A periodic spatio-spectral filter for event-related potentials
In Computers in Biology and Medicine - An International Journal, Elsevier, volume 79, number DOI: 10.1016/j.compbiomed.2016.10.004, pages 286-298, Dec/2016.
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.
Electroencephalogram, event-related potential (ERP) detection, brain-computer interface (BCI), spatial filters, spatio-spectral filters.