A compact and interpretable convolutional neural network for cross-subject driver drowsiness detection from single-channel EEG
DOI: 10.1016/j.ymeth.2021.04.017
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Summary
This paper addresses the challenge of developing a calibration-free, cross-subject driver drowsiness detection system using single-channel electroencephalography (EEG). Driver drowsiness is a significant cause of road fatalities, and while EEG is an effective physiological signal for detection, it suffers from high inter-subject variability and non-stationary noise. The authors aim to overcome these limitations by proposing a compact and interpretable Convolutional Neural Network (CNN) that can discover shared neurophysiological features across different subjects without requiring subject-specific calibration. The study utilizes an open dataset containing EEG recordings from 27 subjects performing a simulated driving task. The authors preprocessed the data by filtering, removing artifacts, and downsampling to 128 Hz. They extracted 3-second epochs from the Oz channel, labeling them as "alert" or "drowsy" based on local and global reaction times to lane-departure events. After filtering for class balance and session quality, the final dataset comprised 2,022 samples from 11 subjects. The proposed model consists of a single 1D convolutional layer with 32 filters, followed by batch normalization, an ELU activation layer, and a Global Average Pooling (GAP) layer. The GAP layer is critical for reducing model parameters and enabling the use of Class Activation Mapping (CAM) for interpretability. The model was evaluated using leave-one-subject-out cross-validation and compared against two deep learning baselines (Deep CNN and EEGNet) and eight conventional machine learning methods using band power features. The proposed CNN achieved an average cross-subject classification accuracy of 73.22%, outperforming the Deep CNN and EEGNet models, which stabilized at lower accuracies. It also surpassed most conventional methods, including Support Vector Machines (69.27%) and Logistic Regression (67.77%), with statistically significant improvements over several baselines. Crucially, the visualization of the model’s decision process via CAM revealed that the network learned biologically explainable features. Specifically, the model identified Alpha spindles and Theta bursts as indicators of drowsiness. Conversely, it utilized artifacts typically associated with wakefulness, such as muscle activity and sensor drifts, to recognize the alert state. This demonstrates that the compact architecture effectively captures discriminative patterns shared across subjects. The significance of this work lies in demonstrating that compact CNNs can serve as powerful tools for discovering interpretable, cross-subject EEG features for mental state classification. By achieving high accuracy without subject-specific calibration and providing visual evidence of learned neurophysiological patterns, the study offers a viable direction for real-time, consumer-grade drowsiness monitoring systems. The ability to interpret deep learning decisions enhances trust in the model and aids in the discovery of new biomarkers for fatigue, addressing both performance and transparency challenges in EEG-based brain-computer interfaces.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | openalex | — | — | 5 | 2026-08-09 |
| extract | success | cached | — | — | 3 | 2026-08-10 |
| clean | success | clean | — | — | 1 | 2026-08-09 |
| chunk | success | chunk | — | — | 1 | 2026-08-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-08-09 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 2 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 10 | 2026-08-11 |
| verify | success | — | — | — | 2 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.
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- Empirical Findings: physiological data