计算机科学
稳健性(进化)
人工智能
正规化(语言学)
域适应
脑-机接口
机器学习
回归
回归分析
脑电图
模糊逻辑
模式识别(心理学)
数据挖掘
分类器(UML)
数学
统计
精神科
化学
基因
生物化学
心理学
作者
Dongrui Wu,Vernon J. Lawhern,Stephen Gordon,Brent J. Lance,Chin‐Teng Lin
出处
期刊:IEEE Transactions on Fuzzy Systems
[Institute of Electrical and Electronics Engineers]
日期:2017-12-01
卷期号:25 (6): 1522-1535
被引量:107
标识
DOI:10.1109/tfuzz.2016.2633379
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 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.
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