{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T23:38:10Z","timestamp":1792453090419,"version":"4.5.0"},"reference-count":76,"publisher":"International Research and Cooperation Association for Bio & Socio-Sciences Advancement (IRCA-BSSA)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BST"],"published-print":{"date-parts":[[2025,6,30]]},"DOI":"10.5582\/bst.2025.01109","type":"journal-article","created":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T18:12:16Z","timestamp":1744827136000},"page":"243-251","source":"Crossref","is-referenced-by-count":32,"title":["Integrative neurorehabilitation using brain-computer interface: From motor function to mental health after stroke"],"prefix":"10.5582","volume":"19","author":[{"given":"Ya-nan","family":"Ma","sequence":"first","affiliation":[{"name":"Department of Neurosurgery, Haikou Affiliated Hospital of Central South University Xiangya School of Medicine, Haikou, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenji","family":"Karako","sequence":"additional","affiliation":[{"name":"Department of Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peipei","family":"Song","sequence":"additional","affiliation":[{"name":"Center for Clinical Sciences, Japan institute for Health Security, Tokyo, Japan. 4 National College of Nursing Japan, Tokyo, Japan."},{"name":"National College of Nursing Japan, Tokyo, Japan."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiqi","family":"Hu","sequence":"additional","affiliation":[{"name":"Integrated Neuroscience Center, Geriatric Hospital of Hainan, Haikou, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Xia","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Haikou Affiliated Hospital of Central South University Xiangya School of Medicine, Haikou, China."},{"name":"Integrated Neuroscience Center, Geriatric Hospital of Hainan, Haikou, China."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"3542","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"1. Diseases GBD, Injuries C. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990-2021: A systematic analysis for the global burden of disease study 2021. Lancet. 2024; 403:2133-2161.","DOI":"10.1016\/S0140-6736(24)00757-8"},{"key":"2","doi-asserted-by":"crossref","unstructured":"2. Hachinski V, Donnan GA, Gorelick PB, <i>et al<\/i>. Stroke: Working toward a prioritized world agenda. Stroke. 2010; 41:1084-1099.","DOI":"10.1161\/STROKEAHA.110.586156"},{"key":"3","doi-asserted-by":"crossref","unstructured":"3. Li J, You SJ, Xu YN, Yuan W, Shen Y, Huang JY, Xiong KP, Liu CF. Cognitive impairment and sleep disturbances after minor ischemic stroke. Sleep Breath. 2019; 23:455-462.","DOI":"10.1007\/s11325-018-1709-4"},{"key":"4","doi-asserted-by":"crossref","unstructured":"4. Xiao W, Liu Y, Huang J, Huang LA, Bian Y, Zou G. Analysis of factors associated with depressive symptoms in stroke patients based on a national cross-sectional study. Sci Rep. 2024; 14:9268.","DOI":"10.1038\/s41598-024-59837-3"},{"key":"5","unstructured":"5. Taub E, Uswatte G, Pidikiti R. Constraint-induced movement therapy: A new family of techniques with broad application to physical rehabilitation--A clinical review. J Rehabil Res Dev. 1999; 36:237-251."},{"key":"6","doi-asserted-by":"crossref","unstructured":"6. Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM. Brain-computer interfaces for communication and control. Clin Neurophysiol. 2002; 113:767-791.","DOI":"10.1016\/S1388-2457(02)00057-3"},{"key":"7","doi-asserted-by":"crossref","unstructured":"7. Cramer SC. Treatments to promote neural repair after stroke. J Stroke. 2018; 20:57-70.","DOI":"10.5853\/jos.2017.02796"},{"key":"8","doi-asserted-by":"crossref","unstructured":"8. Ang KK, Guan C. Brain-computer interface in stroke rehabilitation. J Comput Sci Eng. 2013; 7:139-146.","DOI":"10.5626\/JCSE.2013.7.2.139"},{"key":"9","doi-asserted-by":"crossref","unstructured":"9. Ono T, Tomita Y, Inose M, Ota T, Kimura A, Liu M, Ushiba J. Multimodal sensory feedback associated with motor attempts alters BOLD responses to paralyzed hand movement in chronic stroke patients. Brain Topogr. 2015; 28:340-351.","DOI":"10.1007\/s10548-014-0382-6"},{"key":"10","doi-asserted-by":"crossref","unstructured":"10. Young BM, Stamm JM, Song J, Remsik AB, Nair VA, Tyler ME, Edwards DF, Caldera K, Sattin JA, Williams JC, Prabhakaran V. Brain-computer interface training after stroke affects patterns of brain-behavior relationships in corticospinal motor fibers. Front Hum Neurosci. 2016; 10:457.","DOI":"10.3389\/fnhum.2016.00457"},{"key":"11","doi-asserted-by":"crossref","unstructured":"11. Sulzer J, Papageorgiou TD, Goebel R, Hendler T. Neurofeedback: New territories and neurocognitive mechanisms of endogenous neuromodulation. Philos Trans R Soc Lond B Biol Sci. 2024; 379:20230081.","DOI":"10.1098\/rstb.2023.0081"},{"key":"12","doi-asserted-by":"crossref","unstructured":"12. Meng F, Tong K-y, Chan S-t, Wong W-w, Lui K-h, Tang K-w, Gao X, Gao S. BCI-FES training system design and implementation for rehabilitation of stroke patients. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) (IEEE, 2008; pp. 4103-4106.","DOI":"10.1109\/IJCNN.2008.4634388"},{"key":"13","doi-asserted-by":"crossref","unstructured":"13. Remsik AB, Dodd K, Williams L, Jr., <i>et al<\/i>. Behavioral outcomes following brain-computer interface intervention for upper extremity rehabilitation in stroke: A randomized controlled trial. Front Neurosci. 2018; 12:752.","DOI":"10.3389\/fnins.2018.00752"},{"key":"14","doi-asserted-by":"crossref","unstructured":"14. Nojima I, Sugata H, Takeuchi H, Mima T. Brain-computer interface training based on brain activity can induce motor recovery in patients with stroke: A meta-analysis. Neurorehabil Neural Repair. 2022; 36:83-96.","DOI":"10.1177\/15459683211062895"},{"key":"15","doi-asserted-by":"crossref","unstructured":"15. Hong X, Lu ZK, Teh I, Nasrallah FA, Teo WP, Ang KK, Phua KS, Guan C, Chew E, Chuang KH. Brain plasticity following MI-BCI training combined with tDCS in a randomized trial in chronic subcortical stroke subjects: A preliminary study. Sci Rep. 2017; 7:9222.","DOI":"10.1038\/s41598-017-08928-5"},{"key":"16","doi-asserted-by":"crossref","unstructured":"16. Varkuti B, Guan C, Pan Y, Phua KS, Ang KK, Kuah CW, Chua K, Ang BT, Birbaumer N, Sitaram R. Resting state changes in functional connectivity correlate with movement recovery for BCI and robot-assisted upper-extremity training after stroke. Neurorehabil Neural Repair. 2013; 27:53-62.","DOI":"10.1177\/1545968312445910"},{"key":"17","doi-asserted-by":"crossref","unstructured":"17. Krueger J, Krauth R, Reichert C, <i>et al<\/i>. Hebbian plasticity induced by temporally coincident BCI enhances post-stroke motor recovery. Sci Rep. 2024; 14:18700.","DOI":"10.1038\/s41598-024-69037-8"},{"key":"18","doi-asserted-by":"crossref","unstructured":"18. Kukkar KK, Rao N, Huynh D, Shah S, Contreras-Vidal JL, Parikh PJ. Context-dependent reduction in corticomuscular coupling for balance control in chronic stroke survivors. Exp Brain Res. 2024; 242:2093-2112.","DOI":"10.1007\/s00221-024-06884-x"},{"key":"19","doi-asserted-by":"crossref","unstructured":"19. Dobkin BH. Brain-computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation. J Physiol. 2007; 579:637-642.","DOI":"10.1113\/jphysiol.2006.123067"},{"key":"20","doi-asserted-by":"crossref","unstructured":"20. Zhao CG, Ju F, Sun W, Jiang S, Xi X, Wang H, Sun XL, Li M, Xie J, Zhang K, Xu GH, Zhang SC, Mou X, Yuan H. Effects of training with a brain-computer interface-controlled robot on rehabilitation outcome in patients with subacute stroke: A randomized controlled trial. Neurol Ther. 2022; 11:679-695.","DOI":"10.1007\/s40120-022-00333-z"},{"key":"21","doi-asserted-by":"crossref","unstructured":"21. Yoo IG. Electroencephalogram-based neurofeedback training in persons with stroke: A scoping review in occupational therapy. NeuroRehabilitation. 2021; 48:9-18.","DOI":"10.3233\/NRE-201579"},{"key":"22","doi-asserted-by":"crossref","unstructured":"22. Vourvopoulos A, Bermudez IBS. Motor priming in virtual reality can augment motor-imagery training efficacy in restorative brain-computer interaction: A within-subject analysis. J Neuroeng Rehabil. 2016; 13:69.","DOI":"10.1186\/s12984-016-0173-2"},{"key":"23","doi-asserted-by":"crossref","unstructured":"23. Langhorne P, Coupar F, Pollock A. Motor recovery after stroke: A systematic review. Lancet Neurol. 2009; 8:741-754.","DOI":"10.1016\/S1474-4422(09)70150-4"},{"key":"24","doi-asserted-by":"crossref","unstructured":"24. Buch E, Weber C, Cohen LG, Braun C, Dimyan MA, Ard T, Mellinger J, Caria A, Soekadar S, Fourkas A, Birbaumer N. Think to move: A neuromagnetic brain-computer interface (BCI) system for chronic stroke. Stroke. 2008; 39:910-917.","DOI":"10.1161\/STROKEAHA.107.505313"},{"key":"25","doi-asserted-by":"crossref","unstructured":"25. Ang KK, Guan C, Chua KS, Ang BT, Kuah C, Wang C, Phua KS, Chin ZY, Zhang H. Clinical study of neurorehabilitation in stroke using EEG-based motor imagery brain-computer interface with robotic feedback. Annu Int Conf IEEE Eng Med Biol Soc. 2010; 2010:5549- 5552.","DOI":"10.1109\/IEMBS.2010.5626782"},{"key":"26","doi-asserted-by":"crossref","unstructured":"26. Zhang M, Zhu F, Jia F, Wu Y, Wang B, Gao L, Chu F, Tang W. Efficacy of brain-computer interfaces on upper extremity motor function rehabilitation after stroke: A systematic review and meta-analysis. NeuroRehabilitation. 2024; 54:199-212.","DOI":"10.3233\/NRE-230215"},{"key":"27","doi-asserted-by":"crossref","unstructured":"27. Chung E, Lee BH, Hwang S. Therapeutic effects of brain-computer interface-controlled functional electrical stimulation training on balance and gait performance for stroke: A pilot randomized controlled trial. Medicine (Baltimore). 2020; 99:e22612.","DOI":"10.1097\/MD.0000000000022612"},{"key":"28","doi-asserted-by":"crossref","unstructured":"28. Cho W, Sabathiel N, Ortner R, Lechner A, Irimia DC, Allison BZ, Edlinger G, Guger C. Paired associative stimulation using brain-computer interfaces for stroke rehabilitation: A pilot study. Eur J Transl Myol. 2016; 26:6132.","DOI":"10.4081\/ejtm.2016.6132"},{"key":"29","doi-asserted-by":"crossref","unstructured":"29. Jang YY, Kim TH, Lee BH. Effects of brain-computer interface-controlled functional electrical stimulation training on shoulder subluxation for patients with stroke: A randomized controlled trial. Occup Ther Int. 2016; 23:175-185.","DOI":"10.1002\/oti.1422"},{"key":"30","doi-asserted-by":"crossref","unstructured":"30. Mehrholz J, Pohl M, Platz T, Kugler J, Elsner B. Electromechanical and robot-assisted arm training for improving activities of daily living, arm function, and arm muscle strength after stroke. Cochrane Database Syst Rev. 2018; 9:CD006876.","DOI":"10.1002\/14651858.CD006876.pub5"},{"key":"31","doi-asserted-by":"crossref","unstructured":"31. Ramos-Murguialday A, Broetz D, Rea M, <i>et al<\/i>. Brain-machine interface in chronic stroke rehabilitation: A controlled study. Ann Neurol. 2013; 74:100-108.","DOI":"10.1002\/ana.23879"},{"key":"32","doi-asserted-by":"crossref","unstructured":"32. Baniqued PDE, Stanyer EC, Awais M, Alazmani A, Jackson AE, Mon-Williams MA, Mushtaq F, Holt RJ. Brain-computer interface robotics for hand rehabilitation after stroke: A systematic review. J Neuroeng Rehabil. 2021; 18:15.","DOI":"10.1186\/s12984-021-00820-8"},{"key":"33","doi-asserted-by":"crossref","unstructured":"33. Pichiorri F, Morone G, Petti M, Toppi J, Pisotta I, Molinari M, Paolucci S, Inghilleri M, Astolfi L, Cincotti F, Mattia D. Brain-computer interface boosts motor imagery practice during stroke recovery. Ann Neurol. 2015; 77:851-865.","DOI":"10.1002\/ana.24390"},{"key":"34","doi-asserted-by":"crossref","unstructured":"34. Khatkova SE, Kostenko EV, Akulov MA, Diagileva VP, Nikolaev EA, Orlova AS. Modern aspects of the pathophysiology of walking disorders and their rehabilitation in post-stroke patients. Zh Nevrol Psikhiatr Im S S Korsakova. 2019; 119:43-50. (in Russian)","DOI":"10.17116\/jnevro201911912243"},{"key":"35","doi-asserted-by":"crossref","unstructured":"35. Tang N, Guan C, Ang K, Phua K, Chew E. Motor imagery-assisted brain-computer interface for gait retraining in neurorehabilitation in chronic stroke. Ann Phys Rehabil Med. 2018; 61:e188.","DOI":"10.1016\/j.rehab.2018.05.431"},{"key":"36","doi-asserted-by":"crossref","unstructured":"36. Kim J, Kim DY, Chun MH, Kim SW, Jeon HR, Hwang CH, Choi JK, Bae S. Effects of robot-(Morning Walk((R))) assisted gait training for patients after stroke: A randomized controlled trial. Clin Rehabil. 2019; 33:516-523.","DOI":"10.1177\/0269215518806563"},{"key":"37","doi-asserted-by":"crossref","unstructured":"37. Kwak Y, Song WJ, Kim SE. FGANet: fNIRS-guided attention network for hybrid EEG-fNIRS brain-computer interfaces. IEEE Trans Neural Syst Rehabil Eng. 2022; 30:329-339.","DOI":"10.1109\/TNSRE.2022.3149899"},{"key":"38","doi-asserted-by":"crossref","unstructured":"38. Zhang R, Wang C, He S, Zhao C, Zhang K, Wang X, Li Y. An adaptive brain-computer interface to enhance motor recovery after stroke. IEEE Trans Neural Syst Rehabil Eng. 2023; 31:2268-2278.","DOI":"10.1109\/TNSRE.2023.3272372"},{"key":"39","doi-asserted-by":"crossref","unstructured":"39. Cervera MA, Soekadar SR, Ushiba J, Millan JDR, Liu M, Birbaumer N, Garipelli G. Brain-computer interfaces for post-stroke motor rehabilitation: A meta-analysis. Ann Clin Transl Neurol. 2018; 5:651-663.","DOI":"10.1002\/acn3.544"},{"key":"40","doi-asserted-by":"crossref","unstructured":"40. Wang A, Tian X, Jiang D, <i>et al<\/i>. Rehabilitation with brain-computer interface and upper limb motor function in ischemic stroke: A randomized controlled trial. Med. 2024; 5:559-569 e554.","DOI":"10.1016\/j.medj.2024.02.014"},{"key":"41","doi-asserted-by":"crossref","unstructured":"41. Ali JI, Viczko J, Smart CM. Efficacy of neurofeedback interventions for cognitive rehabilitation following brain injury: Systematic review and recommendations for future research. J Int Neuropsychol Soc. 2020; 26:31-46.","DOI":"10.1017\/S1355617719001061"},{"key":"42","doi-asserted-by":"crossref","unstructured":"42. Cannon KB, Sherlin L, Lyle RR. Neurofeedback efficacy in the treatment of a 43-year-old female stroke victim: A case study. J Neurother. 2010; 14:107-121.","DOI":"10.1080\/10874201003772155"},{"key":"43","doi-asserted-by":"crossref","unstructured":"43. Mroczkowska D, Bia\u0142kowska J, Rakowska A. Neurofeedback as supportive therapy after stroke. Case report. Postep Psychiatr Neurol. 2014; 23:190-201.","DOI":"10.1016\/j.pin.2014.09.002"},{"key":"44","doi-asserted-by":"crossref","unstructured":"44. Park SH, Sohn MK, Jee S, Yang SS. The characteristics of cognitive impairment and their effects on functional outcome after inpatient rehabilitation in subacute stroke patients. Ann Rehabil Med. 2017; 41:734-742.","DOI":"10.5535\/arm.2017.41.5.734"},{"key":"45","doi-asserted-by":"crossref","unstructured":"45. Klimesch W. alpha-band oscillations, attention, and controlled access to stored information. Trends Cogn Sci. 2012; 16:606-617.","DOI":"10.1016\/j.tics.2012.10.007"},{"key":"46","doi-asserted-by":"crossref","unstructured":"46. Bazanova OM, Vernon D. Interpreting EEG alpha activity. Neurosci Biobehav Rev. 2014; 44:94-110.","DOI":"10.1016\/j.neubiorev.2013.05.007"},{"key":"47","doi-asserted-by":"crossref","unstructured":"47. Mane R, Chouhan T, Guan C. BCI for stroke rehabilitation: Motor and beyond. J Neural Eng. 2020; 17:041001.","DOI":"10.1088\/1741-2552\/aba162"},{"key":"48","doi-asserted-by":"crossref","unstructured":"48. Kober SE, Schweiger D, Witte M, Reichert JL, Grieshofer P, Neuper C, Wood G. Specific effects of EEG based neurofeedback training on memory functions in post-stroke victims. J Neuroeng Rehabil. 2015; 12:107.","DOI":"10.1186\/s12984-015-0105-6"},{"key":"49","doi-asserted-by":"crossref","unstructured":"49. Van Doren J, Arns M, Heinrich H, Vollebregt MA, Strehl U, S KL. Sustained effects of neurofeedback in ADHD: A systematic review and meta-analysis. Eur Child Adolesc Psychiatry. 2019; 28:293-305.","DOI":"10.1007\/s00787-018-1121-4"},{"key":"50","doi-asserted-by":"crossref","unstructured":"50. Pengcheng C, Nuo G. Research of VR-BCI and its application in hand soft rehabilitation system. In: 2021 IEEE 7th International Conference on Virtual Reality (ICVR) IEEE, 2021; pp. 254-261.","DOI":"10.1109\/ICVR51878.2021.9483707"},{"key":"51","doi-asserted-by":"crossref","unstructured":"51. Drigas A, Sideraki A. Brain neuroplasticity leveraging virtual reality and brain-computer interface technologies. Sensors (Basel). 2024; 24:5725.","DOI":"10.3390\/s24175725"},{"key":"52","doi-asserted-by":"crossref","unstructured":"52. Silva GA. A New Frontier: The convergence of nanotechnology, brain machine interfaces, and artificial intelligence. Front Neurosci. 2018; 12:843.","DOI":"10.3389\/fnins.2018.00843"},{"key":"53","doi-asserted-by":"crossref","unstructured":"53. Berthier ML. Poststroke aphasia: Epidemiology, pathophysiology and treatment. Drugs Aging. 2005; 22:163-182.","DOI":"10.2165\/00002512-200522020-00006"},{"key":"54","doi-asserted-by":"crossref","unstructured":"54. Dickey L, Kagan A, Lindsay MP, Fang J, Rowland A, Black S. Incidence and profile of inpatient stroke-induced aphasia in Ontario, Canada. Arch Phys Med Rehabil. 2010; 91:196-202.","DOI":"10.1016\/j.apmr.2009.09.020"},{"key":"55","doi-asserted-by":"crossref","unstructured":"55. Mingming Y, Bolun Z, Zhijian L, Yingli W, Lanshu Z. Effectiveness of computer-based training on post-stroke cognitive rehabilitation: A systematic review and meta-analysis. Neuropsychol Rehabil. 2022; 32:481-497.","DOI":"10.1080\/09602011.2020.1831555"},{"key":"56","doi-asserted-by":"crossref","unstructured":"56. Mane R, Wu Z, Wang D. Poststroke motor, cognitive and speech rehabilitation with brain-computer interface: A perspective review. Stroke Vasc Neurol. 2022; 7:541-549.","DOI":"10.1136\/svn-2022-001506"},{"key":"57","doi-asserted-by":"crossref","unstructured":"57. Ardila A. A proposed reinterpretation and reclassification of aphasic syndromes. Aphasiology. 2010; 24:363-394.","DOI":"10.1080\/02687030802553704"},{"key":"58","doi-asserted-by":"crossref","unstructured":"58. Abibullaev B, An J, Moon J-I. Neural network classification of brain hemodynamic responses from four mental tasks. Int J Optomechatronics. 2011; 5:340-359.","DOI":"10.1080\/15599612.2011.633209"},{"key":"59","doi-asserted-by":"crossref","unstructured":"59. Nan W, Dias APB, Rosa AC. Neurofeedback training for cognitive and motor function rehabilitation in chronic stroke: Two case reports. Front Neurol. 2019; 10:800.","DOI":"10.3389\/fneur.2019.00800"},{"key":"60","doi-asserted-by":"crossref","unstructured":"60. Berg A, Paloma\u0308ki H, Lehtihalmes M, Lo\u0308nnqvist J, Kaste M. Poststroke depression: An 18-month follow-up. Stroke. 2003; 34:138-143.","DOI":"10.1161\/01.STR.0000048149.84268.07"},{"key":"61","doi-asserted-by":"crossref","unstructured":"61. Sun N, Li Q-J, Lv D-M, Man J, Liu X-S, Sun M-L. A survey on 465 patients with post-stroke depression in China. Arch Psychiatr Nurs. 2014; 28:368-371.","DOI":"10.1016\/j.apnu.2014.08.007"},{"key":"62","doi-asserted-by":"crossref","unstructured":"62. Chun HY, Whiteley WN, Dennis MS, Mead GE, Carson AJ. Anxiety after stroke: The importance of subtyping. Stroke. 2018; 49:556-564.","DOI":"10.1161\/STROKEAHA.117.020078"},{"key":"63","doi-asserted-by":"crossref","unstructured":"63. Lincoln NB, Brinkmann N, Cunningham S, Dejaeger E, De Weerdt W, Jenni W, Mahdzir A, Putman K, Schupp W, Schuback B, De Wit L. Anxiety and depression after stroke: A 5 year follow-up. Disabil Rehabil. 2013; 35:140-145.","DOI":"10.3109\/09638288.2012.691939"},{"key":"64","doi-asserted-by":"crossref","unstructured":"64. Ojagbemi A, Akinyemi J, Owolabi M, Akinyemi R, Arulogun O, Gebregziabher M, Akpa O, Olaniyan O, Salako B, Ovbiagele B. Predictors and prognoses of new onset post-stroke anxiety at one year in black Africans. J Stroke Cerebrovasc Dis. 2020; 29:105082.","DOI":"10.1016\/j.jstrokecerebrovasdis.2020.105082"},{"key":"65","doi-asserted-by":"crossref","unstructured":"65. Wang J, Zhao D, Lin M, Huang X, Shang X. Post-stroke anxiety analysis <i>via<\/i> machine learning methods. Front Aging Neurosci. 2021; 13:657937.","DOI":"10.3389\/fnagi.2021.657937"},{"key":"66","doi-asserted-by":"crossref","unstructured":"66. Rafsten L, Danielsson A, Sunnerhagen KS. Anxiety after stroke: A systematic review and meta-analysis. J Rehabil Med. 2018; 50:769-778.","DOI":"10.2340\/16501977-2384"},{"key":"67","doi-asserted-by":"crossref","unstructured":"67. Torres PE, Torres EA, Hernandez-Alvarez M, Yoo SG. EEG-based BCI emotion recognition: A survey. Sensors (Basel). 2020; 20:5083.","DOI":"10.3390\/s20185083"},{"key":"68","doi-asserted-by":"crossref","unstructured":"68. Yasemin M, Sarikaya MA, Ince G. Emotional state estimation using sensor fusion of EEG and EDA. Annu Int Conf IEEE Eng Med Biol Soc. 2019; 2019:5609-5612.","DOI":"10.1109\/EMBC.2019.8856895"},{"key":"69","doi-asserted-by":"crossref","unstructured":"69. Ehrlich SK, Agres KR, Guan C, Cheng G. A closed-loop, music-based brain-computer interface for emotion mediation. PLoS One. 2019; 14:e0213516.","DOI":"10.1371\/journal.pone.0213516"},{"key":"70","doi-asserted-by":"crossref","unstructured":"70. Lorenzetti V, Melo B, Basilio R, Suo C, Yucel M, Tierra-Criollo CJ, Moll J. Emotion regulation using virtual environments and real-time fMRI neurofeedback. Front Neurol. 2018; 9:390.","DOI":"10.3389\/fneur.2018.00390"},{"key":"71","doi-asserted-by":"crossref","unstructured":"71. Wang X, Ma Y, Cammon J, Fang F, Gao Y, Zhang Y. Self-supervised EEG emotion recognition models based on CNN. IEEE Trans Neural Syst Rehabil Eng. 2023; 31:1952-1962.","DOI":"10.1109\/TNSRE.2023.3263570"},{"key":"72","doi-asserted-by":"crossref","unstructured":"72. Su Y, Zhang Z, Li X, Zhang B, Ma H. The multiscale 3D convolutional network for emotion recognition based on electroencephalogram. Front Neurosci. 2022; 16:872311.","DOI":"10.3389\/fnins.2022.872311"},{"key":"73","doi-asserted-by":"crossref","unstructured":"73. Vafaei E, Hosseini M. Transformers in EEG Analysis: A review of architectures and applications in motor imagery, seizure, and emotion classification. Sensors (Basel). 2025; 25:1293.","DOI":"10.3390\/s25051293"},{"key":"74","doi-asserted-by":"crossref","unstructured":"74. Micoulaud-Franchi JA, Jeunet C, Pelissolo A, Ros T. EEG neurofeedback for anxiety disorders and post-traumatic stress disorders: A blueprint for a promising brain-based therapy. Curr Psychiatry Rep. 2021; 23:84.","DOI":"10.1007\/s11920-021-01299-9"},{"key":"75","doi-asserted-by":"crossref","unstructured":"75. Parsons TD. Virtual reality for enhanced ecological validity and experimental control in the clinical, affective and social neurosciences. Front Hum Neurosci. 2015; 9:660.","DOI":"10.3389\/fnhum.2015.00660"},{"key":"76","doi-asserted-by":"crossref","unstructured":"76. Edelman BJ, Meng J, Suma D, Zurn C, Nagarajan E, Baxter BS, Cline CC, He B. Noninvasive neuroimaging enhances continuous neural tracking for robotic device control. Sci Robot. 2019; 4:eaaw6844.","DOI":"10.1126\/scirobotics.aaw6844"}],"container-title":["BioScience Trends"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/bst\/19\/3\/19_2025.01109\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T00:31:31Z","timestamp":1751675491000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/bst\/19\/3\/19_2025.01109\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,30]]},"references-count":76,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.5582\/bst.2025.01109","relation":{},"ISSN":["1881-7815","1881-7823"],"issn-type":[{"value":"1881-7815","type":"print"},{"value":"1881-7823","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,30]]},"article-number":"2025.01109"}}