{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T16:22:16Z","timestamp":1782577336875,"version":"3.54.5"},"reference-count":69,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2018,10,11]],"date-time":"2018-10-11T00:00:00Z","timestamp":1539216000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61727807"],"award-info":[{"award-number":["61727807"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61473043"],"award-info":[{"award-number":["61473043"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81671776"],"award-info":[{"award-number":["81671776"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Municipal Science &amp; Technology Commission","award":["Z161100002616020"],"award-info":[{"award-number":["Z161100002616020"]}]},{"DOI":"10.13039\/501100005090","name":"Beijing Nova Program","doi-asserted-by":"publisher","award":["Z171100001117057"],"award-info":[{"award-number":["Z171100001117057"]}],"id":[{"id":"10.13039\/501100005090","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Electroencephalogram (EEG) neurofeedback improves cognitive capacity and behaviors by regulating brain activity, which can lead to cognitive enhancement in healthy people and better rehabilitation in patients. The increased use of EEG neurofeedback highlights the urgent need to reduce the discomfort and preparation time and increase the stability and simplicity of the system\u2019s operation. Based on brain-computer interface technology and a multithreading design, we describe a neurofeedback system with an integrated design that incorporates wearable, multichannel, dry electrode EEG acquisition equipment and cognitive function assessment. Then, we evaluated the effectiveness of the system in a single-blind control experiment in healthy people, who increased the alpha frequency band power in a neurofeedback protocol. We found that upregulation of the alpha power density improved working memory following short-term training (only five training sessions in a week), while the attention network regulation may be related to other frequency band activities, such as theta and beta. Our integrated system will be an effective neurofeedback training and cognitive function assessment system for personal and clinical use.<\/jats:p>","DOI":"10.3390\/s18103396","type":"journal-article","created":{"date-parts":[[2018,10,12]],"date-time":"2018-10-12T02:58:04Z","timestamp":1539313084000},"page":"3396","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Effects of an Integrated Neurofeedback System with Dry Electrodes: EEG Acquisition and Cognition Assessment"],"prefix":"10.3390","volume":"18","author":[{"given":"Guangying","family":"Pei","sequence":"first","affiliation":[{"name":"Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, The Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinglong","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, The Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Duanduan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Life Science, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoxin","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Life Science, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuozhen","family":"Liu","sequence":"additional","affiliation":[{"name":"Valley Christian High School, San Jose, CA 55101, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingxuan","family":"Hong","sequence":"additional","affiliation":[{"name":"School of Life Science and Medicine, Dalian University of Technology, Liaoning 124221, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianyi","family":"Yan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Convergence Medical Engineering System and Healthcare Technology, The Ministry of Industry and Information Technology, Beijing Institute of Technology, Beijing 100081, China"},{"name":"School of Life Science, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,11]]},"reference":[{"key":"ref_1","first-page":"51","article-title":"EEG-Neurofeedback as a tool to modulate cognition and behavior: A review tutorial","volume":"11","author":"Enriquezgeppert","year":"2017","journal-title":"Front. Hum. Neurosci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.eij.2015.06.002","article-title":"Brain computer interfacing: Applications and challenges","volume":"16","author":"Abdulkader","year":"2015","journal-title":"Egypt. Informat. J."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Popescu, F., Fazli, S., Badower, Y., Blankertz, B., and Muller, K.R. (2007). Single trial classification of motor imagination using 6 dry EEG electrodes. PLoS ONE, 2.","DOI":"10.1371\/journal.pone.0000637"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1109\/TBME.2010.2102353","article-title":"Novel dry polymer foam electrodes for long-term EEG measurement","volume":"58","author":"Lin","year":"2011","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1016\/S1388-2457(00)00533-2","article-title":"Scalp electrode impedance, infection risk, and EEG data quality","volume":"112","author":"Ferree","year":"2001","journal-title":"Clin. Neurophysiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1016\/j.clinph.2009.12.025","article-title":"A new EEG recording system for passive dry electrodes","volume":"121","author":"Gargiulo","year":"2010","journal-title":"Clin. Neurophysiol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yu, Y.H., Chen, S.H., Chang, C.L., Lin, C.T., Hairston, W.D., and Mrozek, R.A. (2016). New flexible silicone-based EEG dry sensor material compositions exhibiting improvements in lifespan, conductivity, and reliability. Sensors, 16.","DOI":"10.3390\/s16111826"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1522","DOI":"10.1109\/TBME.2014.2308552","article-title":"Integrated circuits and electrode interfaces for noninvasive physiological monitoring","volume":"61","author":"Ha","year":"2014","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2553","DOI":"10.1109\/TBME.2015.2481482","article-title":"Real-time neuroimaging and cognitive monitoring using wearable dry EEG","volume":"62","author":"Mullen","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Grierson, M., and Kiefer, C. (2011, January 7\u201312). Better brain interfacing for the masses: Progress in event-related potential detection using commercial brain computer interfaces. Proceedings of the CHI 11 Extended Abstracts on Human Factors in Computing Systems, Vancouver, BC, Canada.","DOI":"10.1145\/1979742.1979828"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1007\/s13246-017-0538-2","article-title":"EEG-based alpha neurofeedback training for mood enhancement","volume":"40","author":"Phneah","year":"2017","journal-title":"Australas. Phys. Eng. Sci. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1093\/qjmed\/hcm051","article-title":"Cognitive assessment in the elderly: A review of clinical methods","volume":"100","author":"Woodford","year":"2007","journal-title":"QJM"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"384","DOI":"10.3389\/fneur.2017.00384","article-title":"Cognitive improvement and brain changes after real-time functional MRI neurofeedback training in healthy elderly and prodromal alzheimer\u2019s disease","volume":"8","author":"Hohenfeld","year":"2017","journal-title":"Front. Neurol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2662","DOI":"10.1002\/hbm.23201","article-title":"Neurofeedback training of EEG alpha rhythm enhances episodic and working memory","volume":"37","author":"Hsueh","year":"2016","journal-title":"Hum. Brain Mapp."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1097\/HTR.0b013e318238f146","article-title":"Magnitudes of decline on Automated Neuropsychological Assessment Metrics subtest scores relative to predeployment baseline performance among service members evaluated for traumatic brain injury in Iraq","volume":"27","author":"Bryan","year":"2012","journal-title":"J. Head Trauma Rehabil."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1089\/apc.2011.0051","article-title":"Concurrent validity of a computer-based cognitive screening tool for use in adults with HIV disease","volume":"25","author":"Becker","year":"2011","journal-title":"AIDS Patient Care STDS"},{"key":"ref_17","unstructured":"Schneider, W., Eschman, A., and Zuccolotto, A. (2002). E-Prime Reference Guide, Psychology Software Tools Inc."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.neuroimage.2016.03.035","article-title":"Task-based neurofeedback training: A novel approach toward training executive functions","volume":"134","author":"Hosseini","year":"2016","journal-title":"Neuroimage"},{"key":"ref_19","first-page":"53","article-title":"Changes of the brain\u2019s bioelectrical activity in cognition, consciousness, and some mental disorders","volume":"31","author":"Azimi","year":"2017","journal-title":"Med. J. Islam. Repub. Iran"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"239","DOI":"10.4236\/jbbs.2012.22027","article-title":"Comments for current interpretation EEG alpha activity: A review and analysis","volume":"2","author":"Bazanova","year":"2012","journal-title":"J. Behav. Brain Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10484-005-2169-8","article-title":"Increasing individual upper alpha power by neurofeedback improves cognitive performance in human subjects","volume":"30","author":"Hanslmayr","year":"2005","journal-title":"Appl. Psychophysiol. Biofeedback"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0160-2896(96)80002-X","article-title":"EEG alpha rhythm frequency and intelligence in normal adults","volume":"23","author":"Anokhin","year":"1996","journal-title":"Intelligence"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.ijpsycho.2012.07.182","article-title":"Individual alpha neurofeedback training effect on short term memory","volume":"86","author":"Nan","year":"2012","journal-title":"Int. J. Psychophysiol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.brainresrev.2006.06.003","article-title":"EEG alpha oscillations: The inhibition-timing hypothesis","volume":"53","author":"Klimesch","year":"2007","journal-title":"Brain Res. Rev."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/s10484-014-9257-6","article-title":"The effects of individual upper alpha neurofeedback in ADHD: An open-label pilot study","volume":"39","author":"Escolano","year":"2014","journal-title":"Appl. Psychophysiol. Biofeedback"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lisi, G., Hamaya, M., Noda, T., and Morimoto, J. (2016, January 16\u201321). Dry-wireless EEG and asynchronous adaptive feature extraction towards a plug-and-play co-adaptive brain robot interface. Proceedings of the IEEE International Conference on Robotics and Automation, Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487227"},{"key":"ref_27","unstructured":"Aznan, N.K.N., Bonner, S., Connolly, J.D., Moubayed, N.A., and Breckon, T.P. (arXiv, 2018). On the classification of SSVEP-based dry-EEG signals via convolutional neural networks, arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"24","DOI":"10.3389\/fnins.2018.00024","article-title":"Markov switching model for quick detection of event related desynchronization in EEG","volume":"12","author":"Lisi","year":"2018","journal-title":"Front. Neurosci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chi, Y.M., Wang, Y., Wang, Y.T., Jung, T.P., Kerth, T., and Cao, Y. (2013). A Practical Mobile Dry EEG System for Human Computer Interfaces, Springer.","DOI":"10.1007\/978-3-642-39454-6_69"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"87","DOI":"10.3390\/machines2010087","article-title":"Problems in assessment of novel biopotential front-end with dry electrode: A brief review","volume":"2","author":"Gargiulo","year":"2014","journal-title":"Machines"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1007\/s00221-003-1690-3","article-title":"A multimodal brain-based feedback and communication system","volume":"154","author":"Hinterberger","year":"2004","journal-title":"Exp. Brain Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1007\/s10484-009-9112-3","article-title":"Identifying indices of learning for alpha neurofeedback training","volume":"34","author":"Dempster","year":"2009","journal-title":"Appl. Psychophysiol. Biofeedback"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1097\/00004691-199711000-00010","article-title":"Timing of EEG-based cursor control","volume":"14","author":"Wolpaw","year":"1997","journal-title":"J. Clin. Neurophysiol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1186\/s12938-017-0418-8","article-title":"Portable wireless neurofeedback system of EEG alpha rhythm enhances memory","volume":"16","author":"Wei","year":"2017","journal-title":"Biomed. Eng. Online"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/jnp.12030","article-title":"Attention network test: Assessment of cognitive function in chronic fatigue syndrome","volume":"9","author":"Togo","year":"2015","journal-title":"J. Neuropsychol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1177\/154193120905301802","article-title":"Validation of a dry electrode system for EEG","volume":"53","author":"Estepp","year":"2009","journal-title":"Hum. Factors Ergon. Soc. Annu. Meet. Proc."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Epstein (2006). Guideline 1: Minimum technical requirements for performing clinical electroencephalography. Am. J. Electroneurodiagn. Technol., 46, 198\u2013204.","DOI":"10.1080\/1086508X.2006.11079576"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/0013-4694(70)90186-0","article-title":"EEG alpha activity influenced by visual input and not by eye position","volume":"28","author":"Chapman","year":"1970","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_39","first-page":"2184","article-title":"Real-time modeling and 3D visualization of source dynamics and connectivity using wearable EEG","volume":"2013","author":"Mullen","year":"2013","journal-title":"Conf. Proc. IEEE Eng. Med. Biol. Soc."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Covell, M., and Richardson, J. (1991, January 14\u201317). A new, efficient structure for the short-time Fourier transform, with an application in code-division sonar imaging. Proceedings of the Acoustics, Speech, and Signal Processing, Toronto, ON, Canada.","DOI":"10.1109\/ICASSP.1991.150805"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1109\/TNSRE.2003.814428","article-title":"EEG changes accompanying learned regulation of 12-Hz EEG activity","volume":"11","author":"Delorme","year":"2003","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/0165-0270(94)00111-S","article-title":"Gabor Filters\u2014An informative way for analyzing event-related brain activity","volume":"56","author":"Sinkkonen","year":"1995","journal-title":"J. Neurosci. Methods"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e9753","DOI":"10.1097\/MD.0000000000009753","article-title":"Disordered high-frequency oscillation in face processing in schizophrenia patients","volume":"97","author":"Liu","year":"2018","journal-title":"Medicine"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1580","DOI":"10.1016\/j.neuroimage.2006.02.034","article-title":"Mechanisms of evoked and induced responses in MEG\/EEG","volume":"31","author":"David","year":"2006","journal-title":"Neuroimage"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2406","DOI":"10.1016\/j.clinph.2013.05.020","article-title":"Neurofeedback training improves attention and working memory performance","volume":"124","author":"Wang","year":"2013","journal-title":"Clin. Neurophysiol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"279","DOI":"10.3389\/fpsyg.2011.00279","article-title":"Positive affect versus reward: Emotional and motivational influences on cognitive control","volume":"2","author":"Chiew","year":"2011","journal-title":"Front. Psychol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1037\/0022-3514.69.4.719","article-title":"The structure of psychological well-being revisited","volume":"69","author":"Ryff","year":"1995","journal-title":"J. Pers. Soc. Psychol."},{"key":"ref_48","first-page":"124","article-title":"Virtual reality and brain computer interface in neurorehabilitation","volume":"29","author":"Salisbury","year":"2016","journal-title":"Proc. (Bayl. Univ. Med. Cent.)"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1038\/nn.3940","article-title":"Closed-loop training of attention with real-time brain imaging","volume":"18","author":"DeBettencourt","year":"2015","journal-title":"Nat. Neurosci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"157","DOI":"10.3389\/fnagi.2016.00157","article-title":"An alpha and theta intensive and short neurofeedback protocol for healthy aging working-memory training","volume":"8","author":"Reis","year":"2016","journal-title":"Front. Aging Neurosci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1007\/s12559-015-9379-z","article-title":"Subject-specific channel selection using time information for motor imagery brain\u2013computer interfaces","volume":"8","author":"Yang","year":"2016","journal-title":"Cogn. Comput."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.bspc.2016.11.018","article-title":"Automatic channel selection in EEG signals for classification of left or right hand movement in brain computer interfaces using improved binary gravitation search algorithm","volume":"33","author":"Ghaemi","year":"2017","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1109\/ACCESS.2016.2637409","article-title":"A brain-computer interface based on a few-channel eeg-fnirs bimodal system","volume":"5","author":"Ge","year":"2017","journal-title":"IEEE Access"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.bspc.2017.06.016","article-title":"Subject-specific time-frequency selection for multi-class motor imagery-based BCIs using few Laplacian EEG channels","volume":"38","author":"Yang","year":"2017","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Ozmen, N.G., Gumusel, L., and Yang, Y. (2018). A Biologically inspired approach to frequency domain feature extraction for EEG Classification. Comput. Math. Methods Med.","DOI":"10.1155\/2018\/9890132"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Funahashi, S. (2017). Working memory in the prefrontal cortex. Brain Sci., 7.","DOI":"10.3390\/brainsci7050049"},{"key":"ref_57","first-page":"4741","article-title":"Beta\/theta ratio neurofeedback training effects on the spectral topography of EEG","volume":"2015","author":"Yang","year":"2015","journal-title":"Conf. Proc. IEEE Eng. Med. Biol. Soc."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"846","DOI":"10.4236\/psych.2011.28129","article-title":"The Effects of neurofeedback training on memory performance in elderly subjects","volume":"2","author":"Lecomte","year":"2011","journal-title":"Psychology"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1007\/s00221-012-3305-3","article-title":"Frontal theta is a signature of successful working memory manipulation","volume":"224","author":"Itthipuripat","year":"2013","journal-title":"Exp. Brain Res."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"831","DOI":"10.1073\/pnas.95.3.831","article-title":"Frontoparietal cortical networks for directing attention and the eye to visual locations: Identical, independent, or overlapping neural systems?","volume":"95","author":"Corbetta","year":"1998","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_61","first-page":"568","article-title":"Flaws in current human training protocols for spontaneous brain-computer interfaces: Lessons learned from instructional design","volume":"7","author":"Fabien","year":"2013","journal-title":"Front. Hum. Neurosci."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"81","DOI":"10.2147\/MDER.S36691","article-title":"Investigating the role of combined acoustic-visual feedback in one-dimensional synchronous brain computer interfaces, a preliminary study","volume":"5","author":"Gargiulo","year":"2012","journal-title":"Med. Devices"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1109\/TNSRE.2005.847369","article-title":"Visual spatial attention tracking using high-density SSVEP data for independent brain-computer communication","volume":"13","author":"Kelly","year":"2005","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_64","unstructured":"Kauhanen, L., Palom\u00e4ki, T., Jyl\u00e4nki, P., Aloise, F., Nuttin, M., and Mill\u00e1n, J.D.R. (2006). Haptic Feedback Compared with Visual Feedback for BCI, Verlag Der Tu Graz."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1715","DOI":"10.1109\/TNSRE.2016.2597243","article-title":"Neurofeedback control in parkinsonian patients using electrocortigraphy signals accessed wirelessly with a chronic, fully implanted device","volume":"25","author":"Khanna","year":"2017","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1908","DOI":"10.1161\/STROKEAHA.116.016304","article-title":"Contralesional brain-computer interface control of a powered exoskeleton for motor recovery in chronic stroke survivors","volume":"48","author":"Bundy","year":"2017","journal-title":"Stroke"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1109\/TMECH.2016.2606642","article-title":"Human cooperative wheelchair with brain-machine interaction based on shared control strategy","volume":"22","author":"Li","year":"2017","journal-title":"IEEE-ASME Trans. Mechatron."},{"key":"ref_68","unstructured":"Yang, Y., Wiart, J., and Bloch, I. (2013, January 27). Towards next generation human-computer interaction\u2014Brain-computer interfaces: Applications and challenges. Proceedings of the First International Symposium of Chinese CHI, Paris, France."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Hu, X.L., Zhao, T., Yao, J., Kuang, Y., and Yang, Y. (2017). Advances in neural engineering for rehabilitation. Behav. Neurol.","DOI":"10.1155\/2017\/9240921"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/10\/3396\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:24:51Z","timestamp":1760196291000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/10\/3396"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10,11]]},"references-count":69,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2018,10]]}},"alternative-id":["s18103396"],"URL":"https:\/\/doi.org\/10.3390\/s18103396","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,10,11]]}}}