Effect of EOG Signal Filtering on the Removal of Ocular Artifacts and EEG‐Based Brain‐Computer Interface: A Comprehensive Study

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.

Removal of Artifacts from EEG Signals: A Review

“…Such physiological artifacts may interfere with neural information and even be used as normal phenomena to misleadingly drive a practical application such as brain-computer interface [9]. Furthermore, artifacts might also imitate cognitive or pathologic activity and therefore bias the visual interpretation and diagnosis in clinical research such as sleep order, Alzheimer’s disease [10,11], etc.…”

Investigation of EEG Signal Classification Techniques for Brain Computer Interface

“…The efficiency of preprocessing Figure 2. Timing scheme of the paradigm for data set 2a from BCI Competition IV [33] phase has a direct impact on efficiency attainment of overall BCI system [34]. The raw data set stored in GDF format was loaded by using functions of BioSig toolbox [35].…”

“…Electrodes Location by International 10-20System [Timing scheme of the paradigm for data set 2a from BCI Competition IV[33] …”

Drowsiness, Fatigue and Poor Sleep’s Causes and Detection: A Comprehensive Study

“…These findings can be helpful for avoidance of car crashes and prediction of brain states much earlier than the points at which collisions happen [19], [29], [51]. fNIRS, EEG and fMRI, being non-invasive methodologies, are useful for such investigations [37], [97]- [101]. Among them, EEG and fNIRS are at competitive positions due to additional advantages of portability, low cost, and acceptable temporal and spatial resolutions [96], [102], [103].…”