2 Preprocessing of EEG
25
channel EEG data to mutually uncorrelated principal components (PCs) that preserve
variance of the EEG data as much as possible. A set of PCs can represent artifacts if
artifacts and brain signals are uncorrelated with each other. PCA also assumes joint
normal distributions of the data. Often, it suffers from its restricted assumption that
sources including brain activities are orthogonal to each other [39]. Hence, PCA is
now seldom used directly for artifact removal but instead used for other essential
preprocessing such as whitening [35].
Canonical correlation analysis (CCA) has also been extensively used for artifact
removal from EEG [29, 43, 118]. Basically, CCA seeks for canonical variables that
maximize correlations between two multivariate datasets. For EEG denoising, CCA
finds canonical variables between the original data and its time-shifted version (typically one step behind). In doing so, canonical variables inferred in sequence represent
the autocorrelation from the highest to the lowest. By assuming that brain activities
are more correlated in time than artifacts, CCA identifies and removes canonical components with lower autocorrelations that may correspond to artifacts. The advantage
of CCA over ICA is that it can take temporal correlations of the signals into account
and use less computational resources [52].
Besides the three BSS methods described above, there are other BSS methods
recently proposed for EEG artifact removal. Morphological component analysis
(MCA) can decompose artifacts from EEG if the morphological template of the
target artifacts is available [96]. Singular spectrum analysis (SSA) is a projective subspace method that projects a single-channel EEG signal onto a higher-dimensional
space by time embedding, decomposes the embedded signal vector into uncorrelated
components and reconstructs the EEG signal by projecting the embedded signals in
the directions with large eigenvalues [24, 25, 101]. The sparse time artifact removal
algorithm identifies and removes artifactual components of EEG that are sparse in
both space and time [27].
2.3.2.5 Hybrid Artifact Removal Methods
Recent studies have proposed hybrid approaches for EEG artifact removal by combining more than one artifact removal algorithms. Many studies blend one algorithm
from the BSS family and the other with decomposition (e.g. wavelet transform or
EMD). A hybrid method can be characterized by the order of the applications of the
selected algorithms. One group of methods first decomposes an EEG signal and then
applies a BSS algorithm later whereas a different group of methods first estimates
components using a BSS algorithm followed by a decomposition algorithm. The
former usually corrects a single-channel EEG signal whereas the latter processes
multi-channel EEG signals (Fig. 2.3). The hybrid approaches are generally designed
to overcome the limitations of a single artifact removal approach and thus exhibit
better performance, but require more careful choices of algorithms that fit adequately
to the data and/or system requirements (e.g. computational complexity). The examples of the first group of hybrid methods for artifact removal, decomposition-BSS for
single channels, can be found in various forms, applying wavelet transform followed
25
channel EEG data to mutually uncorrelated principal components (PCs) that preserve
variance of the EEG data as much as possible. A set of PCs can represent artifacts if
artifacts and brain signals are uncorrelated with each other. PCA also assumes joint
normal distributions of the data. Often, it suffers from its restricted assumption that
sources including brain activities are orthogonal to each other [39]. Hence, PCA is
now seldom used directly for artifact removal but instead used for other essential
preprocessing such as whitening [35].
Canonical correlation analysis (CCA) has also been extensively used for artifact
removal from EEG [29, 43, 118]. Basically, CCA seeks for canonical variables that
maximize correlations between two multivariate datasets. For EEG denoising, CCA
finds canonical variables between the original data and its time-shifted version (typically one step behind). In doing so, canonical variables inferred in sequence represent
the autocorrelation from the highest to the lowest. By assuming that brain activities
are more correlated in time than artifacts, CCA identifies and removes canonical components with lower autocorrelations that may correspond to artifacts. The advantage
of CCA over ICA is that it can take temporal correlations of the signals into account
and use less computational resources [52].
Besides the three BSS methods described above, there are other BSS methods
recently proposed for EEG artifact removal. Morphological component analysis
(MCA) can decompose artifacts from EEG if the morphological template of the
target artifacts is available [96]. Singular spectrum analysis (SSA) is a projective subspace method that projects a single-channel EEG signal onto a higher-dimensional
space by time embedding, decomposes the embedded signal vector into uncorrelated
components and reconstructs the EEG signal by projecting the embedded signals in
the directions with large eigenvalues [24, 25, 101]. The sparse time artifact removal
algorithm identifies and removes artifactual components of EEG that are sparse in
both space and time [27].
2.3.2.5 Hybrid Artifact Removal Methods
Recent studies have proposed hybrid approaches for EEG artifact removal by combining more than one artifact removal algorithms. Many studies blend one algorithm
from the BSS family and the other with decomposition (e.g. wavelet transform or
EMD). A hybrid method can be characterized by the order of the applications of the
selected algorithms. One group of methods first decomposes an EEG signal and then
applies a BSS algorithm later whereas a different group of methods first estimates
components using a BSS algorithm followed by a decomposition algorithm. The
former usually corrects a single-channel EEG signal whereas the latter processes
multi-channel EEG signals (Fig. 2.3). The hybrid approaches are generally designed
to overcome the limitations of a single artifact removal approach and thus exhibit
better performance, but require more careful choices of algorithms that fit adequately
to the data and/or system requirements (e.g. computational complexity). The examples of the first group of hybrid methods for artifact removal, decomposition-BSS for
single channels, can be found in various forms, applying wavelet transform followed