# Driver Drowsiness Estimation from EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR)

arXiv: https://arxiv.org/abs/1702.02901 · HTML: https://mlsanity.com/paper/1702.02901/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2017.

## Built on by (most cited first, 9 of 9)

- [Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review](https://mlsanity.com/paper/2102.02729/) (2021)
- [Revisiting Euclidean Alignment for Transfer Learning in EEG-Based Brain-Computer Interfaces](https://mlsanity.com/paper/2502.09203/) (2025)
- [Alignment-Based Adversarial Training (ABAT) for Improving the Robustness and Accuracy of EEG-Based BCIs](https://mlsanity.com/paper/2411.02094/) (2024)
- [Adversarial Artifact Detection in EEG-Based Brain-Computer Interfaces](https://mlsanity.com/paper/2212.00727/) (2022)
- [Adaptive Gaussian Fuzzy Classifier for Real-Time Emotion Recognition in Computer Games](https://mlsanity.com/paper/2103.03488/) (2021)
- [A Fuzzy-set-based Joint Distribution Adaptation Method for Regression and its Application to Online Damage Quantification for Structural Digital Twin](https://mlsanity.com/paper/2211.02656/) (2022)
- [EEG-Fest: Few-shot based Attention Network for Driver's Vigilance Estimation with EEG Signals](https://mlsanity.com/paper/2211.03878/) (2022)
- [Decoding Neural Activity to Assess Individual Latent State in Ecologically Valid Contexts](https://mlsanity.com/paper/2304.09050/) (2023)
- [EGNN-C+: Interpretable Evolving Granular Neural Network and Application in Classification of Weakly-Supervised EEG Data Streams](https://mlsanity.com/paper/2402.17792/) (2024)

## Abstract (arXiv)

One big challenge that hinders the transition of brain-computer interfaces (BCIs) from laboratory settings to real-life applications is the availability of high-performance and robust learning algorithms that can effectively handle individual differences, i.e., algorithms that can be applied to a new subject with zero or very little subject-specific calibration data. Transfer learning and domain adaptation have been extensively used for this purpose. However, most previous works focused on classification problems. This paper considers an important regression problem in BCI, namely, online driver drowsiness estimation from EEG signals. By integrating fuzzy sets with domain adaptation, we propose a novel online weighted adaptation regularization for regression (OwARR) algorithm to reduce the amount of subject-specific calibration data, and also a source domain selection (SDS) approach to save about half of the computational cost of OwARR. Using a simulated driving dataset with 15 subjects, we show that OwARR and OwARR-SDS can achieve significantly smaller estimation errors than several other approaches. We also provide comprehensive analyses on the robustness of OwARR and OwARR-SDS.