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Table of Contents
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1) Introduction
Artifacts are undesirable potentials that contaminate brain signals and are mostly of non-cerebral origin. Unfortunately, they can modify the shape of a neurological phenomenon that drives a BCI system. They can also mistakenly result in an unintentional control of the device [VAU03]. Therefore, there is a need to avoid, reject or remove artifacts from the recordings of brain signals.
In a self-paced BCI (SBCI) system, artifacts can impact the performance of the system in two ways: 1) by changing the shape of the neurological phenomenon during an IC period, they cause a decrease in the TP rate. 2) By mimicking the shape/properties of the neurological phenomenon during the NC periods, artifacts results in an increase in the FP rates.
Figure 1 .An example of how artifacts can affect the performance of an SBCI system. (a) The brain state of the user. (b) The periods when artifacts have occurred. (c) The output of the SBCI system (note: FP: false positive, TN: true negative, FN: false negative and TP: true positive).
Figure 1 shows how this can happen. Figure 1(a) shows the brain states of a user during a specific time frame. As seen, the user is in an NC state, however, at two time instants the user initiates an IC command. Figure 1(b) shows the periods of EEG signals that are contaminated with artifacts. The term “ART” denotes “artifact-contaminated periods” and “NO” refers to the periods not contaminated with artifacts. The second period coincides with the first IC command. Figure 1(c) shows the output of the SBCI system. The occurrence of the first artifact results in a false positive. The second artifact, results in masking the first IC command (a false negative or FN).
Artifacts originate from non-physiological as well as physiological sources. Non-physiological artifacts originate from outside the human body (such as 50/60 Hz power-line noise or changes in electrode impedances), and are usually avoided by proper filtering, shielding, etc.
Physiological artifacts arise from a variety of bodily activities. Electrocardiography (ECG) artifacts are caused by heart beats and may introduce a rhythmic activity into the EEG signal. Respiration can also cause artifacts by introducing a rhythmic activity that is synchronized with the body’s respiratory movements. Skin responses such as sweating may alter the impedance of electrodes and cause artifacts in the EEG signals [BAR03]. The two physiological artifacts that have been most examined in BCI studies, however, are ocular (Electrooculography or EOG) and muscle (Electromyography or EMG) artifacts.
EOG artifacts are generally high-amplitude patterns in the brain signal caused by blinking of the eyes, or low-frequency patterns caused by movements (such as rolling) of the eyes [AND99]. EOG activity has a wide frequency range, being maximal at frequencies below 4Hz, and is most prominent over the anterior head regions [MCF97].
EMG activity (movement of the head, body, jaw or tongue) can cause large disturbances in the brain signal. EMG activity has a wide frequency range, being maximal at frequencies higher than 30 Hz [AND99, MCF97]. Difficult tasks may cause an increase in EMG activity related to the movement of facial muscles [WAT94, COH92].
Some studies have shown that EOG and EMG activities may generate artifacts that affect the neurological phenomena used in a BCI system [GON03, ¸MCF05]. For example, [MCD05] demonstrated that brain rhythms are contaminated with EMG artifacts during the early training sessions of their proposed BCI system that used Mu and Beta rhythms as sources of control.
Physiological artifacts such as EOG and EMG artifacts are much more challenging to handle than non-physiological ones. Moreover, controlling them during the signal acquisition stage is not easy. There are different ways of handling artifacts in BCI systems. In the next Section, we briefly examine the reported methods for handling EOG and EMG artifacts, as these are among the most important sources of contamination in BCI systems.
2) Methods of Handling Artifacts
Methods of handling artifacts are as follows:
2.1) Artifact Avoidance
The first step in handling artifacts is to avoid their occurrence by issuing proper instructions to users. For example, users are instructed to avoid blinking or moving their bodies during the experiments.
Instructing individuals to avoid generating artifacts during data collection has the advantage of being the least computationally demanding among the artifact handling methods, since it is assumed that no artifact is present in the signal (or that the presence of artifacts is minimal). However, it has several drawbacks. First, since many physiological signals, such as the heart beats, are involuntary, artifacts will always be present in brain signals. Even in the case of EOG and EMG activities, it is not easy to control eye and other movement activities during the process of data recording. Second, the occurrence of ocular and muscle activity during an online operation of any BCI system is not avoidable. Third, collecting sufficient amount of data without artifacts may be difficult, especially in cases where a user has a neurological disability [VIG97]. Finally, avoiding artifacts may introduce an additional cognitive task for the individual. For example, it has been shown that refraining from eye blinking results in changes in the amplitude of some evoked potentials [VER91, OCH00].
2.2) Artifact Rejection
Artifact rejection refers to the process of rejecting the trials affected by artifacts. It is perhaps the simplest way of dealing with brain signals contaminated with artifacts. It has some important advantages over the “artifact avoidance” approach. For example, it would be easier for individuals to participate in the experiments and perform the required tasks, especially those individuals with motor disabilities. Also, the “secondary” cognitive task, resulting from an individual trying to avoid generating a particular artifact, will not be present in the EEG signal.
”Artifact rejection” is usually done by visually inspecting the EEG or the artifact signals, or by using an automatic detection method [GRA98].
2.2.1) Manual Rejection
Manual rejection of epochs contaminated with artifacts is a common practice in the BCI field. Trials are visually checked by an expert, and those that are contaminated with artifacts are removed from the analysis.
Similar to “artifact avoidance”, manual rejection also has the advantage of not being computationally demanding, as it is assumed that a human expert has identified all the artifact-contaminated epochs and removed them from the analysis. On the other hand, there are many disadvantages in using “manual rejection”. First, “manual rejection” comes at the cost of intensive human labor, especially if the study involves a large number of individuals or a large amount of recorded data. Second, the process of selecting the artifact-free trials may become subjective. It has been argued that because of the selection bias, the sample trials that are artifact-free may not be representative of the entire population of the trials [GRA98]. Third, in the case of offline analysis, the rejection of artifact-contaminated trials, may lead to a substantial loss of data. This may become a huge drawback, especially in the case of individuals with motor disabilities, where offline data recording is not as convenient as it is for able-bodied individuals.
2.2.2) Automatic Rejection
In the “automatic rejection”, the BCI system automatically discards the epochs of brain signals that are contaminated with particular artifacts. This procedure is commonly carried out in offline investigations.
Automatic rejection of epochs can be done in the following two ways:
2.2.2.1) Rejection using the EOG (EMG) signal
When one of the characteristics of the EOG (EMG) signal in an epoch exceeds a pre-determined threshold, the epoch is considered as artifact-contaminated and is automatically rejected.
2.2.2.2)Rejection using the EEG signal
This rejection methodology is similar to the above; only the EEG signal is used instead of the EOG (EMG) signal. This approach has the advantage of being independent of the EOG (EMG) signal, and is useful if the EOG (EMG) signal is not recorded during data collection.
An advantage of the “automatic rejection” approach over that of “manual rejection” is that it is less labor intensive. However, automatic rejection still suffers from loss of valuable data [RAM00, MIL02]. In the case of EOG artifacts, the automatic rejection approach also does not allow the rejection of contaminated trials when the EOG amplitude is small [CRO00, ROW68].
Two issues need to be addressed for the BCI systems which reject artifacts:
Because of the vast number of artifacts that exist in BCI systems (eye blinking, eye movements, movements of different parts of the body, breathing, etc.), not all the artifact-contaminated trials can be rejected. Usually only the epochs with a strong presence of artifacts are excluded from the analysis. Therefore, the so-called “clean” data are unfortunately not completely free of artifacts.
The second issue is that the rejection of artifact-contaminated data during an offline analysis may generate “cleaner” data. However, for online real-time applications of a BCI system, this may pose a huge drawback. In online applications, artifacts are unavoidable. If artifacts are rejected during the offline analysis, the same rejection mechanism can be used to reject them during the online analysis. The only problem is that during the specific time periods when artifact-contaminated signals are rejected, the system is unreachable and cannot be used for controlling the device.
2.3) Artifact Removal
Artifact removal is the process of identifying and removing artifacts from brain signals. An artifact-removal method should be able to remove the artifacts as well as keep the related neurological phenomenon intact. Common methods for removing the artifacts in EEG signals are linear filtering [BAR84, IVE88], linear combination and regression [CRO00], blind source separation [CHO05], principle component analysis [LAG97], wavelet transform [BRO02] , nonlinear adaptive filtering [HE04]and source dipole analysis (SDA) [BER94].
3) State of the Art
A survey of all BCI studies published before January 2006 shows that most BCI papers do not report whether or not they have considered EMG and/or EOG artifacts in their analysis [FAT07]. This is an important issue, since offline analysis methods that do not account for physiological artifacts may probably face some problems when tested during an online study. As a result, it is important that BCI researchers pay more attention to this important issue and address the method that they have employed for handling artifacts.
A number of BCI studies state that EMG activity will not be present in the EEG signal when the EEG signal is analyzed before a movement has occurred [BUR02]. This argument may not be valid for BCI systems. This is because peripheral changes such as EMG tension can affect the EEG signal, even though the amount by which the EEG signal is affected remains unclear [KUE98]. It is pointed out in [KUE98] that even when the individuals are very restricted, they still preserve motor control over some muscle groups. Although the activities of several muscle groups are monitored in BCI studies, there remain some muscles whose activities are not recorded.
The BCI systems that employ “manual rejection” of EOG and EMG artifacts should also consider the fact that “manual rejection” is only a preliminary step in the design of a BCI system. “Manual rejection” can only be used for offline analysis. In order for a particular BCI system to work in an online fashion, a scheme for handling artifacts should be incorporated. Requesting the individuals to avoid artifacts should be only considered as a temporary solution. In a practical application, EMG and EOG artifacts do happen, so methods of handling these artifacts during an online experiment should be investigated.
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