Artefacts Removal to Detect Visual Evoked Potentials in Brain Computer Interface Systems

The interference of artefacts with evoked scalp electroencephalogram (EEG) responses is a problem in event related brain computer interface (BCI) system that reduces signal quality and interpretability of user's intentions. Many strategies have been proposed to reduce the effects of non-neural artefacts, while the activity of neural sources that do not reflect the considered stimulation has been neglected. However discerning such activities from those to be retained is important, but subtle and difficult as most of their features are the same. We propose an automated method based on a combination of a genetic algorithm (GA) and a support vector machine (SVM) to select only the sources of interest. Temporal, spectral, wavelet, autoregressive and spatial properties of independent components (ICs) of EEG are inspected. The method selects the most distinguishing subset of features among this comprehensive fused set of information and identifies the components to be preserved. EEG data were recorded from 12 healthy subjects in a visual evoked potential (VEP) based BCI paradigm and the corresponding ICs were classified by experts to train and test the algorithm. They were contaminated with different sources of artefacts, including electromyogram (EMG), electrode connection problems, blinks and electrocardiogram (ECG), together with neural contributions not related to VEPs. The accuracy of ICs classification was about 88.5% and the energetic residual error in recovering the clean signals was 3%. These performances indicate that this automated method can effectively identify and remove main artefacts derived from either neural or non-neural sources while preserving VEPs. This could have important potential applications, contributing to speed and remove subjectivity of the cleaning procedure by experts. Moreover, it could be included in a real time BCI as a pre-processing step before the identification of the user’s intention.