The event-related potential data processing using ART 2 network

The event-related potentials (ERPs) obtained by stimulation are much weaker than the continuous electro-encephalographic (EEG) signal. Therefore, the correct signal analysis is vital to detect the stimulation-driven signal components. This paper proposes the combination of matching pursuit for feature extraction and ART 2 neural network for clustering. Then, clusters are filtered and interpreted according to their statistical properties as ERP components or noise. The suggested method can be used to filter the EEG/ERP signal. Furthermore, its results lead to a method that improves averaging when compared to traditional approaches.