Publication

A periodic spatio-spectral filter for event-related potentials

Foad Ghaderi, Su-Kyoung Kim, Elsa Andrea Kirchner

In: Computers in Biology and Medicine - An International Journal 79 DOI: 10.1016/j.compbiomed.2016.10.004 Pages 286-298 Elsevier 12/2016.

Abstract

With respect to single trial detection of event-related potentials (ERPs), spatial and spectral filters are two of the most commonly used pre-processing techniques for signal enhancement. Spatial filters reduce the dimensionality of the data while suppressing the noise contribution and spectral filters attenuate frequency components that most likely belong to noise subspace. However, the frequency spectrum of ERPs overlap with that of the ongoing electroencephalogram (EEG) and different types of artifacts. Therefore, proper selection of the spectral filter cutoffs is not a trivial task. In this research work, we developed a supervised method to estimate the spatial and finite impulse response (FIR) spectral filters, simultaneously. We evaluated the performance of the method on offline single trial classification of ERPs in datasets recorded during an oddball paradigm. The proposed spatiospectral filter improved the overall single-trial classification performance by almost 9% on average compared with the case that no spatial filters were used. We also analyzed the effects of different spectral filter lengths and the number of retained channels after spatial filtering.

Projekte

Weitere Links

20161004_A_periodic_spatio-spectral_filter_for_event-related_potentials.pdf (pdf, 3 MB)

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz