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Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI)
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DOI：10.1109/TFUZZ.2017.2688423.png)
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Abstract
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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%.
Keywords:
Brain-computer interface (BCI)
common spatial pattern (CSP)
electroencephalogram (EEG)
fuzzy sets
psychomotor vigilance task (PVT)
response speed (RS) estimation
spatial filtering
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