16
S.-P. Kim
computer interface (BCI) could be dependent on how EEG preprocessing treated the
recorded EEG signals. In fact, it is obvious that any analytic result from the EEG
signals containing significant noise and artifacts is likely to draw misleading conclusions. Recent reports also emphasize the standardization of preprocessing routines
for multi-site data collection in divergent experimental environments [8, 37].
At the center of EEG processing lies the removal of any unnecessary covert and
overt components of the EEG signals. In this chapter, we denote such unnecessary components as noise and artifacts. Following the previous notion [65], noise is
regarded as neurological activities irrelevant to an examined behavioral task whereas
artifacts are regarded to originate from external sources unrelated to neurological
activities, such as eye movements, respiration or electrical interference. As most
EEG preprocessing techniques pay attention to removing artefacts, we will also narrow our focus on the methods used to eliminate artifacts to clean up the EEG signals.
Note that the topics covered by this chapter do not include the extraction of features from the EEG signals for particular applications, which should be discussed
separately.
This chapter begins with the description of early-stage procedures to remove
basic artifacts, sort out contaminated channels and possibly adjust references. It then
discusses a range of methods to remove artifacts from the EEG signals, followed by
brief discussion on EEG preprocessing.
2.2 Early-Stage Preprocessing
Early-stage EEG preprocessing involves fundamental and semi-automated organization of signal processing functions. It is distinguished from common artifact
removal procedures as this stage of preprocessing is largely independent of any specific artifact. This chapter describes key parts of early-stage preprocessing including
the removal of line noise, referencing and the elimination of bad channels. Before
describing them, however, it is worth reviewing background characteristics of the
EEG signals.
2.2.1 Characteristics of Background EEG
A basic and brief summary of the characteristics of background EEG activity is given
as follows [104]. The frequency range of EEG is reportedly limited approximately
from 0.01 to 100 Hz. The amplitudes of EEG generated from the brain typically range
within ±100 µV. The power spectral density of EEG is known to follow the power
law [44]. Background brain rhythms are present in EEG, generally being classified
in terms of oscillatory frequency into five disjoint bands: delta (0.5–4 Hz), theta
(4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz) and gamma (30–100 Hz). More details
S.-P. Kim
computer interface (BCI) could be dependent on how EEG preprocessing treated the
recorded EEG signals. In fact, it is obvious that any analytic result from the EEG
signals containing significant noise and artifacts is likely to draw misleading conclusions. Recent reports also emphasize the standardization of preprocessing routines
for multi-site data collection in divergent experimental environments [8, 37].
At the center of EEG processing lies the removal of any unnecessary covert and
overt components of the EEG signals. In this chapter, we denote such unnecessary components as noise and artifacts. Following the previous notion [65], noise is
regarded as neurological activities irrelevant to an examined behavioral task whereas
artifacts are regarded to originate from external sources unrelated to neurological
activities, such as eye movements, respiration or electrical interference. As most
EEG preprocessing techniques pay attention to removing artefacts, we will also narrow our focus on the methods used to eliminate artifacts to clean up the EEG signals.
Note that the topics covered by this chapter do not include the extraction of features from the EEG signals for particular applications, which should be discussed
separately.
This chapter begins with the description of early-stage procedures to remove
basic artifacts, sort out contaminated channels and possibly adjust references. It then
discusses a range of methods to remove artifacts from the EEG signals, followed by
brief discussion on EEG preprocessing.
2.2 Early-Stage Preprocessing
Early-stage EEG preprocessing involves fundamental and semi-automated organization of signal processing functions. It is distinguished from common artifact
removal procedures as this stage of preprocessing is largely independent of any specific artifact. This chapter describes key parts of early-stage preprocessing including
the removal of line noise, referencing and the elimination of bad channels. Before
describing them, however, it is worth reviewing background characteristics of the
EEG signals.
2.2.1 Characteristics of Background EEG
A basic and brief summary of the characteristics of background EEG activity is given
as follows [104]. The frequency range of EEG is reportedly limited approximately
from 0.01 to 100 Hz. The amplitudes of EEG generated from the brain typically range
within ±100 µV. The power spectral density of EEG is known to follow the power
law [44]. Background brain rhythms are present in EEG, generally being classified
in terms of oscillatory frequency into five disjoint bands: delta (0.5–4 Hz), theta
(4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz) and gamma (30–100 Hz). More details