023.1205931.","journal-title":"Front Neurosci"},{"issue":"12","key":"1456_CR7","doi-asserted-by":"publisher","first-page":"8367","DOI":"10.1007\/s00521-019-04294-7","volume":"32","author":"A G\u00fcven","year":"2020","unstructured":"G\u00fcven A, Altinkaynak M, Dolu N. Combining functional near-infrared spectroscopy and EEG measurements for the diagnosis of attention-deficit hyperactivity disorder. Neural Comput Appl. 2020;32(12):8367\u201380. https:\/\/doi.org\/10.1007\/s00521-019-04294-7.","journal-title":"Neural Comput Appl"},{"key":"1456_CR8","doi-asserted-by":"publisher","first-page":"101767","DOI":"10.1016\/j.swevo.2024.101767","volume":"91","author":"R Jain","year":"2024","unstructured":"Jain R, Singh P, Kaur A. An ensemble reinforcement learning-assisted deep learning framework for enhanced lung cancer diagnosis. Swarm Evol Comput. 2024;91:101767. https:\/\/doi.org\/10.1016\/j.swevo.2024.101767.","journal-title":"Swarm Evol Comput"},{"key":"1456_CR9","doi-asserted-by":"publisher","unstructured":"Chu H, Cao Y, Jiang J. Optimized EEG\u2013fNIRS based mental workload detection method for practical applications. (2021\u201307\u201324) [2024\u201312\u201320]. https:\/\/www.researchsquare.com\/article\/rs-683529\/v1. https:\/\/doi.org\/10.21203\/rs.3.rs-683529\/v1","DOI":"10.21203\/rs.3.rs-683529\/v1"},{"key":"1456_CR10","doi-asserted-by":"publisher","first-page":"106968","DOI":"10.1016\/j.compbiomed.2023.106968","volume":"160","author":"J Lin","year":"2023","unstructured":"Lin J, Lu J, Shu Z. An EEG-fNIRS neurovascular coupling analysis method to investigate cognitive-motor interference. Comput Biol Med. 2023;160:106968. https:\/\/doi.org\/10.1016\/j.compbiomed.2023.106968.","journal-title":"Comput Biol Med"},{"key":"1456_CR11","doi-asserted-by":"publisher","first-page":"973959","DOI":"10.3389\/fnhum.2022.973959","volume":"16","author":"L Qiu","year":"2022","unstructured":"Qiu L, Zhong Y, He Z. Improved classification performance of EEG-fNIRS multimodal brain-computer interface based on multi-domain features and multi-level progressive learning. Front Hum Neurosci. 2022;16:973959. https:\/\/doi.org\/10.3389\/fnhum.2022.973959.","journal-title":"Front Hum Neurosci"},{"key":"1456_CR12","doi-asserted-by":"publisher","first-page":"106448","DOI":"10.1016\/j.bspc.2024.106448","volume":"95","author":"M Guo","year":"2024","unstructured":"Guo M, Feng L, Chen X. A novel strategy for differentiating motor imagination brain-computer interface tasks by fusing EEG and functional near-infrared spectroscopy signals. Biomed Signal Process Control. 2024;95:106448. https:\/\/doi.org\/10.1016\/j.bspc.2024.106448.","journal-title":"Biomed Signal Process Control"},{"key":"1456_CR13","doi-asserted-by":"publisher","first-page":"479","DOI":"10.3389\/fnhum.2018.00479","volume":"12","author":"MJ Khan","year":"2018","unstructured":"Khan MJ, Ghafoor U, Hong KS. Early detection of hemodynamic responses using EEG: a hybrid EEG-fNIRS study. Front Hum Neurosci. 2018;12:479. https:\/\/doi.org\/10.3389\/fnhum.2018.00479.","journal-title":"Front Hum Neurosci"},{"issue":"6","key":"1456_CR14","doi-asserted-by":"publisher","first-page":"1246","DOI":"10.1109\/TNSRE.2020.2987888","volume":"28","author":"M Abtahi","year":"2020","unstructured":"Abtahi M, Bahram Borgheai S, Jafari R. Merging fNIRS-EEG brain monitoring and body motion capture to distinguish Parkinson\u2019s disease. IEEE Trans Neural Syst Rehabil Eng. 2020;28(6):1246\u201353. https:\/\/doi.org\/10.1109\/TNSRE.2020.2987888.","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"1456_CR15","doi-asserted-by":"publisher","first-page":"101622","DOI":"10.1016\/j.nicl.2018.101622","volume":"21","author":"R Li","year":"2019","unstructured":"Li R, Nguyen T, Potter T. Dynamic cortical connectivity alterations associated with Alzheimer\u2019s disease: an EEG and fNIRS integration study. NeuroImage: Clin. 2019;21:101622. https:\/\/doi.org\/10.1016\/j.nicl.2018.101622.","journal-title":"NeuroImage: Clin"},{"issue":"4","key":"1456_CR16","doi-asserted-by":"publisher","first-page":"e0152373","DOI":"10.1371\/journal.pone.0152373","volume":"11","author":"K Yamamuro","year":"2016","unstructured":"Yamamuro K, Kimoto S, Iida J. Reduced prefrontal cortex hemodynamic response in adults with methamphetamine induced psychosis: relevance for impulsivity. PLoS ONE. 2016;11(4):e0152373. https:\/\/doi.org\/10.1371\/journal.pone.0152373.","journal-title":"PLoS ONE"},{"issue":"4","key":"1456_CR17","doi-asserted-by":"publisher","first-page":"826","DOI":"10.1038\/s41386-018-0252-0","volume":"44","author":"AS Huhn","year":"2019","unstructured":"Huhn AS, Sweeney MM, Brooner RK. Prefrontal cortex response to drug cues, craving, and current depressive symptoms are associated with treatment outcomes in methadone-maintained patients. Neuropsychopharmacology. 2019;44(4):826\u201333. https:\/\/doi.org\/10.1038\/s41386-018-0252-0.","journal-title":"Neuropsychopharmacology"},{"issue":"1","key":"1456_CR18","doi-asserted-by":"publisher","first-page":"923","DOI":"10.1038\/s41598-017-18471-y","volume":"8","author":"Y Huang","year":"2018","unstructured":"Huang Y, Mohan A, DE Ridder D. The neural correlates of the unified percept of alcohol-related craving: a fMRI and EEG study. Sci Rep. 2018;8(1):923. https:\/\/doi.org\/10.1038\/s41598-017-18471-y.","journal-title":"Sci Rep"},{"key":"1456_CR19","unstructured":"Jakkula V. Tutorial on support vector machine (SVM)."},{"issue":"2","key":"1456_CR20","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/s11749-016-0481-7","volume":"25","author":"G Biau","year":"2016","unstructured":"Biau G, Scornet E. A random forest guided tour. TEST. 2016;25(2):197\u2013227. https:\/\/doi.org\/10.1007\/s11749-016-0481-7.","journal-title":"TEST"},{"key":"1456_CR21","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-9878-1","volume-title":"Robust data mining","author":"P Xanthopoulos","year":"2013","unstructured":"Xanthopoulos P, Pardalos PM, Trafalis TB. Robust data mining. New York: Springer; 2013. https:\/\/doi.org\/10.1007\/978-1-4419-9878-1."},{"key":"1456_CR22","doi-asserted-by":"publisher","unstructured":"Kasruddin Nasir AN, Ahmad MA, Najib MS. In ECCE2019: proceedings of the 5th international conference on electrical, control & computer engineering, Kuantan, Pahang, Malaysia, 29th July 2019. Singapore: Springer Singapore, 2020[2025-01-14]. https:\/\/doi.org\/10.1007\/978-981-15-2317-5. https:\/\/doi.org\/10.1007\/978-981-15-2317-5.","DOI":"10.1007\/978-981-15-2317-5"},{"issue":"1","key":"1456_CR23","doi-asserted-by":"publisher","first-page":"15","DOI":"10.3934\/Neuroscience.2025002","volume":"12","author":"M Zhang","year":"2025","unstructured":"Zhang M, Ren J, Li N. Hypnosis efficacy on nicotine addiction: an analysis of EEG microstates and brain oscillation entropy. AIMS Neurosci. 2025;12(1):15\u201331. https:\/\/doi.org\/10.3934\/Neuroscience.2025002.","journal-title":"AIMS Neurosci"},{"issue":"1","key":"1456_CR24","doi-asserted-by":"publisher","DOI":"10.1186\/s12938-022-00980-1","volume":"21","author":"H Chu","year":"2022","unstructured":"Chu H, Cao Y, Jiang J. Optimized electroencephalogram and functional near-infrared spectroscopy-based mental workload detection method for practical applications. BioMed Eng OnLine. 2022;21(1):9. https:\/\/doi.org\/10.1186\/s12938-022-00980-1.","journal-title":"BioMed Eng OnLine"},{"key":"1456_CR25","doi-asserted-by":"publisher","first-page":"389","DOI":"10.3389\/fnhum.2017.00389","volume":"11","author":"Y Liu","year":"2017","unstructured":"Liu Y, Ayaz H, Shewokis PA. Multisubject, \u201cLearning\u201d for mental workload classification using concurrent EEG, fNIRS, and physiological measures. Front Hum Neurosci. 2017;11:389. https:\/\/doi.org\/10.3389\/fnhum.2017.00389.","journal-title":"Front Hum Neurosci"},{"key":"1456_CR26","doi-asserted-by":"publisher","first-page":"108618","DOI":"10.1016\/j.jneumeth.2020.108618","volume":"336","author":"PA Cicalese","year":"2020","unstructured":"Cicalese PA, Li R, Ahmadi MB. An EEG-fNIRS hybridization technique in the four-class classification of Alzheimer\u2019s disease. J Neurosci Methods. 2020;336:108618. https:\/\/doi.org\/10.1016\/j.jneumeth.2020.108618.","journal-title":"J Neurosci Methods"},{"key":"1456_CR27","doi-asserted-by":"publisher","first-page":"599802","DOI":"10.3389\/fnhum.2020.599802","volume":"14","author":"MU Khan","year":"2020","unstructured":"Khan MU, Hasan MAH. Hybrid EEG-fNIRS BCI fusion using multi-resolution singular value decomposition (MSVD). Front Hum Neurosci. 2020;14:599802. https:\/\/doi.org\/10.3389\/fnhum.2020.599802.","journal-title":"Front Hum Neurosci"},{"key":"1456_CR28","doi-asserted-by":"publisher","first-page":"357","DOI":"10.3389\/fnhum.2019.00357","volume":"13","author":"S Ge","year":"2019","unstructured":"Ge S, Wang P, Liu H. Neural activity and decoding of action observation using combined EEG and fNIRS measurement. Front Hum Neurosci. 2019;13:357. https:\/\/doi.org\/10.3389\/fnhum.2019.00357.","journal-title":"Front Hum Neurosci"},{"key":"1456_CR29","doi-asserted-by":"crossref","unstructured":"Zhang X, Gu X, Li X. A study on the assessment of drug addiction level of bimodal Eeg\u2013Nirs based on Tsnet. 2024.","DOI":"10.1145\/3674658.3674698"},{"issue":"1","key":"1456_CR30","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1186\/s13634-015-0251-9","volume":"2015","author":"T Alotaiby","year":"2015","unstructured":"Alotaiby T, El-Samie FEA, Alshebeili SA. A review of channel selection algorithms for EEG signal processing. EURASIP J Adv Signal Process. 2015;2015(1):66. https:\/\/doi.org\/10.1186\/s13634-015-0251-9.","journal-title":"EURASIP J Adv Signal Process"},{"key":"1456_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2019\/8586153","volume":"2019","author":"C Minnerly","year":"2019","unstructured":"Minnerly C, Bressler SL, Shokry IM. Estimating mental health conditions of patients with opioid use disorder. J Addict. 2019;2019:1\u201315. https:\/\/doi.org\/10.1155\/2019\/8586153.","journal-title":"J Addict"},{"key":"1456_CR32","doi-asserted-by":"crossref","unstructured":"Kim J. Frontal EEG response to alcohol craving elicited by individually tailored video cues. 2023.","DOI":"10.2139\/ssrn.4330537"},{"key":"1456_CR33","unstructured":"Hartmann KG, Schirrmeister RT, Ball T. EEG-GAN: generative adversarial networks for electroencephalograhic (EEG) brain signals. arXiv, 2018[2024-02-03]. http:\/\/arxiv.org\/abs\/1806.01875."},{"issue":"5","key":"1456_CR34","doi-asserted-by":"publisher","first-page":"6926","DOI":"10.3934\/mbe.2021344","volume":"18","author":"X Gu","year":"2021","unstructured":"Gu X, Yang B, Gao S. Application of bi-modal signal in the classification and recognition of drug addiction degree based on machine learning. Math Biosci Eng. 2021;18(5):6926\u201340. https:\/\/doi.org\/10.3934\/mbe.2021344.","journal-title":"Math Biosci Eng"},{"issue":"1","key":"1456_CR35","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1186\/s12888-020-02743-5","volume":"20","author":"T Chen","year":"2020","unstructured":"Chen T, Su H, Zhong N. Disrupted brain network dynamics and cognitive functions in methamphetamine use disorder: insights from EEG microstates. BMC Psychiatry. 2020;20(1):334. https:\/\/doi.org\/10.1186\/s12888-020-02743-5.","journal-title":"BMC Psychiatry"},{"issue":"1","key":"1456_CR36","doi-asserted-by":"publisher","first-page":"e14743","DOI":"10.1111\/psyp.14743","volume":"62","author":"NSJ Jacobsen","year":"2025","unstructured":"Jacobsen NSJ, Kristanto D, Welp S. Preprocessing choices for P3 analyses with mobile EEG: a systematic literature review and interactive exploration. Psychophysiology. 2025;62(1):e14743. https:\/\/doi.org\/10.1111\/psyp.14743.","journal-title":"Psychophysiology"},{"issue":"5","key":"1456_CR37","doi-asserted-by":"publisher","first-page":"N91","DOI":"10.1088\/0031-9155\/51\/5\/N02","volume":"51","author":"L Kocsis","year":"2006","unstructured":"Kocsis L, Herman P, Eke A. The modified Beer-Lambert law revisited. Phys Med Biol. 2006;51(5):N91. https:\/\/doi.org\/10.1088\/0031-9155\/51\/5\/N02.","journal-title":"Phys Med Biol"},{"issue":"5","key":"1456_CR38","doi-asserted-by":"publisher","first-page":"606","DOI":"10.3390\/brainsci11050606","volume":"11","author":"PW Dans","year":"2021","unstructured":"Dans PW, Foglia SD, Nelson AJ. Data processing in functional near-infrared spectroscopy (fNIRS) motor control research. Brain Sci. 2021;11(5):606. https:\/\/doi.org\/10.3390\/brainsci11050606.","journal-title":"Brain Sci"},{"issue":"11","key":"1456_CR39","doi-asserted-by":"publisher","first-page":"e3306","DOI":"10.1002\/cem.3306","volume":"34","author":"Y Jiao","year":"2020","unstructured":"Jiao Y, Li Z, Chen X. Preprocessing methods for near-infrared spectrum calibration. J Chemometrics. 2020;34(11):e3306. https:\/\/doi.org\/10.1002\/cem.3306.","journal-title":"J Chemometrics"},{"issue":"5","key":"1456_CR40","doi-asserted-by":"publisher","first-page":"056013","DOI":"10.1088\/1741-2552\/aace8c","volume":"15","author":"VJ Lawhern","year":"2018","unstructured":"Lawhern VJ, Solon AJ, Waytowich NR. EEGnet: a compact convolutional neural network for EEG-based brain\u2013computer interfaces. J Neural Eng. 2018;15(5):056013. https:\/\/doi.org\/10.1088\/1741-2552\/aace8c.","journal-title":"J Neural Eng"}],"container-title":["BioMedical Engineering OnLine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12938-025-01456-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12938-025-01456-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12938-025-01456-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T00:02:54Z","timestamp":1761091374000},"score":1,"resource":{"primary":{"URL":"https:\/\/biomedical-engineering-online.biomedcentral.com\/articles\/10.1186\/s12938-025-01456-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"references-count":40,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1456"],"URL":"https:\/\/doi.org\/10.1186\/s12938-025-01456-8","relation":{},"ISSN":["1475-925X"],"issn-type":[{"type":"electronic","value":"1475-925X"}],"subject":[],"published":{"date-parts":[[2025,10,21]]},"assertion":[{"value":"15 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 October 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":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. We have obtained the ethical approval for the data of individuals with drug addiction from the Ethics Committee of Shanghai University of Medicine and Health Sciences (Approval No. 2021-SWYYKJ-04-230204197506121418).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"123"}}