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• W2594654012 abstract "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 $19.39-86.47%$ ." @default.
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• W2594654012 date "2018-04-01" @default.
• W2594654012 modified "2023-10-09" @default.
• W2594654012 title "Spatial Filtering for EEG-Based Regression Problems in Brain–Computer Interface (BCI)" @default.
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• W2594654012 doi "https://doi.org/10.1109/tfuzz.2017.2688423" @default.
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