{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T13:18:06Z","timestamp":1788441486262,"version":"build-2803163510"},"reference-count":57,"publisher":"IMR Press","issue":"8","license":[{"start":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T00:00:00Z","timestamp":1787702400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Integr. Neurosci."],"abstract":"<jats:p>Background:Gamma and beta oscillations of electroencephalography (EEG) are involved in multiple perceptual information processing. Neurofeedback training (NFB) is a non-invasive neuromodulation approach that provides an endogenous paradigm to investigate the causal relationship between brain oscillations and cognitive abilities. In this study we investigated the effectiveness and specificity of gamma and combined gamma-beta NFB training for improving visual perception in healthy adults.Methods:A total of 48 healthy participants were recruited and randomly assigned into three groups. Four participants withdrew during the study, leaving 44 participants for final analysis : a sham group (n = 14), a gamma NFB training group (n = 16) targeting parieto-occipital (PO) gamma rhythms, and a gamma-beta training group (n = 14) targeting both PO gamma and central beta rhythms. All participants underwent six NFB sessions, each containing nine blocks. Baseline tasks for resting state and visual perception measurements were conducted before and after NFB training.Results:Both training groups showed significant improvements in reaction time and accuracy. Importantly, EEG data obtained during the training process and baseline tests showed that relative PO gamma rhythms were increased in both groups, while relative central beta rhythms were enhanced only in the gamma-beta group. EZ-diffusion model analysis revealed that these improvements were primarily driven by increased drift rates and reduced non-decision times, suggesting enhanced efficiency in perceptual evidence accumulation.Conclusions:The current results validate the effectiveness of NFB training and demonstrate clear spectral and regional specificity, revealing different functional contributions of the two oscillations during visual perception. In the context of a randomized, sham-controlled design, these results provide preliminary causal evidence for the involvement of gamma- and beta-band activity in visual processing.Clinical Trial Registration:No: ChiCTR2600124537. https:\/\/www.chictr.org.cn\/showproj.html?proj=292703.<\/jats:p>","DOI":"10.31083\/jin46741","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T12:51:46Z","timestamp":1788439906000},"source":"Crossref","is-referenced-by-count":0,"title":["Gamma and Beta Neurofeedback Training Can Improve Visual Perception With Spectral and Regional Specificity"],"prefix":"10.31083","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-9669-6472","authenticated-orcid":true,"given":"Lu","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Cognitive Neuroscience and Learning & IDG\/McGovern Institute for Brain Research, Beijing Normal University, 100875 Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1388-7312","authenticated-orcid":true,"given":"Yong","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Medicine, Zhujiang Hospital, Southern Medical University, 510280 Guangzhou, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1877-2709","authenticated-orcid":true,"given":"Zhaohuan","family":"Ding","sequence":"additional","affiliation":[{"name":"Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, 510640 Guangzhou, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2685-1677","authenticated-orcid":true,"given":"He","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation Science and Engineering, South China University of Technology, 510641 Guangzhou, Guangdong, China"},{"name":"Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), 510335 Guangzhou, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1359-5130","authenticated-orcid":true,"given":"Xiaoli","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cognitive Neuroscience and Learning & IDG\/McGovern Institute for Brain Research, Beijing Normal University, 100875 Beijing, China"},{"name":"School of Automation Science and Engineering, South China University of Technology, 510641 Guangzhou, Guangdong, China"},{"name":"Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), 510335 Guangzhou, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"16242","published-online":{"date-parts":[[2026,8,26]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","unstructured":"Fries P. 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