Abstract
It is a fact that contamination of EEG by ocular artifacts reduces the classification accuracy of a brain-computer interface (BCI) and diagnosis of brain diseases in clinical research. Therefore, for BCI and clinical applications, it is very important to remove/reduce these artifacts before EEG signal analysis. Although, EOG-based methods are simple and fast for removing artifacts but their performance, meanwhile, is highly affected by the bidirectional contamination process. Some studies emphasized that the solution to this problem is low-pass filtering EOG signals before using them in artifact removal algorithm but there is still no evidence on the optimal low-pass frequency limits of EOG signals. In this study, we investigated the optimal EOG signal filtering limits using state-of-the-art artifact removal techniques with fifteen artificially contaminated EEG and EOG datasets. In this comprehensive analysis, unfiltered and twelve different low-pass filtering of EOG signals were used with five different algorithms, namely, simple regression, least mean squares, recursive least squares, REGICA, and AIR. Results from statistical testing of time and frequency domain metrics suggested that a low-pass frequency between 6 and 8 Hz could be used as the most optimal filtering frequency of EOG signals, both to maximally overcome/minimize the effect of bidirectional contamination and to achieve good results from artifact removal algorithms. Furthermore, we also used BCI competition IV datasets to show the efficacy of the proposed framework on real EEG signals. The motor-imagery-based BCI achieved statistically significant high-classification accuracies when artifacts from EEG were removed by using 7 Hz low-pass filtering as compared to all other filterings of EOG signals. These results also validated our hypothesis that low-pass filtering should be applied to EOG signals for enhancing the performance of each algorithm before using them for artifact removal process. Moreover, the comparison results indicated that the hybrid algorithms outperformed the performance of single algorithms for both simulated and experimental EEG datasets.
Highlights
The functional dynamics of the brain have been thoroughly investigated over the course of many years using noninvasive brain imaging techniques [1,2,3,4,5]
Eye movements and blinking generate high-magnitude artifacts as compared with the pure neuronal activity present in EEG data [16,17,18]. Such interferences are commonly known as ocular artifacts [19, 20]. It is widely accepted within the brain-computer interface (BCI) research community that in any BCI system, neurological phenomena are the only source of control [21, 22]
One may argue that there are other methods like independent component analysis (ICA) that can be used to remove ocular artifacts from EEG data without the need of EOG signals, but irrespective of their other disadvantages these methods cannot be used for real-time/online BCI applications whereas regression-based methods are simple and fast; it can be used as an optimal option for BCI applications if their performance is enhanced [60]
Summary
The functional dynamics of the brain have been thoroughly investigated over the course of many years using noninvasive brain imaging techniques [1,2,3,4,5]. Eye movements and blinking generate high-magnitude artifacts as compared with the pure neuronal activity present in EEG data [16,17,18] Such interferences are commonly known as ocular artifacts [19, 20]. Yong et al combined stationary wavelet analysis with adaptive thresholding to automatically remove ocular artifacts from EEG data in an EEG- and eye tracker-based selfpaced BCI system [26]. They showed that their system can achieve higher BCI performance than can BCIs in which artifacts are not removed. In either clinical or practical research, to deal with these artifacts prior to the analysis of EEG signals
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