Determination of Inter-Channel Interactions in EEG Sub-Frequency Bands Based on Coherence Analysis and Neural Networks During the Audiological Test Process
Bibliographic record
Abstract
Audiological testing is important for correctly diagnosing hearing problems and making appropriate treatment plans.During the audiological test, it is important to reveal which subfrequency bands of the electroencephalogram (EEG) dominate, and which electrode regions have simultaneous activation for determination of the effects of the audiological test on the brain electrical activity.The purpose of this study is to determine the changes caused by the audiologic test process in the brain activity of individuals.The EEG signals were obtained from 36 volunteers during audiological testing and at rest.The EEG data were analyzed to show the effects of the test process according to the resting state.Dual electrode coherence analyses were performed for delta, theta, alpha, beta and gamma sub-frequency bands of EEG signals.In the study, neural activation in frontal and temporal positions was also examined using wavelet coherence during the audiological test task.In the study, it was also attempted to determine whether the coherence values of the electrode pairs could be used to distinguish between resting and audiological test conditions through the classification process.At this stage, an attempt was made to determine the most effective EEG sub-bands that distinguish resting and audiological test status.The results showed that there was a high coherence in the changes in alpha, theta and delta bands, especially in the symmetric temporal region, throughout the audiological test process.It seems that the sub-frequency bands of EEG signals in the audiological testing process in participants can be distinguished by coherence analysis.
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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 | 0.001 | 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".