Towards the Classification of Single-Trial Event-Related Potentials Using Adapted Wavelets and Particle Swarm Optimization

The accurate detection of event-related potentials (ERPs) is of great importance to construct brain-machine interfaces (BMI) and constitutes a classification problem in which the appropriate selection of features from dense-array EEG signals and tuning of the classifier parameters are critical. In the present work, we propose a method for classifying single-trial ERPs using a combination of the Lifting Wavelet Transform (LWT), Support Vector Machines (SVM) and Particle Swarm Optimization (PSO). In particular, the LWT filters, the set of EEG channels and SVM parameters that maximize the classification accuracy are searched using PSO. We evaluate the method´s performance through offline analyses on the datasets from the BCI Competitions II and III. The proposed method achieved in most cases a similar or higher classification accuracy than that achieved by other methods, and adapted wavelet basis functions and channel sets that match the time-frequency and spatial properties of the P300 ERP.