Transfer Learning for Subject-Independent Sleep DeprivationDetection from Resting-State EEG
Bibliographic record
Abstract
Sleep deprivation (SD) impairs cognition and heightens safety risks, yet reliable electroencephalography (EEG)-based detection remains challenging in low-data settings. We evaluated transfer learning with a compact Convolutional Neural Network (CNN) (EEGNetv4) to classify SD versus well-rested wakefulness using an open-source EEG dataset containing eyes-open resting-state data from 71 healthy young adults. EEGNetv4 was initialized with publicly available weights pretrained on an Event-Related Potential (ERP) dataset. Shape-compatible layers were transferred and frozen, with the remaining layers trained on the target data. Baselines comprised EEGNetv4, a bidirectional Long Short-Term Memory (LSTM), and a Transformer model, each trained without pretraining. Five-fold subject-independent cross-validation was used to evaluate model performance. EEGNetv4 with transfer learning achieved the highest mean accuracy (70.79% ± 4.17), outperforming EEGNetv4 trained from scratch (65.75% ± 5.48), the Transformer (63.35% ± 2.78), and the LSTM (61.70% ± 3.20). These findings suggest that leveraging pretrained EEG representations can enhance subject-generalizable SD classification in small-sample contexts, supporting transfer learning as a pragmatic strategy for neurophysiological applications.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".