2 Preprocessing of EEG
17
of the implications and functions of these rhythms can be found in other resources
(e.g. see [10, 41, 63, 98]).
It is reasonable to consider the EEG signal as stochastic due to the lack of genuine
EEG measurements [93]. In addition, over a long-term period, the EEG signals should
be viewed as a non-stationary time series [57, 66]. However, EEG within a short
time window can be approximately stationary with static statistical properties. The
length of such a window containing stationary EEG signals varies with environments,
generally ranging from several seconds to minutes [51].
2.2.2 Line Noise Removal
Most efforts to eliminate line noise from the EEG signal rely on notch filtering at
60 Hz. A notch filter is typically implemented with a certain frequency width surrounding 60 Hz (e.g. a width of 10 Hz). Consequently, notch filtering, although
successfully removing line noise, could cause unintended distortions in signal components oscillating between 50 and 70 Hz. Also, the notch filter can reportedly
generate a transient oscillation in baseline activity, leading to a potential issue in
data interpretation [18]. Follow-up low-pass filtering with a cutoff frequency lower
than 50 Hz may remedy this problem, but instead give rise to other issues such as
alteration of temporal structures of EEG [106] or spurious interactions between EEG
channels [40].
One suggestion to overcome this problem is estimating line noise embedded in
the recorded EEG signals as precise as possible and subtracting it from the data [8,
80]. This method employs multi-taper decomposition to find line noise components
in the signal. A short-time window slides over the course of the signal in which the
transformation of EEG time series based on multi-tapers is carried out [5]. This transformation can effectively estimate spectral energy within each frequency band. Then,
a regression model is applied to estimate the amplitude and phase of sinusoidal line
noise (e.g. sinusoids at 60 Hz) in the transformed frequency domain. The Thompson
F-test evaluates a significance of the magnitude of the estimated line noise. A time
series of sinusoidal line noise is reconstructed if the magnitude is significant. This
process is repeated over the sliding windows. The reconstructed line noise signal
is subtracted from the original EEG signal. The entire process is repeated until the
magnitude at the frequency of line noise becomes non-significant (Fig. 2.1). In this
way, line noise components can be removed without damaging background spectral
components [83].
2.2.3 Referencing
We often subtract a reference (with the same time resolution as the recorded EEG
signals) from the original EEG signal at each channel. The reference signal should
17
of the implications and functions of these rhythms can be found in other resources
(e.g. see [10, 41, 63, 98]).
It is reasonable to consider the EEG signal as stochastic due to the lack of genuine
EEG measurements [93]. In addition, over a long-term period, the EEG signals should
be viewed as a non-stationary time series [57, 66]. However, EEG within a short
time window can be approximately stationary with static statistical properties. The
length of such a window containing stationary EEG signals varies with environments,
generally ranging from several seconds to minutes [51].
2.2.2 Line Noise Removal
Most efforts to eliminate line noise from the EEG signal rely on notch filtering at
60 Hz. A notch filter is typically implemented with a certain frequency width surrounding 60 Hz (e.g. a width of 10 Hz). Consequently, notch filtering, although
successfully removing line noise, could cause unintended distortions in signal components oscillating between 50 and 70 Hz. Also, the notch filter can reportedly
generate a transient oscillation in baseline activity, leading to a potential issue in
data interpretation [18]. Follow-up low-pass filtering with a cutoff frequency lower
than 50 Hz may remedy this problem, but instead give rise to other issues such as
alteration of temporal structures of EEG [106] or spurious interactions between EEG
channels [40].
One suggestion to overcome this problem is estimating line noise embedded in
the recorded EEG signals as precise as possible and subtracting it from the data [8,
80]. This method employs multi-taper decomposition to find line noise components
in the signal. A short-time window slides over the course of the signal in which the
transformation of EEG time series based on multi-tapers is carried out [5]. This transformation can effectively estimate spectral energy within each frequency band. Then,
a regression model is applied to estimate the amplitude and phase of sinusoidal line
noise (e.g. sinusoids at 60 Hz) in the transformed frequency domain. The Thompson
F-test evaluates a significance of the magnitude of the estimated line noise. A time
series of sinusoidal line noise is reconstructed if the magnitude is significant. This
process is repeated over the sliding windows. The reconstructed line noise signal
is subtracted from the original EEG signal. The entire process is repeated until the
magnitude at the frequency of line noise becomes non-significant (Fig. 2.1). In this
way, line noise components can be removed without damaging background spectral
components [83].
2.2.3 Referencing
We often subtract a reference (with the same time resolution as the recorded EEG
signals) from the original EEG signal at each channel. The reference signal should