Single-Trial Detection of Event-Related Potentials with Artificial Examples Based on Coloring Transformation

Abstract

AbstractNon-invasive Brain-Computer Interfaces (BCIs) using electroencephalography (EEG) recordings are the most common type of BCI. The detection of Event-Related Potentials (ERP) corresponding to the presentation of visual stimuli is one of the main paradigms in BCI, such as for the detection of the P300 ERP component that is used in the P300 speller. The typing speed and the information transfer rate in a BCI speller are directly related to the single-trial detection performance. It corresponds to the binary classification of brain evoked responses corresponding to the presentation of stimuli representing targets vs. non-targets. Many techniques have been proposed in the literature, ranging from shallow approaches using linear discriminant analysis to hierarchical and deep learning methods. For BCIs that require a calibration session, reducing its duration is critical for the implementation of BCIs in clinical settings. For this reason, data augmentation approaches allowing to increase the size of the training database can improve performance while keeping the same number of trials for the calibration session. In this paper, we propose to generate artificial trials based on the properties of the distribution of the signals after spatial filtering using the coloring transformation. The approach is compared with other approaches on the single-trial detection of ERPs from a public database of 8 subjects with amyotrophic lateral sclerosis. The results support the conclusion that artificial trials based on the coloring transformation can be used for training a classifier. However, they do not provide a substantial improvement then added as a data augmentation technique, compared to data augmentation using examples shifted temporally.KeywordsBrain-Computer InterfaceEvent-Related PotentialsArtificial examples