Spatial Filtering of Multichannel Electroencephalographic Recordings Through Principal Component Analysis by Singular Value Decomposition

Abstract

Principal component analysis (PCA) by singular value decomposition (SVD) may be used to analyze an epoch of a multichannel electroencephalogram (EEG) into multiple linearly independent (temporally and spatially noncorrelated) components, or features; the original epoch of the EEG may be reconstructed as a linear combination of the components. The result of SVD includes the components, expressible as time series waveforms, and the factors that determine how much each component waveform contributes to each EEG channel. By omission of some component waveforms from the linear combination, a new EEG can be reconstructed, differing from the original in useful ways. For example, artifacts can be removed and features such as ictal or interictal discharges can be enhanced by suppressing the remainder of the EEG. We developed a variation of this technique in which the factors that reconstruct the modified EEG from the original are stored as a matrix. This matrix is applied to multichannel EEG at successive times to create a new EEG continuously in real time, without redoing the time-consuming SVD. This matrix acts as a spatial filter with useful properties. We successfully applied this method to remove artifacts, including ocular movement and electrocardiographic artifacts. Removal of myogenic artifacts was much less complete, but there was significant improvement in the ability to visualize underlying activity in the presence of myogenic artifacts. The major limitations of the method are its inability to completely separate some artifacts from cerebral activity, especially when both have similar amplitudes, and the possibility that a spatial filter may distort the distribution of activities that overlap with the artifacts being removed.

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“…Principal component analysis (PCA) was applied to the EEG signals and only components with a signal-to-noise ratio greater than 1.0 were retained (Lagerlund et al, 1997). A spherical volume conductor head model was adopted, which consisted of three compartments representing the brain, skull, and scalp (Niedermeyer and da Silva, 1999).…”

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“…However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes (Lagerlund et al, 1997;Jung et al, 1998bJung et al, , 2000. By combining PCA, multiple source models for EOG and EEG, and an artifact-aligned averaging method (Lins et al, 1993), Berg and Scherg (1994) demonstrated a more effective PCA-based approach to correct eye artifacts.…”