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Electroencephalogram (EEG) signals are frequently used in brain-computerinterfaces (BCIs), but they are easily contaminated by artifacts and noises, sopreprocessing must be done before they are fed into a machine learningalgorithm for classification or regression. Spatial filters have been widelyused to increase the signal-to-noise ratio of EEG for BCI classificationproblems, but their applications in BCI regression problems have been verylimited. This paper proposes two common spatial pattern (CSP) filters forEEG-based regression problems in BCI, which are extended from the CSP filterfor classification, by making use of fuzzy sets. Experimental results onEEG-based response speed estimation from a large-scale study, which collected143 sessions of sustained-attention psychomotor vigilance task data from 17subjects during a 5-month period, demonstrate that the two proposed spatialfilters can significantly increase the EEG signal quality. When used in LASSOand k-nearest neighbors regression for user response speed estimation, thespatial filters can reduce the root mean square estimation error by10.02-19.77%, and at the same time increase the correlation to the trueresponse speed by 19.39-86.47%.

Spatial Filtering for EEG-Based Regression Problems in Brain-Computer Interface (BCI)