20
S.-P. Kim
EOG recorded simultaneously with EEG offers an opportunity to readily remove
ocular artifacts from EEG as it helps identify true profiles of artifacts. Once knowing
the waveforms of ocular artifacts, removal algorithms can be developed to subtract
them from the EEG signal without a need to reject contaminated EEG segments.
To measure EOG for ocular artifact removal, it is recommended to record vertical
(vEOG), horizontal (hEOG) and radial (rEOG) oculomotor signals [88].
Muscle artifacts include electric activities originating from muscle contraction of
the body parts, including face, head, neck, limbs and others. Compared to ocular
artifacts, muscle artifacts generate more various forms depending of the sources
of muscles and related movements. The electrical signals associated with muscle
artifacts can be measured by electromyogram (EMG). However, widespread sources
of muscle artifacts over the body make it challenging to identify true profiles of
artifacts. In addition, the spectral properties of cranial muscle artifacts vary across
sources, corrupting high-frequency EEG components as well as low-frequency ones
[93, 105]. The spatial distribution of muscle artifacts is wider than ocular artifacts,
almost uniform over the entire scalp [44]. Temporal patterns of muscle artifacts are
often associated with tasks as movements of subjects naturally occur in response
to task requirements [95]. Considering all these issues, it still remains a significant
challenge to remove muscle artifacts from EEG [76, 77, 95].
Cardiac artifacts originate from electric activities of the heart. Cardiac artifacts
generally show low amplitudes compared to other artifacts. Cardiac electric activity can be measured by electrocardiography (ECG). They have well-known regular
characteristics, which resemble epileptic EEG activity and thus possibly leading to
incorrect seizure diagnosis [30]. However, for the perspective of removal algorithms,
regular cardiac waveforms make it easier to correct in EEG. When an EEG electrode
is positioned over a scalp artery, its contact with the skin can alter periodically due
to recurrent motion of a pulsating vessel, which is likely to rhythmic electric activity
similar to EEG oscillations [68]. But this pulsation effect shows periodicity synchronous with the heart, rendering itself being identified by ECG.
2.3.2 Artifact Removal Methods
Artifact removal methods aim to cancel or correct artifacts in EEG with minimal
distortions in the brain signal. Here we briefly overview the computational methods to
remove artifacts from EEG [52, 104]. Along this path, we avoid describing the details
of mathematical backgrounds underlying each method (e.g. blind source separation
(BSS), regression, linear transformation of multivariate Gaussian, etc.). Overall, an
EEG artifact removal method belongs to one of the two kinds: a group of methods
that corrects a single channel independently or another group that processes the
whole channels all together. The single-channel processing methods employ various
techniques including linear regression, filtering, wavelet transform and empirical
mode decomposition (EMD). The whole-channel processing methods are based on
BSS to estimate a set of hidden sources from an observed mixture of those sources
S.-P. Kim
EOG recorded simultaneously with EEG offers an opportunity to readily remove
ocular artifacts from EEG as it helps identify true profiles of artifacts. Once knowing
the waveforms of ocular artifacts, removal algorithms can be developed to subtract
them from the EEG signal without a need to reject contaminated EEG segments.
To measure EOG for ocular artifact removal, it is recommended to record vertical
(vEOG), horizontal (hEOG) and radial (rEOG) oculomotor signals [88].
Muscle artifacts include electric activities originating from muscle contraction of
the body parts, including face, head, neck, limbs and others. Compared to ocular
artifacts, muscle artifacts generate more various forms depending of the sources
of muscles and related movements. The electrical signals associated with muscle
artifacts can be measured by electromyogram (EMG). However, widespread sources
of muscle artifacts over the body make it challenging to identify true profiles of
artifacts. In addition, the spectral properties of cranial muscle artifacts vary across
sources, corrupting high-frequency EEG components as well as low-frequency ones
[93, 105]. The spatial distribution of muscle artifacts is wider than ocular artifacts,
almost uniform over the entire scalp [44]. Temporal patterns of muscle artifacts are
often associated with tasks as movements of subjects naturally occur in response
to task requirements [95]. Considering all these issues, it still remains a significant
challenge to remove muscle artifacts from EEG [76, 77, 95].
Cardiac artifacts originate from electric activities of the heart. Cardiac artifacts
generally show low amplitudes compared to other artifacts. Cardiac electric activity can be measured by electrocardiography (ECG). They have well-known regular
characteristics, which resemble epileptic EEG activity and thus possibly leading to
incorrect seizure diagnosis [30]. However, for the perspective of removal algorithms,
regular cardiac waveforms make it easier to correct in EEG. When an EEG electrode
is positioned over a scalp artery, its contact with the skin can alter periodically due
to recurrent motion of a pulsating vessel, which is likely to rhythmic electric activity
similar to EEG oscillations [68]. But this pulsation effect shows periodicity synchronous with the heart, rendering itself being identified by ECG.
2.3.2 Artifact Removal Methods
Artifact removal methods aim to cancel or correct artifacts in EEG with minimal
distortions in the brain signal. Here we briefly overview the computational methods to
remove artifacts from EEG [52, 104]. Along this path, we avoid describing the details
of mathematical backgrounds underlying each method (e.g. blind source separation
(BSS), regression, linear transformation of multivariate Gaussian, etc.). Overall, an
EEG artifact removal method belongs to one of the two kinds: a group of methods
that corrects a single channel independently or another group that processes the
whole channels all together. The single-channel processing methods employ various
techniques including linear regression, filtering, wavelet transform and empirical
mode decomposition (EMD). The whole-channel processing methods are based on
BSS to estimate a set of hidden sources from an observed mixture of those sources