Feature Extraction to Identify Depression and Anxiety Based on EEG
Bibliographic record
Abstract
Biomarkers in neurophysiological signals can be analyzed to determine indicators of mood disorders for diagnosis. In this paper, EEG signals were analyzed from a public database of 119 subjects ages 18 to 24 performing a cognitive task. 45 subjects had moderate to severe anxiety and/or depression and the remaining 74 subjects had minimal or none. A subject's level of depression and/or anxiety was classified by standard psychological tests. EEG signals were preprocessed and separated into frequency bands: beta (12-30 Hz), alpha (8-12 Hz), theta (4-8 Hz) and delta (0.5-4 Hz). Features were extracted including Higuchi Fractal Dimension, correlation dimension, approximate entropy, Lyapunov exponent and detrended fluctuation analysis. Similarities, and asymmetry can be examined between the left and right brain hemispheres as well as the prefrontal cortex channels. ANOVA II analysis showed a significant difference (p<0.05) for topographical region comparisons of several features between the affected and unaffected subjects for specific features. The results demonstrate physiological asymmetry between high scoring subjects indicating a mood disorder, with low scoring, to be used as an indicator of illness. Understanding the complexities of how depression and anxiety are manifested physiologically including its comorbidities, is critical for accurate and objective diagnosis of mood and anxiety order disorders.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.001 |
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".