標題: Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI)
作者: Wu, Dongrui
King, Jung-Tai
Chuang, Chun-Hsiang
Lin, Chin-Teng
Jung, Tzyy-Ping
腦科學研究中心
Brain Research Center
關鍵字: Brain-computer interface (BCI);common spatial pattern (CSP);electroencephalogram (EEG);fuzzy sets;psychomotor vigilance task (PVT);response speed (RS) estimation;spatial filtering
公開日期: 1-Apr-2018
摘要: Electroencephalogram (EEG) signals are frequently used in brain-computer interfaces (BCIs), but they are easily contaminated by artifacts and noise, so preprocessing must be done before they are fed into a machine learning algorithm for classification or regression. Spatial filters have been widely used to increase the signal-to-noise ratio of EEG for BCI classification problems, but their applications in BCI regression problems have been very limited. This paper proposes two common spatial pattern (CSP) filters for EEG-based regression problems in BCI, which are extended from the CSP filter for classification, by using fuzzy sets. Experimental results on EEG-based response speed estimation from a large-scale study, which collected 143 sessions of sustained-attention psychomotor vigilance task data from 17 subjects during a 5-month period, demonstrate that the two proposed spatial filters can significantly increase the EEG signal quality. When used in LASSO and k-nearest neighbors regression for user response speed estimation, the spatial filters can reduce the root-mean-square estimation error by 10.02-19.77%, and at the same time increase the correlation to the true response speed by 19.39-86.47%.
URI: http://dx.doi.org/10.1109/TFUZZ.2017.2688423
http://hdl.handle.net/11536/144749
ISSN: 1063-6706
DOI: 10.1109/TFUZZ.2017.2688423
期刊: IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume: 26
起始頁: 771
結束頁: 781
Appears in Collections:Articles