Classifying Severity of Depression and Anxiety by Analyzing Electroencephalography (EEG) Signals for Neurophysiological Biomarkers
Bibliographic record
Abstract
Biomarkers detected in neurophysiological signals can be analyzed to determine indicators of disorders. Electroencephalography (EEG) detects neural activity in the brain and the signals can be analyzed to diagnose stress and mood disorders. The objective is to analyze EEG signals to identify and delineate the severity of depression and/or anxiety validated by the results of psychological test scores. Signals were analyzed from a public database of 119 participants aged 18 to 24 with 45 individuals having moderate to severe anxiety and/or depression and the remaining 74 people having minimal or none. Using extracted signal features, individual variations were compared during a testing protocol for both groups, affected and unaffected. Similarities, and asymmetry, were numerically and visually examined between the left and right brain hemispheres as well as the specific channels. In addition, machine learning classification was performed to predict the class based on the input data. The results demonstrate indications of physiological differences between participants, indicating a likely presence or absence of a mood disorder. Understanding the complexities of how mood and anxiety disorders, including its comorbidities, are physiologically manifested is critical for accurate and objective diagnosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".