An effective approach is proposed in this paper to remove ocular artifacts from the raw EEG recording. The proposed approach first conducts the blind source separation on the raw EEG recording by the stationary subspace analysis (SSA) algorithm. Unlike the classic blind source separation algorithms, SSA is explicitly tailored to the understanding of distribution changes, where both the mean and the covariance matrix are taken into account. In addition, neither independency nor uncorrelation is required among the sources by SSA. Thereby, it can concentrate artifacts in fewer components than the representative blind source separation methods. Next, the components that are determined to be related to the ocular artifacts are projected back to be subtracted from EEG signals, producing the clean EEG data eventually. The experimental results on both the artificially contaminated EEG data and real EEG data have demonstrated the effectiveness of the proposed method, in particular for the cases where limited number of electrodes are used for the recording, as well as when the artifact contaminated signal is highly nonstationary and the underlying sources cannot be assumed to be independent or uncorrelated.
The electroencephalographic (EEG) provides a noninvasive facility to investigate the intricacy of human brain. It has been applied in numerous applications such as brain-computer interface and clinical diagnosis of neurological disorders [
In the literature, the most common EOG artifacts removal method is based on the blind source separation (BSS), usually by independent component analysis (ICA) [
However, the classic BSS techniques such as ICA and SOBI may not be effective on the highly nonstationary EOG artifact-contaminated EEG recordings. On one hand, the ocular artifacts resulting from eye movement and blink demonstrate strongly nonstationary characteristics within a considerably long interval: it is often localized with abruptly large amplitude and low frequency; its duration and amplitude appear to differ stochastically and considerably between successive eye movements or blinks. This implies that there are significant distribution changes in the artifact-contaminated EEG observations, such as the changes in the mean and the covariance matrix. However, ICA is not devoted to the understanding of the distribution changes but to find the components that are both statistically independent and non-Gaussian [
To addresses the weakness of ICA-based and SOBI-based artifact correction methods on highly nonstationary EEG recordings with limited number of electrodes, this paper proposes a novel EOG artifact removal approach that utilizes the recently proposed effective BSS method, that is, stationary subspace analysis (SSA) [
The remainder of the paper is organized as follows. In Section
Figure
Block diagram of the proposed approach.
The first key step in the proposed approach is the application of SSA to separate the artifactual components from the raw EEG data. The observed signal
As can be observed from (
Using the estimated mixing matrix,
apply SSA on estimate the artifacts in multichannel EEG data by subtract the artifacts from EEG data to get clean EEG:
Forty healthy volunteers, 20 males and 20 females, aged between 20 and 33 years (mean age 27.6 years) were involved in the study.
The EEG signals were recorded on 20 volunteers with the NeuroScan SynAmps2 system. 6 EEG channels (Fp1, Fp2, C3, C4, O1, O2) were used for recording and the ground electrode was placed at position Cz, according to the 10–20 system (Figure
Placement of the EEG electrodes on the scalp according to the recording 10–20 system.
To obtain the artifact signals for contaminating the pure EEG, separate EOG signals were obtained on the remaining 20 volunteers during eyes-open sessions with eye rolling, which were recorded by two electrodes placed above and below the left eye and another two on the outer canthi. This process gave rise to two bipolar signals for each volunteer, namely, vertical-EOG (VEOG), which is equal to the upper minus lower EOG electrode recordings and horizontal-EOG (HEOG), which is equal to the left minus right EOG electrode recordings. These EOG signals were band pass filtered between 0.5 and 15 Hz.
Finally, to generate the “artificially contaminated EEG signals” (used for evaluating the performance of approaches for EOG artifact correction), we have used the Elbert’s contamination model [
The first six channels in Figure
Results of EOG artifact removal on an example EEG data set from the 20 artificially contaminated ones. The artificially contaminated EEG data with eye movement and blink artifacts are shown in the first six EEG channels of (a) and the corresponding EOG signals used in the mixing procedure are presented in the last two channels of (a). Components separated by SSA, SOBI, and ICA are shown in (b), (c), and (d), respectively. The zoomed mixed, precontaminated EEG, and corrected EEG signals by each method on channel Fp1, C3, and O1 are depicted in (e), (f), and (g), respectively.
Thanks to the precontaminated EEG signals described above, we were able to conduct quantitative comparison between the original and the corrected signals. Two commonly used evaluation metrics were adopted in the experiments with two goals to test the quality of recovering the cerebral signals and the degree of removing the ocular artifacts.
The proposed SSA-based method (referred to as SSA) was compared with SOBI-based approach (referred to as SOBI) and ICA-based approach (referred to as ICA). For SOBI and ICA, we adopted the SOBI and infomax ICA algorithms implemented in the EEGLAB toolbox [
By applying SSA on the EEG data on the first six channels shown in Figure
For the example data shown in Figure
MI by different EOG artifact correction methods over 20 artificially contaminated EEG data sets.
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Topographic map showing improvements of SAR after applying the ocular artifact correction procedure.
There are three important characteristics of the artificially contaminated EEG signals. One is that the EEG data set was recorded with limited number of channels (6 channels, see Section
In this section, we applied the artifact correction methods on real EEG data sets. Twenty volunteers, 10 males and 10 females, aged between 20 and 32 years old took part in the data collection. The data sets also contain EOG artifacts from rotation movement of the eye balls, which were recorded using the same collection configuration shown in Figure
Results of EOG artifact removal on an example EEG data set from the 20 real recordings. The EEG data with eye movement artifacts are shown in the first six EEG channels of (a) and the corresponding EOG signals are presented in the last two channels of (a). Components separated by SSA, SOBI, and ICA are shown in (b), (c), and (d), respectively. The zoomed raw and corrected EEG signals by each method on channel Fp1, C3, and O1 are depicted in (e), (f), and (g), respectively.
For the EEG data on the first six channels shown in Figure
The results of components separation by SOBI and ICA on the same data are shown in Figures
Figures
MI by different EOG artifact correction methods over 20 real EEG data sets.
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The real EEG recordings have the same three characteristics as the artificially contaminated EEG signals. Both SOBI and ICA again failed to concentrate the artifacts in fewer components again on such kind of data sets. Consequently, the removal of the contaminated components, followed by a signal reconstruction has led to distortions of the underlying cerebral activity. By contrast, SSA captured the eye movement activities in fewer components. Thereby, the proposed method has effectively suppressed the EOG artifacts, while kept the cerebral activities almost intact.
An effective approach is proposed in this paper to address the problem of ocular artifact removal from raw EEG recording. To extract artifactual sources from the raw EEG recording, we have employed the stationary subspace analysis algorithm, which can concentrate artifacts in fewer components than the representative blind source separation methods. Then the artifactual components are projected back to be subtracted from EEG signals, producing the clean EEG data eventually. The experimental results on both the artificially contaminated EEG data and real EEG data have demonstrated the effectiveness of the proposed method, in particular for the cases where limited number of electrodes are used for the recording, as well as when the artifact contaminated signal is highly nonstationary and the underlying sources cannot be assumed to be independent or uncorrelated.
The discussion here is intent to enhance the awareness that BSS algorithms may not produce physical meaningful components unless they take the characteristics of the signals into account and their underlying assumption meets the properties of signals to be analyzed. Unlike the classic blind source separation algorithms, SSA is explicitly tailored to the understanding of distribution changes, where both the mean and the covariance matrix are taken into account. In addition, neither independency nor uncorrelation is required among the sources by SSA. Thereby, it can concentrate artifacts in fewer components than the representative blind source separation methods, leading to better artifacts removal performance in the difficult scenarios mentioned above.
It has been shown that the components selected for removal may also contain neural activity aside from pure artifacts, in particular when there are limited number of recording electrodes [
The authors have declared that no conflict of interests exists.
The work is supported by the National Natural Science Foundation of China (no. 61105048), the Doctoral Fund of Ministry of Education of China (no. 20110092120034), the Natural Science Foundation of Jiangsu Province (BK20130696), and the Technology Foundation for Selected Overseas Chinese Scholar, Ministry of Human Resources and Social Security of China (no. 6722000008).