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

## TL;DR

This paper introduces OwARR, a novel online transfer learning algorithm that improves driver drowsiness estimation from EEG signals with minimal calibration data, demonstrating superior accuracy and efficiency.

## Contribution

It presents OwARR, a new online weighted adaptation regularization method for regression in BCI, integrating fuzzy sets and source domain selection to reduce calibration effort and computational cost.

## Key findings

- OwARR achieves significantly lower estimation errors than existing methods.
- OwARR-SDS reduces computational cost by about half.
- The methods demonstrate robustness across subjects.

## Abstract

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.