Effects of eye artifact removal methods on single trial P300 detection, a comparative study

Ghaderi, Foad; Kim, Su Kyoung; Kirchner, Elsa Andrea

doi:10.1016/j.jneumeth.2013.08.025

Cited by 37 publications

(29 citation statements)

References 26 publications

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“…In efforts to overcome the effect of bidirectional contamination, a number of studies have applied different low-pass filtering on EOG signals ranging from 5 to 100 Hz, but there is no consensus on which low-pass frequency should be used for optimal results. The idea of low-pass filtering EOG is based on studies that have demonstrated that highfrequency components in EOG signals are generated from brain activities [52], and this is supported by some studies [10,39,43,44,47,51]. It has been previously shown that performance of simple regression-based algorithms can be improved by using low-pass filtered EOG signals (7.5 Hz) as compared to unfiltered EOG signals [43].…”

Section: Discussionmentioning

confidence: 99%

“…Among different methods, EOG-based algorithms are simple and fast due to which could be used as a good tool for real-time/online BCI applications if their performance is enhanced, since it is highly affected by bidirectional contamination [10,44,47,51,60]. The simplest solution to this problem is low-pass filtering EOG signals before using them in artifact removal algorithm [10,39]. In efforts to overcome the effect of bidirectional contamination, a number of studies have applied different low-pass filtering on EOG signals ranging from 5 to 100 Hz, but there is no consensus on which low-pass frequency should be used for optimal results.…”

Section: Discussionmentioning

confidence: 99%

“…EOG-based methodologies assume that the true neuronal activity and ocular artifacts are present in a linear combination in acquired EEG signals. These methods employ regression-based analysis [10,18,[34][35][36][37][38][39][40][41][42][43][44][45][46], by which, in the time domain, the contamination coefficients of EOG signals are estimated and subtracted from each EEG channel to obtain clean EEG signals. Since being very low computation demanding, they are a great tool for real-time/online BCI applications.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, removal of ocular artifacts using EOG signals would also remove common neuronal activity present in both EEG and EOG data. In a modified version of the regression method, called filtered regression, the effects of bidirectional contamination are reduced by low-pass filtering of EOG signals prior to regression analysis [39,43,44,52]. This idea is based on studies that have shown that high-frequency components in EOG channels are generated from brain activity [43,52].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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“…By the properties, theoretically multivariate statistical analysis approaches like independent component analysis (ICA) can separate observed EEG signals into spatially and temporally distinguishable components effectively, and then, estimated components will be identified as neuronal or artifactual sources by hard/soft threshold to reconstruct artifact-free EEG matrix [10,11]. Whereas there are several reviews on artifact rejection methods including overall procedure (signal separation, component identification, and signal reconstruction) for multi-channel EEG signals [12][13][14][15][16], we have never seen review of artifact rejection methods for single-channel EEG signals. In this chapter, we therefore describe algorithms for artifact rejection in multi-/single-channel EEG signals.…”

Section: Introductionmentioning

confidence: 99%

Review of Artifact Rejection Methods for Electroencephalographic Systems

Kanoga¹,

Mitsukura²

2017

Electroencephalography

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Technologies using electroencephalographic (EEG) signals have been penetrated into public by the development of EEG systems. During EEG system operation, recordings ought to be obtained under no restriction of movement for routine use in the real world. However, the lack of consideration of situational behavior constraints will cause technical/biological artifacts that often mixed with EEG signals and make the signal processing difficult in all respects by ingeniously disguising themselves as EEG components. EEG systems integrating gold standard or specialized device in their processing strategies would appear as daily tools in the future if they are unperturbed to such obstructions. In this chapter, we describe algorithms for artifact rejection in multi-/single-channel. In particular, some existing single-channel artifact rejection methods that will exhibit beneficial information to improve their performance in online EEG systems were summarized by focusing on the advantages and disadvantages of algorithms.

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21.1: Invited Paper: EEG‐based comparative measurement of visual fatigue caused by stereoscopic depth perception

Jia

Huang

Ding

et al. 2023

Symp Digest of Tech Papers

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In this study, we investigated the influence of stereoscopic depth on visual fatigue based on subjective and EEG measurement. 19 channels of EEG data of 17 subjects before and after watching 3D movie with different depth ranges were recorded, and the power of δ, θ, α, β, power spectral parameters (gravity frequency, power spectral entropy) were calculated and analyzed. The results showed that visual fatigue caused by 3D enhanced movies with larger negative disparity was more significantly severe than that of 3D normal movies.

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Effects of eye artifact removal methods on single trial P300 detection, a comparative study

Cited by 37 publications

References 26 publications

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

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

Review of Artifact Rejection Methods for Electroencephalographic Systems

21.1: Invited Paper: EEG‐based comparative measurement of visual fatigue caused by stereoscopic depth perception

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