2 Preprocessing of EEG
19
low-frequency components and detect a bad channel showing a ratio higher than a
threshold.
Once being detected, bad channels are replaced with virtual healthy channels
created by the interpolation from neighboring channels, in order to reconstruct the
global brain responses [8, 31]. There exist a number of interpolation schemes useful
for channel reconstruction, including spherical splines [87], higher-order polynomials [4], nearest-neighbor averaging [15] and radial basis function [53]. Using spherical splines allows accurate estimation of scalp potentials if the electrode mapping
is sufficiently dense [38, 97]. Interpolation using a statistical method such as radial
basis functions has advantages of cost-effectiveness with less computational loads.
2.3 Artifact Removal
In this section, we briefly review the potential sources of artifacts mixed in the EEG
signal and the techniques to remove or reduce artifacts. We primarily deal with artifact
removal techniques, forgoing other steps of artifact management such as artifact
detection. However, it does not mean that other methods including artifact detection
or artifact avoidance are less crucial than artifact removal. In fact, artifact removal
is often accompanied by artifact detection for efficient processing of artifacts. There
have been a number of methods for artifact detection that the interested readers can
refer to [3, 14, 32, 52, 81, 84].
2.3.1 Sources of Artifacts
The sources of EEG artifacts can be categorized into two classes: internal and external
sources. The internal sources originate from the physiological systems of self and
include electromagnetic activities of heart, eyes, muscle and so on. The external
sources include all other possible signals from environments that can contaminate
EEG such as wireless telecommunication signals, electrode attachment, recording
equipment and cable movements [93]. Recently. the handling of external artifacts
has become more important as EEG applications tend to move out of laboratories
toward in-home healthcare systems [100]. Yet, the external sources, owing to their
origins, can be inhibited once being identified. On the other hand, the internal artifacts
physiologically permeate EEG, making it difficult to prevent them from occurring
in advance. Therefore, most artifact removal methods have been focused on dealing
with the internal artifacts and here we also pay our attention to the most pronounced
internal artifacts that have been handled by EEG artifact removal methods.
Ocular artifacts include electric activities generated by eye movements or eye
blinking [22, 23]. Interference by ocular artifacts is strong enough to be visible
in EEG waveforms. EEG channels proximal to eyes are more vulnerable to ocular
artifacts. Ocular artifacts can be detected by electrooculogram (EOG) measurements.
19
low-frequency components and detect a bad channel showing a ratio higher than a
threshold.
Once being detected, bad channels are replaced with virtual healthy channels
created by the interpolation from neighboring channels, in order to reconstruct the
global brain responses [8, 31]. There exist a number of interpolation schemes useful
for channel reconstruction, including spherical splines [87], higher-order polynomials [4], nearest-neighbor averaging [15] and radial basis function [53]. Using spherical splines allows accurate estimation of scalp potentials if the electrode mapping
is sufficiently dense [38, 97]. Interpolation using a statistical method such as radial
basis functions has advantages of cost-effectiveness with less computational loads.
2.3 Artifact Removal
In this section, we briefly review the potential sources of artifacts mixed in the EEG
signal and the techniques to remove or reduce artifacts. We primarily deal with artifact
removal techniques, forgoing other steps of artifact management such as artifact
detection. However, it does not mean that other methods including artifact detection
or artifact avoidance are less crucial than artifact removal. In fact, artifact removal
is often accompanied by artifact detection for efficient processing of artifacts. There
have been a number of methods for artifact detection that the interested readers can
refer to [3, 14, 32, 52, 81, 84].
2.3.1 Sources of Artifacts
The sources of EEG artifacts can be categorized into two classes: internal and external
sources. The internal sources originate from the physiological systems of self and
include electromagnetic activities of heart, eyes, muscle and so on. The external
sources include all other possible signals from environments that can contaminate
EEG such as wireless telecommunication signals, electrode attachment, recording
equipment and cable movements [93]. Recently. the handling of external artifacts
has become more important as EEG applications tend to move out of laboratories
toward in-home healthcare systems [100]. Yet, the external sources, owing to their
origins, can be inhibited once being identified. On the other hand, the internal artifacts
physiologically permeate EEG, making it difficult to prevent them from occurring
in advance. Therefore, most artifact removal methods have been focused on dealing
with the internal artifacts and here we also pay our attention to the most pronounced
internal artifacts that have been handled by EEG artifact removal methods.
Ocular artifacts include electric activities generated by eye movements or eye
blinking [22, 23]. Interference by ocular artifacts is strong enough to be visible
in EEG waveforms. EEG channels proximal to eyes are more vulnerable to ocular
artifacts. Ocular artifacts can be detected by electrooculogram (EOG) measurements.