One Hundred Years Later! The Current Utility of EEG Tools in Psychiatry: Some Insights and Perspectives
Bibliographic record
Abstract
ObjectiveSince the pioneering work of Hans Berger in 1929 introducing the utility of human electroencephanlography (EEG) in psychiatry, a considerable amount of work has been devoted to the identification of pathophysiological mechanisms of mental diseases. However, how electrophysiology may be useful in clinical psychiatric settings is still matter of debate. Here we provide a summary of current emerging data and perspectives regarding the promising utility of various EEG tools in the treatment of mental diseases.Methods and ResultsIn this report we focus on new insights reported through the use of various EEG tools (quantitative EEG, QEEG; cognitive event-related potentials, ERPs) and some new EEG-based methods (Mobile Brain/Body Imaging or Artificial Intelligence algorithms) suggesting that their use might be helpful at the clinical level in the management of various forms of mental diseases.ConclusionGiven the encouraging results highlighting how these electrophysiological tools may be used with regard to mental disorders, continued efforts to better implement these EEG tools into psychiatric clinical settings remains one of the most pressing challenges for neurophysiologists.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".