Upper-alpha EEG neurofeedback training promotes reorganization of both clustered and integrated brain activity
Bibliographic record
Abstract
Neurofeedback (NF) aims to support an individual's self-regulation of a pattern of brain activity by providing a real-time representation of that pattern, commonly measured using electroencephalography (EEG). While NFtraining has been associated with the reorganization of brain networks, the effect of the feedback sensory modality remains to be fully elucidated. This study assesses how different feedback sensory modalities affect the outcome of EEG-based NF-training and functional connectivity across brain networks. Twenty healthy volunteers were split into three groups based on the sensory modality used for the feedback: visual 2D screen, headmounted display, or auditory, and performed upper-alpha (UA) band EEG-based NF-training along four sessions. The EEG data were evaluated in terms of: i) the within-session variation of the relative amplitude of the UA (RAUA) at the EEG Cz channel; ii) the within-session variation of the UA functional connectivity patterns across all channels, computed using the imaginary part of coherency; and iii) UA band global network metrics transitivity, characteristic path length, and global efficiency. We found an increment of functional connectivity between channels over parietal areas (e.g., Pz) in the first sessions, independently of the feedback sensory modality. Also, the visual 2D screen and auditory sensory modalities yielded statistically significant within-session increases in transitivity and global efficiency and decreases in characteristic path length. These findings suggest that the UA EEG-based NF-training protocol promotes both clustered and integrated brain activity reorganization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".