# A Compact and Interpretable Convolutional Neural Network for Cross-Subject Driver Drowsiness Detection from Single-Channel EEG

arXiv: https://arxiv.org/abs/2106.00613 · HTML: https://mlsanity.com/paper/2106.00613/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 52 references that are in the map.
- First posted on arXiv 30 May 2021.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG](https://mlsanity.com/paper/1703.04046/) (2017)
- [Deep learning-based electroencephalography analysis: a systematic review](https://mlsanity.com/paper/1901.05498/) (2019)
- [Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks](https://mlsanity.com/paper/1511.06448/) (2015)
- [Multi-channel EEG recordings during a sustained-attention driving task](https://mlsanity.com/paper/1809.06534/) (2018)

## Built on by (most cited first, 12 of 12)

- [Towards Best Practice of Interpreting Deep Learning Models for EEG-based Brain Computer Interfaces](https://mlsanity.com/paper/2202.06948/) (2022)
- [Explainable artificial intelligence approaches for brain-computer interfaces: a review and design space](https://mlsanity.com/paper/2312.13033/) (2023)
- [A New Era in Human Factors Engineering: A Survey of the Applications and Prospects of Large Multimodal Models](https://mlsanity.com/paper/2405.13426/) (2024)
- [FCPCA: Fuzzy clustering of high-dimensional time series based on common principal component analysis](https://mlsanity.com/paper/2505.07276/) (2025)
- [Subject-Independent Drowsiness Recognition from Single-Channel EEG with an Interpretable CNN-LSTM model](https://mlsanity.com/paper/2112.10894/) (2021)
- [An EEG Channel Selection Framework for Driver Drowsiness Detection via Interpretability Guidance](https://mlsanity.com/paper/2304.14920/) (2023)
- [Masked EEG Modeling for Driving Intention Prediction](https://mlsanity.com/paper/2408.07083/) (2024)
- [Modified TSception for Analyzing Driver Drowsiness and Mental Workload from EEG](https://mlsanity.com/paper/2512.21747/) (2025)
- [Studying Drowsiness Detection Performance while Driving through Scalable Machine Learning Models using Electroencephalography](https://mlsanity.com/paper/2209.04048/) (2022)
- [LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring](https://mlsanity.com/paper/2407.05749/) (2024)
- [SecureV2X: An Efficient and Privacy-Preserving System for Vehicle-to-Everything (V2X) Applications](https://mlsanity.com/paper/2508.19115/) (2025)
- [Bidirectional Temporal Dynamics Modeling for EEG-based Driving Fatigue Recognition](https://mlsanity.com/paper/2602.14071/) (2026)

## Abstract (arXiv)

Driver drowsiness is one of main factors leading to road fatalities and hazards in the transportation industry. Electroencephalography (EEG) has been considered as one of the best physiological signals to detect drivers drowsy states, since it directly measures neurophysiological activities in the brain. However, designing a calibration-free system for driver drowsiness detection with EEG is still a challenging task, as EEG suffers from serious mental and physical drifts across different subjects. In this paper, we propose a compact and interpretable Convolutional Neural Network (CNN) to discover shared EEG features across different subjects for driver drowsiness detection. We incorporate the Global Average Pooling (GAP) layer in the model structure, allowing the Class Activation Map (CAM) method to be used for localizing regions of the input signal that contribute most for classification. Results show that the proposed model can achieve an average accuracy of 73.22% on 11 subjects for 2-class cross-subject EEG signal classification, which is higher than conventional machine learning methods and other state-of-art deep learning methods. It is revealed by the visualization technique that the model has learned biologically explainable features, e.g., Alpha spindles and Theta burst, as evidence for the drowsy state. It is also interesting to see that the model uses artifacts that usually dominate the wakeful EEG, e.g., muscle artifacts and sensor drifts, to recognize the alert state. The proposed model illustrates a potential direction to use CNN models as a powerful tool to discover shared features related to different mental states across different subjects from EEG signals.