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The electroencephalogram (EEG) is the most popular form of input for braincomputer interfaces (BCIs). However, it can be easily contaminated by variousartifacts and noise, e.g., eye blink, muscle activities, powerline noise, etc.Therefore, the EEG signals are often filtered both spatially and temporally toincrease the signal-to-noise ratio before they are fed into a machine learningalgorithm for recognition. This paper considers spatial filtering,particularly, the common spatial pattern (CSP) filters for EEG classification.In binary classification, CSP seeks a set of filters to maximize the variancefor one class while minimizing it for the other. We first introduce thetraditional solution, and then a new solution based on a slightly differentobjective function. We performed comprehensive experiments on motor imagery tocompare the two approaches, and found that generally the traditional CSPsolution still gives better results. We also showed that adding regularizationto the covariance matrices can improve the final classification performance, nomatter which objective function is used.

Spatial Filtering for Brain Computer Interfaces: A Comparison between the Common Spatial Pattern and Its Variant