Drowsiness Detection Using Brain Signal Recognition Deep Neural Network (BSRDNN)
Bibliographic record
Abstract
A novel deep learning architecture, brain signal recognition deep neural network (BSRDNN), based on a one-dimensional convolutional neural network model (1D-CNN) and artificial neural network (ANN), is proposed for drowsiness detection from singlechannel electroencephalographic (EEG) data.The effectiveness of the method is shown using the MIT/BIH polysomnographic EEG dataset (MIT/BIH-PED) with more than 80h long-term EEG data collected by a single electrode.EEG signals for 16 subjects were classified by BSRDNN as wakefulness, drowsiness, and sleep.BSRDNN was used via two approaches: Option 1 consists of feature extraction and classification by deep learning; in Option 2, feature and classification are performed by machine learning algorithms, naï ve Bayes (NB), k-nearest neighbours (KNN), random forest (RF), and stochastic gradient descent (SGD).Combined-subject validation was applied extraction to enhance the performance of the proposed technique.Simulations demonstrated better performance in terms of accuracy, recall, F1-score and precision compared to the current state-of-the-art techniques applied to the same dataset: We obtained 92.31% overall accuracy in Option 1, and 94.8-100% in Option 2. The proposed novel BSRDNN model demonstrates clear superiority over those featured in published research that used the same MIT/BIH-PED dataset.It can perform its designated task with less trainable parameters and arithmetic operations compared to other models, resulting in faster training and testing phases.This enhanced speed facilitates quicker drowsiness detection, thereby reducing the overall time required for the process.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".