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

## TL;DR

This paper introduces two novel spatial filtering methods for EEG-based regression in brain-computer interfaces, significantly improving signal quality and estimation accuracy in a large-scale psychomotor vigilance study.

## Contribution

The paper extends common spatial pattern (CSP) filters to regression problems using fuzzy sets, demonstrating their effectiveness in EEG response speed estimation.

## Key findings

- Spatial filters significantly increase EEG signal quality.
- Reduction of root mean square estimation error by up to 19.77%.
- Increase in correlation to true response speed by up to 86.47%.

## Abstract

Electroencephalogram (EEG) signals are frequently used in brain-computer interfaces (BCIs), but they are easily contaminated by artifacts and noises, 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 making use of 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%.