Spatial Filtering for EEG-Based Regression Problems in Brain–Computer Interface (BCI)
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...
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| Main Authors: | , , , , |
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| Format: | Artigo |
| Jezik: | angleščina |
| Izdano: |
2017
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| Online dostop: | https://doi.org/10.1109/tfuzz.2017.2688423 |
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| Izvleček: | 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%. |
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