Implementation of tools for lessening the influence of artifacts in EEG signal analysis

Ana M. Barbancho; Isabel Barbancho; Lorenzo J. Tardon; Mario Molina-Molina

REVIEW 4 major objections 6 minor 24 references

Implementation of tools for lessening the influence of artifacts in EEG signal analysis

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This paper provides ready-to-run scripts that reduce ocular artifacts in EEG by removing or attenuating independent components correlated with EOG signals.

desk verdict A useful open-source Fieldtrip implementation of EOG-artifact ICA methods that honestly demonstrates rather than validates itself, with the effectiveness claim resting on visual inspection and a tautological metric. read the letter →

arxiv 2411.13931 v1 pith:BKUTV55O submitted 2024-11-21 eess.SP

Mario Molina-Molina , Lorenzo J. Tardon , Ana M. Barbancho , Isabel Barbancho This is my paper · ORCID

classification eess.SP

keywords independentcomponentanalysisocularartifactremovalelectro-oculogrammembershipfunctionEEGpreprocessingMatlabFieldtrip

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper reports a set of ready-to-run scripts that lessen the influence of ocular (eye-movement and blink) artifacts in EEG recordings without requiring trials to be discarded. The implementations translate three previously proposed ICA-based methods into reusable functions: removing independent components that correlate strongly with vertical and horizontal EOG channels, removing those components only at artifact-marked time samples, and deriving the ICA unmixing matrix from artifact-free segments before partial removal. The authors argue these tools matter because long-trial EEG experiments cannot always avoid eye artifacts by rejecting contaminated trials, and because open, reproducible code lowers the barrier to using and extending ICA-based artifact handling.

What carries the argument

The load-bearing object is the artifact membership function (MSF), a logical vector of the same length as the data in which artifact samples are set to 1 and all others to 0. The paper provides a function that builds the MSF automatically using thresholded z-scores on the EOG channels after a 2-15 Hz band-pass filter, with a cut-off of 0.5 and 0.1 seconds of padding; it also uses the sum of absolute correlation coefficients between ICA components and the vertical and horizontal EOG channels to select which components to remove. The MSF makes the pipeline modular, because any artifact detector that outputs such a marker vector can be substituted.

What would settle it

Take an EEG recording with simultaneous video-based eye tracking or expert-annotated blink and saccade intervals, build the membership function with the provided defaults, and compare its marked samples against the annotations; if many real artifacts go unmarked or many non-artifact segments are marked, the partial-removal methods will either leave artifacts in or attenuate brain responses. A quantitative version would add simulated ocular components with known timing into clean EEG and check whether the partial-removal methods attenuate exactly those components.

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Extended reading notes

Core claim

The paper's central claim is that its provided functions, built around an artifact membership function and EOG-correlation measures, successfully reduce ocular artifact influence in EEG channels. In the complete-removal variant, ICA components whose summed absolute correlation with the vertical and horizontal EOG signals is an outlier are discarded and the channels are reconstructed from the rest. In the two partial-removal variants, the same component selection is applied but only samples marked by the artifact membership function are zeroed, so non-artifact brain activity outside those windows is meant to be preserved; the third variant recomputes the ICA unmixing matrix on artifact-free samples before the partial removal. The paper shows example results on frontal channels, where large artifact peaks disappear and the summed correlation between independent components and EOG channels drops after processing.

Load-bearing premise

The automatic artifact detector that builds the membership function is assumed to mark exactly the eye-artifact samples, so that zeroing them removes ocular noise without also deleting genuine brain activity.

Editorial extensions

If this is right

Researchers can apply the functions to their own EEG recordings and keep contaminated trials in the analysis instead of discarding them.
Because the artifact membership function is a simple binary marker vector, alternative artifact detectors can be plugged in without changing the component selection or reconstruction code.
In the two partial-removal methods, only samples identified as artifacts are altered, so genuine brain activity in clean segments should survive the cleaning.
The complete-removal method offers a baseline that removes whole components and may discard some non-artifact signal, which the partial methods are designed to avoid.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

An implication the paper leaves implicit is that the default artifact-detection parameters are shown on one subject and one stimulus; testing them across many recordings would show whether they generalize or need per-recording tuning.
A testable extension would replace the sum-of-absolute-correlation selection with component topographies or equivalent dipole locations, which often separate ocular sources more cleanly than raw correlation.
The same membership-function machinery could be pointed at other artifact types with reference channels, such as muscle or cardiac artifacts, though the paper does not demonstrate this.
Allowing the membership function to carry continuous weights instead of zeros and ones would enable graded attenuation, a possibility the authors mention but do not implement.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is an engineering companion to the authors' earlier method paper [14]. The genuine contribution is a set of documented Matlab/Fieldtrip functions for three variants of EOG-correlation-based ICA artifact removal, plus an automatic artifact membership function builder, all on a public repository. That fills a real practical gap: EEG researchers who keep long trials often need open implementations rather than ad hoc scripts. The paper does a good job of step-by-step API documentation and makes the code and one subject's data available.

What it does not do is provide a quantitative validation of the artifact-reduction claim. The evidence is visual inspection of one subject (subject 001) and Figure 2, which shows the sum of |correlation| between ICs and EOG before/after removal. That metric is exactly what the algorithm minimizes, so the drop is by construction. There is no independent error measure, no expert annotation, no downstream-task evaluation, no baseline comparison with, say, ADJUST or MARA. The paper itself acknowledges amplitude decreases in non-artifact regions after Method 1, which is consistent with over-removal. Methods 2 and 3 rely on the MSF built by ft_artifact_zvalue with cut-off 0.5, 2-15 Hz band-pass, and padding 0.1; those parameters are never validated against ground truth. So the effectiveness claim is plausible but not established.

That said, the authors are fairly careful in language: they say 'illustrative examples' and 'expected results,' and they explicitly invite users to substitute other artifact detectors and selection criteria. The code is the deliverable, and for a software descriptor paper that's a reasonable framing. The lack of quantitative evaluation is a soft spot, not a fatal flaw, as long as readers treat the figures as demonstrations rather than proof.

Who's this for? Applied EEG researchers who want a working Fieldtrip starting point for EOG-artifact reduction and are comfortable adapting parameters. Not for methodologists. The paper deserves a serious referee because the software release is checkable and useful, but the referee should push for either clearer effectiveness claims or an evaluation protocol.

Referee Report

4 major / 6 minor

Summary. The manuscript describes and releases Matlab/Fieldtrip implementations of three methods, originally from [14], for reducing ocular artifacts in EEG: (1) complete removal of ICA components most correlated with EOG channels; (2) partial removal of those components only at samples flagged by an artifact membership function (MSF); and (3) partial removal using an unmixing matrix estimated from artifact-free data. It also provides a function to build the MSF using ft_artifact_zvalue, sample scripts (Boxes 1-3), and example results for one subject (001). The central claim is that these implementations lessen the influence of ocular artifacts and that the code and data allow exact replication.

Significance. The paper addresses a real need identified in [13]: lack of open-source artifact-removal implementations. Its strengths are the detailed function-by-function description, the release of data and code at a persistent handle, and the attempt to keep the MSF format simple and replaceable. If the methods were quantitatively validated, the contribution would be a useful reference implementation for practitioners. However, the current evidence for effectiveness is limited to visual inspection of two channels in one subject and a correlation metric that is partly self-referential; the paper therefore does not yet substantiate its central claim.

major comments (4)

[Section 5, Figure 2] The only quantitative evidence is the reduction in the sum of absolute correlations between ICs and EOG before and after processing. Because the algorithm selects components precisely to minimize this quantity, the decrease is expected by construction and does not independently show that ocular artifacts in EEG channels are reduced or that brain signals are preserved. I recommend adding validation against expert-labeled artifact intervals, a downstream EEG analysis endpoint, or comparison with an established method such as ADJUST, MARA, or ASR, with quantitative metrics such as artifact attenuation, signal-to-error ratio, or preservation of known evoked responses.
[Section 5, Method 1] The text states that in some areas not influenced by artifacts there is a decrease in amplitude with respect to the original shape of the signal. This indicates possible over-removal, but it is not quantified or compared across methods. Since the paper claims lessening artifact influence, it must report the trade-off between artifact energy removed and non-artifact signal distortion, for example by computing RMS error or spectral error on artifact-free segments, or by showing preservation of known neural responses.
[Section 4.5] The automatic MSF created by fta_create_msf_fieldtrip uses ft_artifact_zvalue with a cut-off of 0.5, a 2-15 Hz band-pass, and padding of 0.1. These parameters are asserted without validation. Methods 2 and 3 depend entirely on this MSF, so the paper should report the sensitivity of the results to these choices and, ideally, compare the MSF against expert annotation or another artifact detector, such as those in [20-22], in terms of detection accuracy.
[Section 5] All examples are from a single subject (001) and a single stimulus ('truck'). No across-subject statistics, error bars, or reproducibility checks are provided. For a software/tool paper, a small multi-subject demonstration with quantitative summary measures is necessary to support the claim that the implementations generally lessen artifact influence rather than merely working on one selected example.

minor comments (6)

[Abstract] The phrase 'describes and implementation of scripts of code' should be corrected to 'describes an implementation of scripts of code' or 'describes implementations of scripts of code'.
[Section 2.1] The units '10 KQ' should be '10 kΩ', and the same correction should be applied throughout the equipment description.
[Section 4.1, step 4] The term 'modulus of correlation coefficients' should be replaced with 'absolute value' or 'magnitude' for clarity and consistency with standard statistical terminology.
[Boxes 1-3] The example code contains typos and inconsistencies with the function signatures described in the text, such as 'clean.data' instead of 'cleaned_data', 'cfg.hpfilttype -' instead of 'cfg.hpfilttype =', and 'Corretion' instead of 'Correction'. These should be fixed so that the examples run exactly as printed.
[Sections 4.1-4.9] Function names are rendered inconsistently, for example 'fta_select_compjrom_coeff_correlation' versus 'fta_select_compjrom_coeffcorrelation' and 'fta_channels_jrom_ica_reject_components' versus 'fta_channels_from_ica_reject_components'. The text should use exactly the names that appear in the released code to avoid confusion for readers attempting to replicate the pipeline.
[Figure captions] The captions for Figures 1 and 3 use 'Fpl', but the standard and presumably intended label is 'Fp1'.

Circularity Check

1 steps flagged · score 6.0 of 10

Quantitative artifact-reduction evidence (Figure 2) is tautological: the plotted correlation sum is the algorithm's own selection criterion, so its decrease is guaranteed by construction.

fitted input called prediction [Section 5, Method 1, Figure 2; selection rule in Section 4.1, steps 4–5]
"The reduction of the correlation coefficients between the independent components and EOG data after using the method is illustrated in Figure 2; specifically, Figure 2b shows a significant reduction of the correlation coefficients with respect to Figure 2a, after the application of the procedure."

The selection rule in Section 4.1 uses the same quantity as the reported outcome metric: 'the sum of the correlation coefficients is ordered from highest to lowest to show these components can be selected using a different criterion. These components are the ones selected for elimination.' Removing the components with the largest summed |correlation| with EOG guarantees that the post-removal sum in Figure 2b is smaller than the pre-removal sum in Figure 2a. The reported 'significant reduction' is therefore true by construction; it is the method's objective function renamed as a result, and it provides no independent evidence that ocular influence in reconstructed EEG channels is reduced or that non-artifact brain activity is preserved.

full rationale

The paper is primarily a software-implementation and reproducibility contribution, not a claim of a newly derived scientific law. The central deliverable—Matlab/Fieldtrip code, data, and examples—is self-contained and externally reproducible, so the availability claim is not circular. Reference [14] is a self-citation, but it is cited as the method being implemented rather than as a proof of efficacy; no load-bearing derivation rests on that citation. The circular element is concentrated in the quantitative support of Figure 2: the before/after correlation drop is the method's own selection objective, so it reduces by construction. Figures 1, 3, and 4 are single-subject visual illustrations with no independent artifact annotation, downstream-task metric, or comparison to a gold standard, and the automatically built MSF is not validated against expert labeling. Those are validation gaps and correctness risks rather than circular derivations. Overall, the artifact-reduction claim is partially supported by a tautological metric, giving a score of 6.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The implementation depends on hand-chosen MSF parameters and an unspecified IC-outlier rule, none of which is validated against independent data; no new physical or conceptual entities are introduced.

free parameters (5)

MSF z-value cut-off = 0.5
Threshold for ft_artifact_zvalue to mark samples as ocular artifacts (Section 4.5); chosen by hand, not validated against ground truth.
MSF band-pass filter = Butterworth order 3, 2-15 Hz
Passband used before z-value artifact detection (Section 4.5); manual choice affecting which artifacts are detected.
artifact padding = 0.1 s
Extension of artifact intervals on both sides (Section 4.5); manual choice affecting MSF extent.
trial padding = 0 s
Segment padding for artifact detection (Section 4.5); manual choice.
IC outlier selection criterion
The function fta_select_compjrom_coeff_correlation is said to select outliers, but the exact rule or threshold is not specified (Sections 4.3 and 5); this is an implicit free parameter controlling how many ICs are removed.

assumptions (4)

domain assumption EEG channels are a linear mixture of statistically independent sources (ICA model), so unmixing separates ocular artifacts into distinct components.
Needed for all methods to justify removing or zeroing individual ICs and reconstructing channels (Sections 3 and 4).
domain assumption Independent components with high absolute correlation with VEOG/HEOG are ocular artifact components.
The component-selection criterion in all three methods (Sections 4.1, 4.6, 4.8); no expert-labeled validation.
domain assumption The artifact membership function produced by ft_artifact_zvalue with the selected parameters correctly identifies ocular artifact samples.
Required for Methods 2 and 3 to zero only artifact samples; no ground-truth comparison is provided (Section 4.5).
domain assumption An ICA unmixing matrix estimated from artifact-free samples is valid for the entire recording.
Used in Method 3 (Section 4.8); assumes stationarity and sufficient data without testing.

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Cite this review

Pith. "Pith review of Implementation of tools for lessening the influence of artifacts in EEG signal analysis." pith.science (2026). https://pith.science/paper/BKUTV55O

@misc{pith2026241113931,
  author       = {Pith},
  title        = {Pith review of: Implementation of tools for lessening the influence of artifacts in EEG signal analysis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BKUTV55O}},
  note         = {Machine review of arXiv:2411.13931}
}

read the original abstract

This manuscript describes and implementation of scripts of code aimed at reducing the influence of artifacts, specifically focused on ocular artifacts, in the measurement and processing of electroencephalogram (EEG) signals. This process is of importance because it benefits the analysis and study of long trial samples when the appearance of ocular artifacts cannot be avoided by simply discarding trials. The implementations provided to the reader illustrate, with slight modifications, previously proposed methods aimed at the partial or complete elimination of EEG channels or components are those that resemble the electro-oculogram (EOG) signals in which artifacts are detected. In addition to the description of each of the provided functions, examples of utilization and illustrative figures will be included to show the expected results and processing pipeline.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

24 extracted references · 24 canonical work pages

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [14]

Preprocessing for Lessening the Influence of Eye Artifacts in EEG Analysis

Villena, A.; Tardón, L.J.; Barbancho, I.; Barbancho, A.M.; Brattico, E.; Haumann, N.T. Preprocessing for Lessening the Influence of Eye Artifacts in EEG Analysis. Appl. Sci. 2019, 9,1757. [CrossRef]

work page 2019

[2] [13]

Review of Challenges Associated with the EEG Artifact Removal Methods

Mumtaz, W; Rasheed, S.; Irfan, A. Review of Challenges Associated with the EEG Artifact Removal Methods. Biomed. Signal Process. Control 2021, 68,102741. [CrossRef]

work page 2021

[3] [1]

Correction of EOG Artifacts in Event-Related Potentials of the EEG: Aspects of Reliability and Validity

Verleger, R.; Gasser, T.; Mocks, J. Correction of EOG Artifacts in Event-Related Potentials of the EEG: Aspects of Reliability and Validity. Psychophysiology 1982,19,472-480. [CrossRef] [PubMed]

work page 1982

[4] [2]

The Removal of the Eye-Movement Artifact from the EEG by Regression Analysis in the Frequency Domain

Woestenburg, J.; Verbaten, M.; Slangen, J. The Removal of the Eye-Movement Artifact from the EEG by Regression Analysis in the Frequency Domain. Biol. Psychol. 1983,16,127-147. [CrossRef] [PubMed]

work page 1983

[5] [3]

Adaptive AR Modeling of Nonstationary Time Series by Means of Kalman Filtering

Arnold, M.; Milner, X.H.R.; Witte, H.; Bauer, R.; Braun, C. Adaptive AR Modeling of Nonstationary Time Series by Means of Kalman Filtering. IEEE Trans. Biomed. Eng. 1998,45,553-562. [CrossRef] [PubMed]

work page 1998

[6] [4]

Automatic Removal of Eye Movement and Blink Artifacts from EEG Data using Blind Component Separation

Joyce, C.; Gorodnitsky, L; Kutas, M. Automatic Removal of Eye Movement and Blink Artifacts from EEG Data using Blind Component Separation. Psychophysiology 2004, 41,313-325. [CrossRef] [PubMed]

work page 2004

[7] [5]

Removal of Artifacts from EEG Signals: A Review

Jiang, X.; Bian, G.B.; Tian, Z. Removal of Artifacts from EEG Signals: A Review. Sensors 2019,19, 987. [CrossRef] [PubMed]

work page 2019

[8] [6]

Isolation of Multiple Electrocardiogram Artifacts using Independent Vector Analysis

Uddin, Z.; Altaf, M.; Ahmad, A.; Qamar, A.; Orakzai, F. Isolation of Multiple Electrocardiogram Artifacts using Independent Vector Analysis. Peer] Comput. Sci. 2023, 9, ell89. [CrossRef] [PubMed]

work page 2023