標題: | Driver Drowsiness Estimation From EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR) |
作者: | Wu, Dongrui Lawhern, Vernon J. Gordon, Stephen Lance, Brent J. Lin, Chin-Teng 腦科學研究中心 Brain Research Center |
關鍵字: | Brain-computer interface;domain adaptation (DA);EEG;ensemble learning;fuzzy sets (FSs);transfer learning (TL) |
公開日期: | 1-十二月-2017 |
摘要: | 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 fromEEG 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. |
URI: | http://dx.doi.org/10.1109/TFUZZ.2016.2633379 http://hdl.handle.net/11536/144204 |
ISSN: | 1063-6706 |
DOI: | 10.1109/TFUZZ.2016.2633379 |
期刊: | IEEE TRANSACTIONS ON FUZZY SYSTEMS |
Volume: | 25 |
起始頁: | 1522 |
結束頁: | 1535 |
顯示於類別: | 期刊論文 |