Pith. sign in

REVIEW 4 major objections 5 minor 93 references

Improved Riemannian potato field: an Automatic Artifact Rejection Method for EEG

T0 review · 4 major / 5 minor · reviewed 2026-08-04 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read iRPF, a fully automatic EEG artifact rejection method, outperforms four rivals with no manual threshold tuning.

desk verdict A well-engineered extension of RPF with serious evaluation, but the manual relabeling of EYEAR ground truth and test-data-tuned baseline keep the headline claims from being fully trustworthy. read the letter →

arxiv 2509.09264 v1 pith:TQLY2OIU submitted 2025-09-11 eess.SP

Davoud Hajhassani (GIPSA-VIBS) , Quentin Barthélemy , Jérémie Mattout (CRNL , CRNL-COPHY) , Marco Congedo (GIPSA-VIBS) This is my paper · ORCID
classification eess.SP
keywords EEGartifactrejectionRiemanniangeometrycovariancematricesadaptivethresholdingsignalqualityindexp-valuecombinationoutlierbrain-computerinterfaces
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

The paper claims that artifact rejection in EEG can be made fully automatic and adaptive, eliminating the manual threshold choices that handicap existing methods. It introduces iRPF, which builds on the Riemannian Potato Field idea by combining many small covariance-based detectors targeting specific artifacts, then adds four improvements: automatic outlier rejection, adaptive barycenter estimation, extra distance metrics, and a meta-combination of p-values. On two public EEG databases comprising 226 recordings, iRPF reports higher recall, specificity, precision, and F1 than Riemannian Potato, Riemannian Potato Field, Isolation Forest, and Autoreject, with statistical significance and large effect sizes in most comparisons, while running in under 8 ms per epoch. If correct, this gives researchers a fast, data-driven preprocessing step that does not distort the signal and may scale to large datasets and real-time use.

What carries the argument

The central object is the "potato field": a collection of low-dimensional Riemannian potatoes, each a detector built on the covariance matrix of a specific subset of EEG channels in a specific frequency band, whose distances to a robust barycenter become z-scores, then p-values, merged into a per-epoch Signal Quality Index. Carrying the argument are two adaptive mechanisms: an outlier-rejection threshold defined from the field root mean square (FRMS) of the data, assuming clean EEG's FRMS is symmetric about its median-based mean, and a knee-detection (Kneedle) algorithm that sets the acceptance threshold from the sorted SQI values, making the whole pipeline free of manually chosen thresholds

What would settle it

Run iRPF on an EEG dataset with expert-labeled clean epochs containing high-amplitude but non-artifactual activity (e.g., generalized epileptiform discharges, high-amplitude occipital alpha, or drowsy slow waves) and count how many labeled-clean epochs are rejected. If a substantial fraction (e.g., >10%) of clean epochs are rejected in the high-amplitude subjects, the symmetry assumption fails and the automatic outlier rejection is not universally safe.

Watch

Extended reading notes

Core claim

The central claim is that iRPF is a fully automatic and adaptive EEG artifact rejection method that outperforms established alternatives. It replaces fixed thresholds with a knee-detection algorithm that reads the data's own distribution, adds a field-root-mean-square-based outlier rejection stage so the geometric mean of covariance matrices is not pulled toward extreme artifacts, and enriches the Riemannian Potato Field with Euclidean and diagonal-Euclidean distances to catch vertical eye movements and myogenic artifacts that the Riemannian distance alone tends to miss. Combining these distances' p-values through multiple combination functions in a meta-combination yields a Signal Quality I

Load-bearing premise

The method assumes that the field root mean square (FRMS) of normal EEG is symmetrically distributed around its median-based mean, so that the adaptive threshold (mean plus the gap to the lower limit) separates extreme artifacts from legitimate high-amplitude brain activity.

Editorial extensions

If this is right

  • EEG preprocessing for brain-computer interfaces or clinical studies could run iRPF without per-dataset threshold tuning and still match or exceed tuned competitors.
  • By adding Euclidean and diagonal-Euclidean distances, iRPF is claimed to detect vertical eye movements and muscle artifacts that the affine-invariant Riemannian distance alone tends to miss.
  • The reported speed (under 8 ms per epoch on a laptop) supports large-scale offline processing and suggests real-time feasibility after further development.
  • The ablation study shows automatic outlier rejection carries much of the gain: dropping it reduces F1 from 0.96 to 0.78 on the eye-artifact database with EOG channels, indicating the advantage rests on the full combination of components.
  • Because iRPF rejects rather than corrects epochs, it preserves the original signal, which is a stated advantage for downstream cognitive and clinical analyses.

Reading between the lines

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

  • If the FRMS symmetry assumption holds broadly, the same adaptive thresholding recipe could transfer to other biosignals whose epochs are naturally represented as covariance matrices, such as MEG or EMG.
  • A testable extension: on clinical recordings with high-amplitude pathological oscillations (e.g., epileptiform discharges), iRPF's outlier rejection may reject legitimate brain activity because the clean FRMS distribution is right-skewed; measuring false-rejection rates on such data would expose the boundary of the symmetry assumption.
  • The meta-combination strategy is a general recipe — combining p-value functions sensitive to different parts of the distribution could improve signal-quality indices beyond EEG artifact rejection.
  • Because potato-field design still requires knowledge of electrode layout and artifact physiology, a natural next step left implicit by the paper is to auto-generate potatoes from channel coordinates alone; the paper's own robustness results suggest performance varies little with reasonable field definitions.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The manuscript proposes an improved Riemannian Potato Field (iRPF) for automatic EEG artifact rejection. The method extends the existing RPF framework with four main contributions: (1) an adaptive FRMS-based outlier rejection step preceding barycenter estimation, (2) a Kneedle-based adaptive robust barycenter estimation that removes the fixed threshold, (3) additional Euclidean and diagonal-Euclidean distance features in the potato field, and (4) additional p-value combination functions and a meta-combination strategy for computing the signal quality index, together with automatic knee-based thresholding of the SQI. The method is evaluated on two publicly available databases, EYEAR (with and without EOG channels) and TUAR, against RPF, RP, Isolation Forest, and Autoreject. The authors report consistent improvements in recall, specificity, precision, and F1-score, supported by permutation-based multiple-comparison tests and Cohen's d effect sizes, and report an execution time below 8 ms per epoch.

Significance. If the reported results hold, iRPF would be a practically valuable contribution: it is a fully automatic, fast, and publicly implemented method with a plausible mechanism for improving robustness of Riemannian-geometry-based artifact rejection. The statistical analysis is a clear strength: the use of a 200K-permutation t-max step-down procedure to control family-wise error rate, the reporting of effect sizes, and the inclusion of runtime comparisons are all commendable. The central claim of superiority over four baselines is, however, conditional on the integrity of the EYEAR labels, the fairness of the baseline configurations, and the validity of the FRMS symmetry assumption. These issues are addressable and do not, on the present evidence, require rejection, but they require substantive revision before the claim can be accepted.

major comments (4)
  1. [Section 2.6.1 (EYEAR database)] The authors state that after discovering that some epochs labeled 'rest' were contaminated by blinks, they 'manually reviewed the data and relabeled the affected epochs.' This is a developer-driven modification of the test labels, and the manuscript reports no details about the relabeling procedure, no inter-rater reliability, and no comparison with the original labels. Because iRPF is specifically designed to detect eye blinks, even subtle label bias could inflate its recall, precision, and F1. This is load-bearing: the strongest reported result (F1 = 0.96 on EYEAR with EOG) and the large ablation contribution of the outlier-rejection component (Table 7) come from this database. Please report the number and nature of relabeled epochs, provide results using the original database labels, and/or perform a sensitivity analysis excluding or conservatively re-labeling the affected epochs with
  2. [Section 3.1 and Table 7 (baseline threshold selection)] The RPF baseline threshold is set to 0.5 because it 'yielded the highest averaged F1-score.' If this selection was made on the same test data used for the comparison, the RPF baseline is effectively oracle-tuned, while iRPF must discover its threshold automatically. The ablation study uses the same practice: when automatic thresholding is disabled, threshold 0.01 is adopted because it 'yielded the best results.' This makes the comparison between automatic and manual thresholding difficult to interpret. Please use nested cross-validation or report performance across a grid of thresholds with uncertainty, and clarify explicitly whether the 0.5 and 0.01 choices were made on the evaluation folds.
  3. [Section 2.4.1 (automatic outlier rejection and FRMS symmetry)] The method's adaptive outlier-rejection threshold relies on the assumption that the FRMS distribution of normal EEG is symmetric around the median-based mean, so that the threshold t_hrej = mu_frms + u_lim(mu_frms - l_lim) removes extreme outliers without discarding legitimate high-amplitude activity. The ablation study (Table 7) shows that this component contributes 0.18 F1 on EYEAR with EOG, so the assumption is load-bearing. Two issues arise. First, u_lim is a free parameter described as 'adjustable based on the characteristics of the database,' which conflicts with the 'fully automatic' claim in the abstract. Second, no empirical validation of the symmetry assumption is provided, and clinical EEG with pathological high-amplitude activity or different amplifier settings may violate it. Please report the empirical FRMS distributions on both databases (e.g., skewness, quantiles, the loc
  4. [Section 3.1 (modified baselines)] The authors state that to ensure a fair comparison they applied the proposed automatic outlier rejection method before using RPF and RP. As a result, the evaluated RPF and RP are not the original published methods, but hybrids that include a key iRPF component. This does not necessarily bias the comparison against iRPF, but it means the reported comparison does not isolate the contribution of iRPF over the original methods, and the abstract's phrase 'compared to ... Riemannian Potato, and Riemannian Potato Field' is potentially misleading. Please also evaluate the original, unmodified RPF and RP, or clearly label the modified baselines as 'RPF + iRPF outlier rejection' and 'RP + iRPF outlier rejection'.
minor comments (5)
  1. [Eq. (1)] The sample covariance estimator is written as (1/(N-1)) X X^T, but with X in R^{N x T} the normalization should be by T-1, not N-1. If the implementation uses a standard covariance routine this is likely only a typo, but it should be corrected for reproducibility.
  2. [Section 2.4.3] Typo: 'connivance matrices' should be 'covariance matrices.'
  3. [General / Results] The paper reports only Cohen's d values and figures for the main performance metrics; no table gives the per-method means and standard deviations of recall, specificity, precision, and F1 for each database. Please add such a table so that the claimed gains can be inspected numerically.
  4. [Section 2.5] In the description of EOG electrodes, the right-eye electrode labels appear inconsistent (EOGL1, EOGL2, EOGR3). Please verify the nomenclature against Table 2.
  5. [Section 2.3 / threshold direction] The definition of the RPF SQI using p-values and the statement that epochs with p below a threshold are rejected should be made more explicit, since a threshold of 0.5 is much higher than the original RPF literature convention of 0.01 and can be confusing without a clear statement of the rejection direction.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: iRPF's performance claims rest on external labels and unsupervised adaptive thresholds, not on its own definitions or load-bearing self-citations.

full rationale

The central claim that iRPF outperforms RPF, RP, IF, and AR is evaluated against externally provided artifact labels in EYEAR and TUAR. The method's thresholds and distances are computed unsupervised from the unlabeled epochs; no label information enters the derivation of the SQI or the rejection rule. The automatic outlier rejection (Section 2.4.1) and knee-based thresholding (Sections 2.4.2, 2.4.5) are data-driven heuristics, not fits to the evaluation labels. The RPF baseline is cited from prior work by two of the authors ([65]), but the comparison is empirical and external, so the self-citation is not load-bearing. The manual relabeling of EYEAR rest epochs (Section 2.6.1) and the choice of RPF threshold 0.5 as the best F1 (Section 3.1) are validity/fairness concerns, not circularity: they do not make iRPF's output equivalent to its inputs by construction. The paper explicitly acknowledges its limitations (Section 4), which further supports that no circular derivation is being concealed.

Assumptions & free parameters 3 free parameters · 6 assumptions · 0 invented entities

No new physical or latent entities are postulated. The central claim rests on standard Riemannian geometry, several domain-specific statistical assumptions about EEG artifact distributions, and the authors' manual curation of the evaluation data.

free parameters (3)
  • u_lim (outlier rejection upper limit) = 1 (default)
    Sets the FRMS outlier threshold as mu_frms + u_lim*(mu_frms - l_lim); the paper says adjustable based on database, so it is a hand-set hyperparameter of the iRPF method (Section 2.4.1).
  • max iterations for robust barycenter = 4
    The adaptive robust barycenter stops after four iterations, a limit said to be determined through preliminary experiments and prior research (Section 2.4.2).
  • RPF baseline rejection threshold = 0.5
    In the comparison, RPF's SQI threshold was set to 0.5 because it yielded the highest averaged F1-score on the evaluation data, i.e., fitted post hoc (Section 3.1). Not part of iRPF but part of the evidence for the headline claim.
assumptions (6)
  • standard math The affine-invariant Riemannian distance and geometric mean of SPD matrices define meaningful similarity between EEG epochs (Eqs. 2-3).
    Standard Riemannian geometry, cited to [57,64,73,74] in Section 2.1. This is the foundation of the potato approach.
  • standard math p-values combined via Fisher, Liptak, Pearson, and Tippett are valid under the assumption that the individual z-score p-values are uniform and independent (Section 2.4.4).
    The paper invokes the standard distributional results under uniformity and independence, but notes in Section 2.3 that z-scores of affine-invariant Riemannian distances are generally not normally distributed, so the uniformity assumption is violated in practice.
  • domain assumption In normal EEG, the distribution of FRMS values across time is finite and symmetric, so the adaptive outlier threshold t_hrej separates extreme artifacts from clean signal (Section 2.4.1).
    This assumption underlies the automatic outlier rejection component, which the ablation study shows is the largest contributor to iRPF's F1 gains. No empirical validation of symmetry is provided.
  • domain assumption The knee in the sorted p-values or SQI values marks the boundary between artifacts and clean epochs (Sections 2.4.2, 2.4.5).
    Kneedle is applied to unsupervised statistics; the paper shows one example (Fig. 6) but no systematic validation that the knee corresponds to the true labeling.
  • domain assumption The hand-designed potato fields (Tables 2, 3, 5) are appropriate for the artifact types in each database.
    Section 2.5 says the field must be customized per headset using expert knowledge; Section 3.3 tests sensitivity to field design but only within reasonable designs.
  • domain assumption The relabeled EYEAR epochs and the selected 196 TUAR files provide accurate ground truth for evaluation (Section 2.6).
    Manual review by the authors altered labels, and 114 of 310 TUAR files were excluded because they lacked sufficient clean data. These curation choices are not independently auditable from the paper.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Improved Riemannian potato field: an Automatic Artifact Rejection Method for EEG." pith.science (2026). https://pith.science/paper/TQLY2OIU

@misc{pith2026250909264,
  author       = {Pith},
  title        = {Pith review of: Improved Riemannian potato field: an Automatic Artifact Rejection Method for EEG},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TQLY2OIU}},
  note         = {Machine review of arXiv:2509.09264}
}
read the original abstract

Electroencephalography (EEG) signal cleaning has long been a critical challenge in the research community. The presence of artifacts can significantly degrade EEG data quality, complicating analysis and potentially leading to erroneous interpretations. While various artifact rejection methods have been proposed, the gold standard remains manual visual inspection by human experts-a process that is time-consuming, subjective, and impractical for large-scale EEG studies. Existing techniques are often hindered by a strong reliance on manual hyperparameter tuning, sensitivity to outliers, and high computational costs. In this paper, we introduce the improved Riemannian Potato Field (iRPF), a fast and fully automated method for EEG artifact rejection that addresses key limitations of current approaches. We evaluate iRPF against several state-of-the-art artifact rejection methods, using two publicly available EEG databases, labeled for various artifact types, comprising 226 EEG recordings. Our results demonstrate that iRPF outperforms all competitors across multiple metrics, with gains of up to 22% in recall, 102% in specificity, 54% in precision, and 24% in F1-score, compared to Isolation Forest, Autoreject, Riemannian Potato, and Riemannian Potato Field, respectively. Statistical analysis confirmed the significance of these improvements (p < 0.001) with large effect sizes (Cohen's d > 0.8) in most comparisons. Additionally, on a typical EEG recording iRPF performs artifact cleaning in under 8 milliseconds per epoch using a standard laptop, highlighting its efficiency for large-scale EEG data processing and real-time applications. iRPF offers a robust and data-driven artifact rejection solution for high-quality EEG pre-processing in brain-computer interfaces and clinical neuroimaging applications.

Figures

Figures reproduced from arXiv: 2509.09264 by the authors.

Figure 4
Figure 4. Illustration of Euclidean distance (right) versus Riemannian distance (left) in distinguishing the covariance matrices of epochs labeled as vertical eye movement (VEM) from the covariance matrices labeled as resting state (rest). The Riemannian barycenter is represented by the black  sign. The colormap indicates the z-score, and a selected isocontour (𝒛𝒕𝒉 = 𝟎. 𝟓 to enhance visualization) defines the boundary of the… view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

93 extracted references · 28 canonical work pages

  1. [1]

    The origin of extracellular fields and currents -EEG, ECoG, LFP and spikes

    Buzsáki G, Anastassiou CA, Koch C. The origin of extracellular fields and currents -EEG, ECoG, LFP and spikes. Nat Rev Neurosci 2012;13. https://doi.org/10.1038/nrn3241

    doi:10.1038/nrn3241 2012

  2. [2]

    Where Does EEG Come From and What Does It Mean? Trends Neurosci 2017;40:208–18

    Cohen MX. Where Does EEG Come From and What Does It Mean? Trends Neurosci 2017;40:208–18. https://doi.org/10.1016/J.TINS.2017.02.004

    doi:10.1016/j.tins.2017.02.004 2017

  3. [3]

    Electroencephalography (EEG) Technology Applications and Available Devices

    Soufineyestani M, Dowling D, Khan A. Electroencephalography (EEG) Technology Applications and Available Devices. Applied Sciences 2020, Vol 10, Page 7453 2020;10:7453. https://doi.org/10.3390/APP10217453

    work page doi:10.3390/app10217453 2020

  4. [4]

    A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update

    Lotte F, Bougrain L, Cichocki A, Clerc M, Congedo M, Rakotomamonjy A, et al. A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update. J Neural Eng 2018;15. htt ps://doi.org/10.1088/1741- 2552/AAB2F2. 17

    doi:10.1088/1741- 2018

  5. [5]

    EEG artifact removal-state-of-the-art and guidelines

    Urigüen JA, Garcia-Zapirain B. EEG artifact removal-state-of-the-art and guidelines. J Neural Eng 2015;12. https://doi.org/10.1088/1741-2560/12/3/031001

    doi:10.1088/1741-2560/12/3/031001 2015

  6. [6]

    Methods for artifact detection and removal from scalp EEG: A review

    Islam MK, Rastegarnia A, Yang Z. Methods for artifact detection and removal from scalp EEG: A review. Neurophysiologie Clinique/Clinical Neurophysiology 2016;46:287–305. https://doi.org/10.1016/J.NEUCLI.2016.07.002

    work page doi:10.1016/j.neucli.2016.07.002 2016

  7. [7]

    EEG signal processing and feature extraction

    Hu L, Zhang Z. EEG signal processing and feature extraction. Springer Singapore; 2019. http s://doi.org/10.1007/978- 981-13-9113-2/COVER

    doi:10.1007/978- 2019

  8. [8]

    Removal of Artifacts from EEG Signals: A Review

    Jiang X, Bian G Bin, Tian Z. Removal of Artifacts from EEG Signals: A Review. Sensors (Basel) 2019;19. https://doi.org/10.3390/S19050987

    doi:10.3390/s19050987 2019

Show all 93 references
  1. [10]

    Identification and removal of physiological artifacts from electroencephalogram signals: A review

    Mannan MMN, Kamran MA, Jeong MY. Identification and removal of physiological artifacts from electroencephalogram signals: A review. IEEE Access 2018;6:30630–52. https://doi.org/10.1109/ACCESS.2018.2842082

    2018

  2. [11]

    Change: detection and modification

    Montgomery DD. Change: detection and modification. Appl Psychophysiol Biofeedback 2001;26:215 –26. https://doi.org/10.1023/A:1011350204547

    2001 doi

  3. [12]

    Neurofeedback and Basic Learning Theory: Implications for Research and Practice

    Sherlin LH, Arns M, Lubar J, Heinrich H, Kerson C, Strehl U, et al. Neurofeedback and Basic Learning Theory: Implications for Research and Practice. J Neurother 2011;15:292–304. https://doi.org/10.1080/10874208.2011.623089

    2011

  4. [13]

    EEG is better left alone

    Delorme A. EEG is better left alone. Scientific Reports 2023 13:1 2023;13:1–12. https://doi.org/10.1038/s41598-023- 27528-0

    2023 doi

  5. [14]

    PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals

    Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 2000;101. https://doi.org/10.1161/01.CIR.101.23.E215

    doi

  6. [15]

    The Human Connectome Project: a data acquisition perspective

    Van Essen DC, Ugurbil K, Auerbach E, Barch D, Behrens TEJ, Bucholz R, et al. The Human Connectome Project: a data acquisition perspective. Neuroimage 2012;62:2222–31. https://doi.org/10.1016/J.NEUROIMAGE.2012.02.018

    2012 doi

  7. [16]

    The Temple University Hospital EEG Data Corpus

    Obeid I, Picone J. The Temple University Hospital EEG Data Corpus. Front Neurosci 2016;10:196. https://doi.org/10.3389/FNINS.2016.00196

    2016

  8. [17]

    Removal of Muscle Artifacts from the EEG: A Review and Recommendations

    Chen X, Xu X, Liu A, Lee S, Chen X, Zhang X, et al. Removal of Muscle Artifacts from the EEG: A Review and Recommendations. IEEE Sens J 2019;19:5353–68. https://doi.org/10.1109/JSEN.2019.2906572

    2019

  9. [18]

    Deep learning denoising for EOG artifacts removal from EEG signals

    Mashhadi N, Khuzani AZ, Heidari M, Khaledyan D. Deep learning denoising for EOG artifacts removal from EEG signals. 2020 IEEE Global Humanitarian Technology Conference, GHTC 2020 2020. https://doi.org/10.1109/GHTC46280.2020.9342884

    2020

  10. [19]

    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. https://doi.org/10.1016/j.bspc.2021.102741

    2021

  11. [20]

    Wavelet Transform, Reconstructed Phase Space, and Deep Learning Neural Networks for EEG - Based Schizophrenia Detection

    Al Fahoum A, Zyout A. Wavelet Transform, Reconstructed Phase Space, and Deep Learning Neural Networks for EEG - Based Schizophrenia Detection. Https://DoiOrg/101142/S0129065724500461 2024;34. https://doi.org/10.1142/S0129065724500461

    2024 doi

  12. [21]

    Early detection of neurological abnormalities using a combined phase space reconstruction and deep learning approach

    Al Fahoum A, Zyout A. Early detection of neurological abnormalities using a combined phase space reconstruction and deep learning approach. Intell Based Med 2023;8:100123. https://doi.org/10.101 6/J.IBMED.2023.100123

    2023

  13. [22]

    Real -time ocular artifacts removal of EEG data using a hybrid ICA-ANC approach

    Jafarifarmand A, Badamchizadeh MA, Khanmohammadi S, Nazari MA, Tazehkand BM. Real -time ocular artifacts removal of EEG data using a hybrid ICA-ANC approach. Biomed Signal Process Control 2017;31:199–210. https://doi.org/10.1016/J.BSPC.2016.08.006

    2017 doi

  14. [24]

    A hybrid method for artifact removal of visual evoked EEG

    Sheela P, Puthankattil SD. A hybrid method for artifact removal of visual evoked EEG. J Neurosci Methods 2020;336:108638. https://doi.org/10.1016/J.JNEUMETH.2020.108638

    2020

  15. [25]

    Automatic Electroe ncephalogram Artifact Removal Using Deep Convolutional Neural Networks

    Lopes F, Leal A, Medeiros J, Pinto MF, Dourado A, Dumpelmann M, et al. Automatic Electroe ncephalogram Artifact Removal Using Deep Convolutional Neural Networks. IEEE Access 2021;9:149955 –70. https://doi.org/10.1109/ACCESS.2021.3125728

    2021

  16. [26]

    Automatic ocular artifacts removal in EEG using deep learning

    Yang B, Duan K, Fan C, Hu C, Wang J. Automatic ocular artifacts removal in EEG using deep learning. Biomed Signal Process Control 2018;43:148–58. https://doi.org/10.1016/J.BSPC.2018.02.021

    2018 doi

  17. [27]

    AnEEG: leveraging deep learning for effective artifact removal in EEG data

    Kalita B, Deb N, Das D. AnEEG: leveraging deep learning for effective artifact removal in EEG data. Scientific Reports 2024 14:1 2024;14:1–20. https://doi.org/10.1038/s41598-024-75091-z

    2024 doi

  18. [28]

    The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks

    Carlini N, Liu C, Erlingsson Ú, Kos J, Song D. The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks. Proceedings of the 28th USENIX Security Symposium 2018:267–84

    2018

  19. [29]

    Machine learning models that remember too much

    Song C, Ristenpart T, Shmatikov V. Machine learning models that remember too much. Proceedings of the ACM Conference on Computer and Communications Security 2017:587–601. https://doi.org/10.1145/3133956.3134077/SUPPL_FILE/CONGZHENGSONG-MACHINELEARNING.MP4

    2017

  20. [30]

    Cross-Dataset Variability Problem in EEG Decoding With Deep Learning

    Xu L, Xu M, Ke Y, An X, Liu S, Ming D. Cross-Dataset Variability Problem in EEG Decoding With Deep Learning. Front Hum Neurosci 2020;14. https://doi.org/10.3389/FNHUM.2020.00103

    2020

  21. [31]

    Artifact removal in physiological signals--practices and possibilities

    Sweeney KT, Ward TE, McLoone SF. Artifact removal in physiological signals--practices and possibilities. IEEE Trans Inf Technol Biomed 2012;16:488–500. https://doi.org/10.1109/TITB.2012.2188536

    2012

  22. [32]

    Eye movement artifact in the CNV

    Hillyard SA, Galambos R. Eye movement artifact in the CNV. Electroencephalogr Clin Neurophysiol 1970;28:173–82. https://doi.org/10.1016/0013-4694(70)90185-9. 18

    1970 doi

  23. [33]

    Adaptive filtering for ECG rejection from surface EMG recordings

    Marque C, Bisch C, Dantas R, Elayoubi S, Brosse V, Pérot C. Adaptive filtering for ECG rejection from surface EMG recordings. Journal of Electromyography and Kinesiology 2005;15:310–5. https://doi.org/10.1016/J.JELEKIN.2004.10.001

    2005 doi

  24. [34]

    A generic EEG artifact removal algorithm based on the multi -channel Wiener filter

    Somers B, Francart T, Bertrand A. A generic EEG artifact removal algorithm based on the multi -channel Wiener filter. J Neural Eng 2018;15:036007. https://doi.org/10.1088/1741-2552/AAAC92

    2018 doi

  25. [35]

    Independent Component Analysis of Electroencephalographic Data

    Makeig S, Jung T-P, Bell AJ, Sejnowski TJ. Independent Component Analysis of Electroencephalographic Data. Adv Neural Inf Process Syst 1995;8

    1995

  26. [36]

    AUTOMATIC ARTIFACT REJECTION FOR EEG DATA USING HIGH-ORDER STATISTICS AND INDEPENDENT COMPONENT ANALYSIS 2001

    Delorme A, Makeig S, Sejnowski T, Pines NT. AUTOMATIC ARTIFACT REJECTION FOR EEG DATA USING HIGH-ORDER STATISTICS AND INDEPENDENT COMPONENT ANALYSIS 2001

    2001

  27. [37]

    Characterizing nonlinear relationships in functional imaging data using eigenspace maximal information canonical correlation analysis (emiCCA)

    Dong L, Zhang Y, Zhang R, Zhang X, Gong D, Valdes-Sosa PA, et al. Characterizing nonlinear relationships in functional imaging data using eigenspace maximal information canonical correlation analysis (emiCCA). Neuroimage 2015;109:388–401. https://doi.org/10.1016/J.NEUROIMAGE.2...

    2015 doi

  28. [38]

    A canonical correlation approach to exploratory data analysis in fMRI

    Borga M, Friman O, … PL-P of the, 2002 undefined. A canonical correlation approach to exploratory data analysis in fMRI. CdsIsmrmOrgM Borga, O Friman, P Lundberg, H KnutssonProceedings of the ISMRM Annual Meeting, Honolulu, Hawaii, 2002•cdsIsmrmOrg n.d

    2002

  29. [39]

    A novel approach for the removal of artifacts in EEG signals

    Lakshmi KGA, Surling SNN, Sheeba O. A novel approach for the removal of artifacts in EEG signals. Proceedings of the 2017 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2017 2017;2018-January:2595–9. https://doi.org/10.1109/WISPN...

    2017

  30. [40]

    Dipole modelling of eye activity and its application to the removal of eye arte facts from the EEG and MEG

    Berg P, Scherg M. Dipole modelling of eye activity and its application to the removal of eye arte facts from the EEG and MEG. Clin Phys Physiol Meas 1991;12 Suppl A:49–54. https://doi.org/10.1088/0143-0815/12/A/010

    1991 doi

  31. [41]

    Empirical mode decomposition and blind source separation methods for antijamming with GPS signals

    Kamath V, Lai YC, Zhu L, Urval S. Empirical mode decomposition and blind source separation methods for antijamming with GPS signals. Record - IEEE PLANS, Position Location and Navigation Symposium 2006;2006:335 –

    2006

  32. [42]

    https://doi.org/10.1109/PLANS.2006.1650620

    2006 arXiv

  33. [43]

    Removal of muscle artifact from EEG data: Comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet -based) approaches

    Safieddine D, Kachenoura A, Albera L, Birot G, Karfoul A, Pasnicu A, et al. Removal of muscle artifact from EEG data: Comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet -based) approaches. EURASIP J Adv Signal Process 2012;2012:1–15. https://doi.org...

    2012 doi

  34. [44]

    The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis

    Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. RSPSA 1998;454:903–98. https://doi.org/10.1098/RSPA.1998.0193

    1998

  35. [45]

    Artifact suppression from EEG signals using data adaptive time domain filtering

    Molla MKI, Rabiul Islam M, Tanaka T, Rutkowski TM. Artifact suppression from EEG signals using data adaptive time domain filtering. Neurocomputing 2012;97:297–308. https://doi.org/10.1016/J.NEUCOM.2012.05.009

    2012 doi

  36. [46]

    Artifacts-matched blind source separation and wavelet transform for multichannel EEG denoising

    Rakibul Mowla M, Ng SC, Zilany MSA, Paramesran R. Artifacts-matched blind source separation and wavelet transform for multichannel EEG denoising. Biomed Signal Process Control 2015;22:111–8. https://doi.org/10.1016/J.BSPC.2015.06.009

    2015 doi

  37. [47]

    Online artifact removal for brain‐ computer interfaces using support vector machines and blind source separation

    Halder S, Bensch M, Urgen Mellinger J¨, Bogdan M, Ubler AK¨, Birbaumer N, et al. Online artifact removal for brain‐ computer interfaces using support vector machines and blind source separation. Wiley Online LibraryS Halder, M Bensch, J Mellinger, M Bogdan, A Kübler, N Birbaum...

    2007 doi

  38. [48]

    Ocular reduction in EEG signals based on adaptive filtering, regression and blind source separation

    Romero S, Mañanas MA, Barbanoj MJ. Ocular reduction in EEG signals based on adaptive filtering, regression and blind source separation. Ann Biomed Eng 2009;37:176–91. https://doi.org/10.1007/S10439-008-9589-6/METRICS

    2009 doi

  39. [49]

    Brainstorm: a user-friendly application for MEG/EEG analysis

    Tadel F, Baillet S, Mosher JC, Pantazis D, Leahy RM. Brainstorm: a user-friendly application for MEG/EEG analysis. Comput Intell Neurosci 2011;2011. https://doi.org/10.1155/2011/879716

    2011 doi

  40. [50]

    FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data

    Oostenveld R, Fries P, Maris E, Schoffelen JM. FieldTrip: Open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput Intell Neurosci 2011;2011. https://doi.org/10.1155/2011/156869

    2011 doi

  41. [51]

    EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

    Delorme A, Makeig S. EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 2004;134:9 –21. https://doi.org/10.1016/j.jneumeth.2003.10.009

    2004 doi

  42. [52]

    MEG and EEG data analysis with MNE-Python

    Gramfort A, Luessi M, Larson E, Engemann DA, Strohmeier D, Brodbeck C, et al. MEG and EEG data analysis with MNE-Python. Front Neurosci 2013;7. https://doi.org/10.3389/FNINS.2013.00267

    2013

  43. [53]

    FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection

    Nolan H, Whelan R, Reilly RB. FASTER: Fully Automated Statistical Thresholding for EEG artifact Rejection. J Neurosci Methods 2010;192:152–62. https://doi.org/10.1016/J.JNEUMETH.2010.07.015

    2010 doi

  44. [54]

    The PREP pipeline: standardized preprocessing for large - scale EEG analysis

    Bigdely-Shamlo N, Mullen T, Kothe C, Su KM, Robbins KA. The PREP pipeline: standardized preprocessing for large - scale EEG analysis. Front Neuroinform 2015;9:1–19. https://doi.org/10.3389/FNINF.2015.00016

    2015

  45. [55]

    Autoreject: Automated artifact rejection for MEG and EEG data

    Jas M, Engemann DA, Bekhti Y, Raimondo F, Gramfort A. Autoreject: Automated artifact rejection for MEG and EEG data. Neuroimage 2017;159:417–29. https://doi.org/10.1016/J.NEUROIMAGE.2017.06.030

    2017 doi

  46. [56]

    Reliable and fast automatic artifact rejection of Long -Term EEG recordings based on Isolation Forest

    Zhang R, Rong R, Gan JQ, Xu Y, Wang H, Wang X. Reliable and fast automatic artifact rejection of Long -Term EEG recordings based on Isolation Forest. Med Biol Eng Comput 2024;62:521 –35. https://doi.org/10.1007/S11517-023- 02961-5

    2024 doi

  47. [58]

    Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review

    Congedo M, Barachant A, Bhatia R. Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review. Brain-Computer Interfaces 2017;4:155–74. https://doi.org/10.1080/2326263X.2017.1297192

    2017

  48. [59]

    The Riemannian Potato: an automatic and adaptive artifact detection method for online experiments using Riemannian geometry

    Barachant A, Andreev A, Congedo M. The Riemannian Potato: an automatic and adaptive artifact detection method for online experiments using Riemannian geometry. TOBI Workshop lV, Sion, Switzerland: 2013, p. 19 –20. 19

    2013

  49. [60]

    Mayaud L, Cabanilles S, Van Langhenhove A, Congedo M, Barachant A, Pouplin S, et al. Brain -computer interface for the communication of acute patients: a feasibility study and a randomized controlled trial comparing performance with healthy participants and a traditional assis...

    2016

  50. [61]

    Recreational Applications of OpenViBE: Brain Invaders and Use-the- Force

    Andreev A, Barachant A, Lotte F, Congedo M. Recreational Applications of OpenViBE: Brain Invaders and Use-the- Force. Brain-Computer Interfaces 2: Technology and Applications 2016:241–58. https://doi.org/10.1002/9781119332428.CH14

    2016 doi

  51. [62]

    A neurophysiological training evaluation metric for air traffic management

    Borghini G, Aricò P, Ferri F, Graziani I, Pozzi S, Napoletano L, et al. A neurophysiological training evaluation metric for air traffic management. Annu Int Conf IEEE Eng Med Biol Soc 2014;2014:3005 –8. https://doi.org/10.1109/EMBC.2014.6944255

    2014

  52. [63]

    Epilepsy Detection Based on Riemann Potato in Noisy Environment

    Ru Y, Li J, Wei Z. Epilepsy Detection Based on Riemann Potato in Noisy Environment. Appl Bionics Biomech 2022;2022. https://doi.org/10.1155/2022/8311249

    2022 doi

  53. [64]

    From Euclidean to Rieman nian Means: Information Geometry for SSVEP Classification

    Kalunga EK, Chevallier S, Barthélemy Q, Djouani K, Hamam Y, Monacelli E. From Euclidean to Rieman nian Means: Information Geometry for SSVEP Classification. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bio...

    2015 doi

  54. [65]

    EEG Source Analysis

    Congedo M. EEG Source Analysis. Université de Grenoble; 2013

    2013

  55. [66]

    The Riemannian Potato Field: A Tool for Online Signal Quality Index of EEG

    Barthelemy Q, Mayaud L, Ojeda D, Congedo M. The Riemannian Potato Field: A Tool for Online Signal Quality Index of EEG. IEEE Trans Neural Syst Rehabil Eng 2019;27:244–55. https://doi.org/10.1109/TNSRE.2019.2893113

    2019

  56. [68]

    Finding a “kneedle” in a haystack: Dete cting knee points in system behavior

    Satopää V, Albrecht J, Irwin D, Raghavan B. Finding a “kneedle” in a haystack: Dete cting knee points in system behavior. Proc Int Conf Distrib Comput Syst 2011:166–71. https://doi.org/10.1109/ICDCSW.2011.20

    2011 doi

  57. [69]

    Riemannian-Artifact-Rejection 2024

    Hajhassani D, Congedo M. Riemannian-Artifact-Rejection 2024. https://github.com/Davoud-Hajhassani/Riemannian- Artifact-Rejection (accessed January 15, 2025)

    2024

  58. [70]

    An Automatic Riemannian Artifact Rejection Method for P300 -based BCIs

    Hajhassani D, Mattout J, Congedo M. An Automatic Riemannian Artifact Rejection Method for P300 -based BCIs. 2024 32nd European Signal Processing Conference (EUSIPCO) 2024:1616 –20. https://doi.org/10.23919/EUSIPCO63174.2024.10715349

    2024

  59. [71]

    Introduction to Statistical Pattern Recognition

    Keinosuke F. Introduction to Statistical Pattern Recognition. Introduction to Statistical Pattern Recognition 1990. https://doi.org/10.1016/C2009-0-27872-X

    1990 doi

  60. [73]

    Riemannian Approaches in Brain-Computer Interfaces: A Review

    Yger F, Berar M, Lotte F. Riemannian Approaches in Brain-Computer Interfaces: A Review. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2017;25:1753–62. https://doi.org/10.1109/TNSRE.2016.2627016

    2017

  61. [74]

    Riemannian Geometry and Geometric Analysis 2017

    Jost J. Riemannian Geometry and Geometric Analysis 2017. https://doi.org/10.1007/978 -3-319-61860-9

    2017 doi

  62. [75]

    A Differential Geometric Approach to the Geometric Mean of Symmetric Positive -Definite Matrices

    Moakher M. A Differential Geometric Approach to the Geometric Mean of Symmetric Positive -Definite Matrices. SIAM Journal on Matrix Analysis and Applications 2005;26:735–47. https://doi.org/10.1137/S0895479803436937

    2005 doi

  63. [76]

    Principal geodesic analysis for the study of nonlinear statistics of shape

    Fletcher PT, Lu C, Pizer SM, Joshi S. Principal geodesic analysis for the study of nonlinear statistics of shape. IEEE Trans Med Imaging 2004;23:995–1005. https://doi.org/10.1109/TMI.2004.831793

    2004

  64. [77]

    Approximate joint diagonalization and geometric mean of symmetric positive definite matrices

    Congedo M, Afsari B, Barachant A, Moakher M. Approximate joint diagonalization and geometric mean of symmetric positive definite matrices. PLoS One 2015;10. https://doi.org/10.1371/JOURNAL.PONE.0121423

    2015 doi

  65. [78]

    Fixed Point Algorithms for Estimating Power Means of Positive Definite Matrices

    Congedo M, Barachant A, Koopaei EK. Fixed Point Algorithms for Estimating Power Means of Positive Definite Matrices. IEEE Transactions on Signal Processing 2017;65:2211–20. https://doi.org/10.1109/TSP.2017.2649483

    2017

  66. [79]

    pyRiemann/pyRiemann: v0.7 2024

    barachant alexandre, Barthélemy Q, Berg GW vom, Gramfort A, KING J -R, Rodrigues PLC, et al. pyRiemann/pyRiemann: v0.7 2024. https://doi.org/10.5281/ZENODO.13887520

    2024 doi

  67. [80]

    Global field power measurement versus classical method in the determination of the latency of evoked potential components

    Hamburger HL, Michelle MAG. Global field power measurement versus classical method in the determination of the latency of evoked potential components. Brain Topogr 1991;3:391–6. https://doi.org/10.1007/BF01129642

    1991 doi

  68. [81]

    Global field power and topographic similarity

    Skrandies W. Global field power and topographic similarity. Brain Topogr 1990;3:137–41. https://doi.org/10.1007/BF01128870

    1990 doi

  69. [82]

    Using Riemannian geometry for SSVEP -based Brain Computer Interface

    Kalunga EK, Chevallier S, Barthelemy Q. Using Riemannian geometry for SSVEP -based Brain Computer Interface. Neurocomputing 2015;191:55–68. https://doi.org/10.1016/j.neucom.2016.01.007

    2015 doi

  70. [83]

    Choosing between methods of combining p-values

    Heard NA, Rubin-Delanchy P. Choosing between methods of combining p-values. Biometrika 2018;105:239–46. https://doi.org/10.1093/BIOMET/ASX076

    2018 doi

  71. [84]

    10/20, 10/10, and 10/5 systems revisited: their validity as relat ive head-surface-based positioning systems

    Jurcak V, Tsuzuki D, Dan I. 10/20, 10/10, and 10/5 systems revisited: their validity as relat ive head-surface-based positioning systems. Neuroimage 2007;34:1600–11. https://doi.org/10.1016/J.NEUROIMAGE.2006.09.024

    2007 doi

  72. [85]

    A comparison of ocular artifact removal methods for block design based electroencephalography experiments 2017:236–41

    Kobler R, Sburlea AI, Müller-Putz G. A comparison of ocular artifact removal methods for block design based electroencephalography experiments 2017:236–41. https://doi.org/10.3217/978-3-85125-533-1-44

    2017 doi

  73. [86]

    The effects of ocular artifacts on (lateralized) broadband power in the EEG

    Hagemann D, Naumann E. The effects of ocular artifacts on (lateralized) broadband power in the EEG. Clin Neurophysiol 2001;112:215–31. https://doi.org/10.1016/S1388-2457(00)00541-1

    2001 doi

  74. [87]

    Effects of eyelid closure, blinks, and eye movements on the electroencephalogram

    Iwasaki M, Kellinghaus C, Alexopoulos A V., Burgess RC, Kumar AN, Han YH, et al. Effects of eyelid closure, blinks, and eye movements on the electroencephalogram. Clinical Neurophysiology 2005;116:878 –85. https://doi.org/10.1016/J.CLINPH.2004.11.001. 20

    2005 doi

  75. [88]

    EMG contamination of EEG: Spectral and topographical characteristics

    Goncharova II, McFarland DJ, Vaughan TM, Wolpaw JR. EMG contamination of EEG: Spectral and topographical characteristics. Clinical Neurophysiology 2003;114:1580–93. https://doi.org/10.1016/S1388-2457(03)00093-2

    2003 doi

  76. [89]

    Tuning characteristics of low-frequency EEG to positions and velocities in visuomotor and oculomotor tracking tasks

    Kobler RJ, Sburlea AI, Müller-Putz GR. Tuning characteristics of low-frequency EEG to positions and velocities in visuomotor and oculomotor tracking tasks. Scientific Reports 2018 8:1 2018;8:1 –14. https://doi.org/10.1038/s41598-018- 36326-y

    2018 doi

  77. [90]

    Continuous low-frequency EEG decoding of arm movement for closed-loop, natural control of a robotic arm

    Mondini V, Kobler RJ, Sburlea AI, Müller-Putz GR. Continuous low-frequency EEG decoding of arm movement for closed-loop, natural control of a robotic arm. J Neural Eng 2020;17. https://doi.org/10.1088/1741 -2552/ABA6F7

    2020 doi

  78. [91]

    The Temple University Artifact Corpus: An Annotated Corpus of EEG Artifacts

    Hamid A, Gagliano K, Rahman S, Tulin N, Tchiong V, Obeid I, et al. The Temple University Artifact Corpus: An Annotated Corpus of EEG Artifacts. 2020 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2020 - Proceedings 2020. https://doi.org/10.1109/SPMB50085.2020.9353647

    2020

  79. [92]

    Recent Advances in the TUH EEG Corpus: Improving the Interrater Agreement for Artifacts and Epileptiform Events

    Buckwalter G, Chhin S, Rahman S, Obeid I, Picone J. Recent Advances in the TUH EEG Corpus: Improving the Interrater Agreement for Artifacts and Epileptiform Events. 2021 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2021 - Proceedings 2021. https://doi.org/10....

    2021

  80. [93]

    Corneo-retinal-dipole and eyelid-related eye artifacts can be corrected offline and online in electroencephalographic and magnetoencephalographic signals

    Kobler RJ, Sburlea AI, Lopes-Dias C, Schwarz A, Hirata M, Müller-Putz GR. Corneo-retinal-dipole and eyelid-related eye artifacts can be corrected offline and online in electroencephalographic and magnetoencephalographic signals. Neuroimage 2020;218:117000. https://doi.org/10.1...

    2020

  81. [94]

    PythonCall.jl: Python and Julia in harmony

    Rowley C. PythonCall.jl: Python and Julia in harmony. 2022. https://github.com/JuliaPy/PythonCall.jl (accessed January 15, 2025)

    2022

  82. [95]

    Enhanced Cardiac Arrhythmia Detection Utilizing Deep Learning Architectures and Multi -Scale ECG Analysis

    Fahoum AS Al. Enhanced Cardiac Arrhythmia Detection Utilizing Deep Learning Architectures and Multi -Scale ECG Analysis. Tuijin Jishu/Journal of Propulsion Technology 2023;44:5539 –48

    2023

  83. [96]

    Resampling-based multiple testing : examples and methods for P-value adjustment 1993:340

    Westfall PH., Young SStanley. Resampling-based multiple testing : examples and methods for P-value adjustment 1993:340

    1993

  84. [97]

    PermutationTests.jl: Univariate and multiple comparisons statistical hypothesis testing by data permutation 2024

    Congedo M, Finos L. PermutationTests.jl: Univariate and multiple comparisons statistical hypothesis testing by data permutation 2024. https://github.com/Marco-Congedo/PermutationTests.jl (accessed January 15, 2025)

    2024

  85. [98]

    Statistical Power Analysis for the Behavioral Sciences Second Edition

    Cohen J. Statistical Power Analysis for the Behavioral Sciences Second Edition. 1988. Fig. 1: 2D projection of the z-score map for a Riemannian potato, shown for two selected channels (EOGL1, EOGR1) across 66 epochs from an EYEAR database (R. Kobler et al., 2017), along with t...

    1988

Pith tools

Reviewed August 4, 2026 · model on record in the stance chip above.