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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".