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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/46295

Title: Spatial Filtering for EEG-Based Regression Problems in Brain–Computer Interface (BCI)
Authors: Dongrui Wu
Jung-Tai King
Chun-Hsiang Chuang
Chin-Teng Lin
Tzyy-Ping Jung
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: Brain–computer interface (BCI);common spatial pattern (CSP);electroencephalogram (EEG);fuzzy sets;psychomotor vigilance task (PVT);response speed (RS) estimation;spatial filtering
Date: 2018-04
Issue Date: 2018-05-14T07:38:28Z
Publisher: IEEE Transactions on Fuzzy Systems
Abstract: Abstract:
Electroencephalogram (EEG) signals are frequently used in brain-computer interfaces (BC!s), 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 BC! classification problems, but their applications in BC! regression problems have been very limited. This paper proposes two common spatial pattern (CSP) filters for EEG-based regression problems in BC!, 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%.
Relation: 26(2), pp.771-781
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/46295
Appears in Collections:[資訊工程學系] 期刊論文

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