{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T21:45:13Z","timestamp":1790199913657,"version":"4.1.0"},"reference-count":84,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T00:00:00Z","timestamp":1624492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100017284","name":"Jihua Laboratory","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100017284","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Hum. Neurosci."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>In combined with neurofeedback, Motor Imagery (MI) based Brain-Computer Interface (BCI) has been an effective long-term treatment therapy for motor dysfunction caused by neurological injury in the brain (e.g., post-stroke hemiplegia). However, individual neurological differences have led to variability in the single sessions of rehabilitation training. Research on the impact of short training sessions on brain functioning patterns can help evaluate and standardize the short duration of rehabilitation training. In this paper, we use the electroencephalogram (EEG) signals to explore the brain patterns\u2019 changes after a short-term rehabilitation training.<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and Methods<\/jats:title><jats:p>Using an EEG-BCI system, we analyzed the changes in short-term (about 1-h) MI training data with and without visual feedback, respectively. We first examined the EEG signal\u2019s Mu band power\u2019s attenuation caused by Event-Related Desynchronization (ERD). Then we use the EEG\u2019s Event-Related Potentials (ERP) features to construct brain networks and evaluate the training from multiple perspectives: small-scale based on single nodes, medium-scale based on hemispheres, and large-scale based on all-brain.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Results showed no significant difference in the ERD power attenuation estimation in both groups. But the neurofeedback group\u2019s ERP brain network parameters had substantial changes and trend properties compared to the group without feedback. The neurofeedback group\u2019s Mu band power\u2019s attenuation increased but not significantly (fitting line slope = 0.2, <jats:italic>t<\/jats:italic>-test value <jats:italic>p<\/jats:italic> &amp;gt; 0.05) after the short-term MI training, while the non-feedback group occurred an insignificant decrease (fitting line slope = \u22120.4, <jats:italic>t<\/jats:italic>-test value <jats:italic>p<\/jats:italic> &amp;gt; 0.05). In the ERP-based brain network analysis, the neurofeedback group\u2019s network parameters were attenuated in all scales significantly (<jats:italic>t<\/jats:italic>-test value: <jats:italic>p<\/jats:italic> &amp;lt; 0.01); while the non-feedback group\u2019s most network parameters didn\u2019t change significantly (<jats:italic>t<\/jats:italic>-test value: <jats:italic>p<\/jats:italic> &amp;gt; 0.05).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The MI-BCI training\u2019s short-term effects does not show up in the ERD analysis significantly but can be detected by ERP-based network analysis significantly. Results inspire the efficient evaluation of short-term rehabilitation training and provide a useful reference for subsequent studies.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fnhum.2021.627100","type":"journal-article","created":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T06:45:23Z","timestamp":1624517123000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Changes in EEG Brain Connectivity Caused by Short-Term BCI Neurofeedback-Rehabilitation Training: A Case Study"],"prefix":"10.3389","volume":"15","author":[{"given":"Youhao","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuzhu","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiying","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongbo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2021,6,24]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.jneumeth.2015.01.033","article-title":"Performance variation in motor imagery brain\u2013computer interface: a brief review.","volume":"243","author":"Ahn","year":"2015","journal-title":"J. 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