A comparison of feature extraction strategies using wavelet dictionaries and feature selection methods for single trial P300-based BCI

The P300 component of event-related potentials (ERPs) is widely used in the implementation of brain computer interfaces (BCI). In this context, one of the main issues to solve is the binary classification problem that entails differentiating between electroencephalographic (EEG) signals with and without P300. Given the particularly unfavorable signal-to-noise ratio (SNR) in the single-trial detection scenario, this is a challenging problem in the pattern recognition field. To the best of our knowledge, there are no previous experimental studies comparing feature extraction and selection methods for single trial P300-based BCIs using unified criteria and data. In order to improve the performance and robustness of single-trial classifiers, we analyzed and compared different alternatives for the feature generation and feature selection blocks. We evaluated different orthogonal decompositions based on the wavelet transform for feature extraction, as well as different filter, wrapper, and embedded alternatives for feature selection. Accuracies over 75% were obtained for most of the analyzed strategies with a relatively low computational cost, making them attractive for a practical BCI implementation using inexpensive hardware.