2 Preprocessing of EEG
27
cult to assess how much a noisy EEG signal become purified by an artifact removal
algorithm [52]. One way to address this issue is to synthesize simulated signals
mixed with putative true EEG signals and artifacts and evaluate an algorithm with
the simulated signals [60, 64, 92]. Others have suggested using a well-known EEG
waveform evoked by an established cognitive task to test artifact removal methods
[104]. For example, an audio-visual task evoking the auditory N100 event-related
potential may provide a validation dataset with which researchers can evaluate different artifact removal methods by assessing N100 waveforms after eliminating artifacts
by different methods (see [88] for more details).
Besides performance evaluation discussed above, there are other issues to address
for the development of an EEG artifact removal method. First, many recent EEG
applications demand online preprocessing of artifacts [26, 43, 86]. Such online preprocessing is capable of detecting and removing artifacts even for non-stationary
environments so that it can adaptively update the parameters of algorithms by tracking environmental changes. As such, the requirement of online processing sometimes
weakens the advantages of certain algorithms that rely on the estimation of model
parameters using a chunk of the training data (e.g. ICA or EMD). Also, computationally expensive machine learning algorithms (e.g. those with deep learning algorithms)
may need further justification to be used for online processing. Yet, in the course
of the development of a new artifact removal algorithm, it would be more effective
to consider online implementation if possible. A fully automated artifact removal
algorithm will underpin online implementation [26, 84]. Second, the availability of
reference channels should be taken into consideration for artifact removal. If no reference channel is available, we need to use prior knowledge about artifacts or infer
artifacts directly from EEG data [62, 72, 86]. Generally, using an explicit reference
channel may help customizing algorithms for each individual, yielding a more precise preprocessing method. Depending on the types of artifacts, it may be useful for
improving EEG preprocessing to utilize reference channels, often acquired with a
separate device, such as: EOG channel [22, 23, 61], ECG channel [30], eye tracker
[85], accelerometer [24, 25], and contact impedance [119]. Third, it would be crucial
to match the properties of an algorithm with statistical and physiological characteristics of the artifacts to remove. The readers may refer to Urigüen et al. [104] for the
suggestions of artifact removal algorithms suitable for different types of artifacts.
Fourth, researchers often opt to utilize public software tools for EEG preprocessing
as well as other EEG data analyses (see [52] for the list of available software tools).
Even though a number of software tools offer complete preprocessing routines and
user interfaces for EEG studies, it is recommended to intensively explore the theoretical backgrounds and technical details of a tool being used. Otherwise, it is difficult
to understand how EEG signals are processed at each preprocessing step. Fifth, it is
helpful to inform study participants about the problems of artifacts in EEG recordings such that participants can minimize their movements during the main tasks
[89]. Although it would be also problematic if participants pay too much attention
to movement restriction throughout the whole experiment, a short training phase for
participants to minimize movements during the task periods interleaved with more
flexible breaks can help acquiring high-quality EEG data at the stage of recording.
27
cult to assess how much a noisy EEG signal become purified by an artifact removal
algorithm [52]. One way to address this issue is to synthesize simulated signals
mixed with putative true EEG signals and artifacts and evaluate an algorithm with
the simulated signals [60, 64, 92]. Others have suggested using a well-known EEG
waveform evoked by an established cognitive task to test artifact removal methods
[104]. For example, an audio-visual task evoking the auditory N100 event-related
potential may provide a validation dataset with which researchers can evaluate different artifact removal methods by assessing N100 waveforms after eliminating artifacts
by different methods (see [88] for more details).
Besides performance evaluation discussed above, there are other issues to address
for the development of an EEG artifact removal method. First, many recent EEG
applications demand online preprocessing of artifacts [26, 43, 86]. Such online preprocessing is capable of detecting and removing artifacts even for non-stationary
environments so that it can adaptively update the parameters of algorithms by tracking environmental changes. As such, the requirement of online processing sometimes
weakens the advantages of certain algorithms that rely on the estimation of model
parameters using a chunk of the training data (e.g. ICA or EMD). Also, computationally expensive machine learning algorithms (e.g. those with deep learning algorithms)
may need further justification to be used for online processing. Yet, in the course
of the development of a new artifact removal algorithm, it would be more effective
to consider online implementation if possible. A fully automated artifact removal
algorithm will underpin online implementation [26, 84]. Second, the availability of
reference channels should be taken into consideration for artifact removal. If no reference channel is available, we need to use prior knowledge about artifacts or infer
artifacts directly from EEG data [62, 72, 86]. Generally, using an explicit reference
channel may help customizing algorithms for each individual, yielding a more precise preprocessing method. Depending on the types of artifacts, it may be useful for
improving EEG preprocessing to utilize reference channels, often acquired with a
separate device, such as: EOG channel [22, 23, 61], ECG channel [30], eye tracker
[85], accelerometer [24, 25], and contact impedance [119]. Third, it would be crucial
to match the properties of an algorithm with statistical and physiological characteristics of the artifacts to remove. The readers may refer to Urigüen et al. [104] for the
suggestions of artifact removal algorithms suitable for different types of artifacts.
Fourth, researchers often opt to utilize public software tools for EEG preprocessing
as well as other EEG data analyses (see [52] for the list of available software tools).
Even though a number of software tools offer complete preprocessing routines and
user interfaces for EEG studies, it is recommended to intensively explore the theoretical backgrounds and technical details of a tool being used. Otherwise, it is difficult
to understand how EEG signals are processed at each preprocessing step. Fifth, it is
helpful to inform study participants about the problems of artifacts in EEG recordings such that participants can minimize their movements during the main tasks
[89]. Although it would be also problematic if participants pay too much attention
to movement restriction throughout the whole experiment, a short training phase for
participants to minimize movements during the task periods interleaved with more
flexible breaks can help acquiring high-quality EEG data at the stage of recording.