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GOPSA EEG Domain Adaptation Method — Poster

A conference poster presenting GOPSA, a method for multi-source domain adaptation on EEG data using Riemannian geometry. It compares GOPSA against baselines like D0 Intercept on the HarMnqEEG dataset, showing improved performance in brain age prediction.

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Paper title: Geodesic Optimization for Predictive Shift Adaptation on EEG data Abstract: A conference poster presenting GOPSA, a method for multi-source domain adaptation on EEG data using Riemannian geometry. It compares GOPSA against baselines like D0 Intercept on the HarMnqEEG dataset, showing improved performance in brain age prediction. Paper body (method & results): <!DOCTYPE html> <html lang="en"> <head> <meta content="text/html; charset=utf-8" http-equiv="content-type"/> <title>Geodesic Optimization for Predictive Shift Adaptation on EEG data</title>  <meta content="width=device-width, initial-scale=1, shrink-to-fit=no" name="viewport"/> <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/css/bootstrap.min.css" rel="stylesheet" type="text/css"/> <link href="/static/browse/0.3.4/css/ar5iv.0.7.9.min.css" rel="stylesheet" type="text/css"/> <link href="/static/browse/0.3.4/css/ar5iv-fonts.0.7.9.min.css" rel="stylesheet" type="text/css"/> <link href="/static/browse/0.3.4/css/latexml_styles.css" rel="stylesheet" type="text/css"/> <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/js/bootstrap.bundle.min.js"></script> <script src="https://cdnjs.cloudflare.com/ajax/libs/html2canvas/1.3.3/html2canvas.min.js"></script> <script src="/static/browse/0.3.4/js/addons_new.js"></script> <script src="/static/browse/0.3.4/js/feedbackOverlay.js"></script> <base href="/html/2407.03878v1/"/></head> <body> <nav class="ltx_page_navbar"> <nav class="ltx_TOC"> <ol class="ltx_toclist"> <li class="ltx_tocentry ltx_tocentry_paragraph"><a class="ltx_ref" href="https://arxiv.org/html/2407.03878v1#S0.SS0.SSS0.Px1" title="In Geodesic Optimization for Predictive Shift Adaptation on EEG data">Keywords:</a></li> <li class="ltx_tocentry ltx_tocentry_section"> <a class="ltx_ref" href="https://arxiv.org/html/2407.03878v1#S1" title="In Geodesic Optimization for Predictive Shift Adaptation on EEG data">1 Introduction</a> <ol class="ltx_toclist ltx_toclist_section"> <li class="ltx_tocentry ltx_tocentry_paragraph"><a class="ltx_ref" href="https://arxiv.org/html/2407.03878v1#S1.SS0.SSS0.Px1" title="In 1 Introduction ‣ Geodesic Optimization for Predictive Shift Adaptation on EEG data">Related work</a></li> </ol> </li> <li class="ltx_tocentry ltx_tocentry_section"><a class="ltx_ref" href="https://arxiv.org/html/2407.03878v1#S2" title="In Geodesic Optimization for Predictive Shift Adaptation on EEG data">2 Regression modeling from covariance matrices using Riemannian geometry</a></li> <li class="ltx_tocentry ltx_tocentry_section"> <a class="ltx_ref" href="https://arxiv.org/html/2407.03878v1#S3" title="In Geodesic Optimization for Predictive Shift Adaptation on EEG data">3 Learning to recenter from highly shifted <math alttext="y" class="ltx_Math" display="inline"><semantics><mi>y</mi><annotation-xml encoding="MathML-Content"><ci>𝑦</ci></annotation-xml><annotation encoding="application/x-tex">y</annotation><annotation encoding="application/x-llamapun">italic_y</annotation></semantics></math> distributions with GOPSA</a> <ol class="ltx_toclist ltx_toclist_section"> <li class="ltx_tocentry ltx_tocentry_subsection"><a class="ltx_ref" href="https://arxiv.org/html/2407.03878v1#S3.SS1" title="In 3 Learning to recenter from highly shifted 𝑦 distributions with GOPSA ‣ Geodesic Optimization for Predictive Shift Adaptation on EEG data">3.1 Train-time</a></li> <li class="ltx_tocentry ltx_tocentry_subsection"><a class="ltx_ref" href="https://arxiv.org/html/2407.03878v1#S3.SS2" title="In 3 Learning to recenter from highly shifted 𝑦 distributions with GOPSA ‣ Geodesic Optimization for Predictive Shift Adaptation on EEG data">3.2 Test-time</a></li> </ol> </li> <li class="ltx_tocentry ltx_tocentry_section"> <a class="ltx_ref" href="https://arxiv.org/html/2407.03878v1#S4" title="In Geodesic Optimization for Predictive Shift Adaptation on EEG data">4 Empirical benchmarks</a> <ol class="ltx_toclist ltx_toclist_section"> <li class="ltx_tocentry ltx_tocentry_paragraph"><a class="ltx_ref" href="https://arxiv.org/html/2407.03878v1#S4.SS0.SSS0.Px1" title="In 4 Empirical benchmarks ‣ Geodesic Optimization for Predictive Shift Adaptation on EEG data">Results</a></li> <li class="ltx_tocentry ltx_tocentry_paragraph"><a class="ltx_ref" href="https://arxiv.org/html/2407.03878v1#S4.SS0.SSS0.Px2" title="In 4 Empirical benchmarks ‣ Geodesic Optimization for Predictive Shift Adaptation on EEG data">Model inspection</a></li> </ol> </li> <li class="ltx_tocentry ltx_tocentry_section"><a class="ltx_ref" href="https://arxiv.org/html/2407.03878v1#S5" title="In Geodesic Optimization for Predictive Shift Adaptation on EEG data">5 Conclusion</a></li> <li class="ltx_tocentry ltx_tocentry_appendix"> <a class="ltx_ref" href="https://arxiv.org/html/2407.03878v1#A1" title="In Geodesic Optimization for Predictive Shift Adaptation on EEG data">A Appendix</a> <ol class="ltx_toclist ltx_toclist_appendix"> <li class="ltx_tocentry ltx_tocentry_subsection"><a class="ltx_ref" href="https://arxiv.org/html/2407.03878v1#A1.SS1" title="In Appendix A Appendix ‣ Geodesic Optimization for Predictive Shift Adaptation on EEG data">A.1 Matrix operations</a></li> <li class="ltx_tocentry ltx_tocentry_subsection"><a class="ltx_ref" href="https://arxiv.org/html/2407.03878v1#A1.SS2" title="In Appendix A Appendix ‣ Geodesic Optimization for Predictive Shift Adaptation on EEG data">A.2 Proof of Lemma 2.1</a></li> <li class="ltx_tocentry ltx_tocentry_subsection"> <a class="ltx_ref" href="https://arxiv.org/html/2407.03878v1#A1.SS3" title="In Appendix A Appendix ‣ Geodesic Optimization for Predictive Shift Adaptation on EEG data">A.3 Cross-spectrum computation and preprocessing</a> <ol class="ltx_toclist ltx_toclist_subsection"> <li class="ltx_tocentry ltx_tocentry_paragraph"><a class="ltx_ref" href="https://arxiv.org/html/2407.03878v1#A1.SS3.SSS0.Px1" title="In A.3 Cross-spectrum computation and preprocessing ‣ Appendix A Appendix ‣ Geodesic Optimization for Predictive Shift Adaptation on EEG data">Bartlett estimator</a></li> <li class="ltx_tocentry ltx_tocentry_paragraph"><a class="ltx_ref" href="https://arxiv.org/html/2407.03878v1#A1.SS3.SSS0.Px2" title="In A.3 Cross-spectrum computation and preprocessing ‣ Appendix A Appendix ‣ Geodesic Optimization for Predictive Shift Adaptation on EEG data">Common average reference (CAR)</a></li> <li class="ltx_tocentry ltx_tocentry_paragraph"><a class="ltx_ref" href="https://arxiv.org/html/2407.03878v1#A1.SS3.SSS0.Px3" title="In A.3 Cross-spectrum computation and preprocessing ‣ Appendix A Appendix ‣ Geodesic Optimization for Predictive Shift Adaptation on EEG data">Global Scale Factor (GSF)</a></li> </ol> </li> <li class="ltx_tocentry ltx_tocentry_subsection"><a class="ltx_ref" href="https://arxiv.org/html/2407.03878v1#A1.SS4" title="In Appendix A Appendix ‣ Geodesic Optimization for Predictive Shift Adaptation on EEG data">A.4 Baselines</a></li> <li class="ltx_tocentry ltx_tocentry_subsection"><a class="ltx_ref" href="https://arxiv.org/html/2407.03878v1#A1.SS5" title="In Appendix A Appendix ‣ Geodesic Optimization for Predictive Shift Adaptation on EEG data">A.5 HarMNqEEG dataset</a></li> <li class="ltx_tocentry ltx_tocentry_subsection"><a class="ltx_ref" href="https://arxiv.org/html/2407.03878v1#A1.SS6" title="In Appendix A Appendix ‣ Geodesic Optimization for Predictive Shift Adaptation on EEG data">A.6 Figure 2 without normalization</a></li> <li class="ltx_tocentry ltx_tocentry_subsection"><a class="ltx_ref" href="https://arxiv.org/html/2407.03878v1#A1.SS7" title="In Appendix A Appendix ‣ Geodesic Optimization for Predictive Shift Adaptation on EEG data">A.7 Boxplots of each source-target sites for the three metrics</a></li> </ol> </li> </ol></nav> </nav> <div class="ltx_page_main"> <div class="ltx_page_content"> <article class="ltx_document ltx_authors_1line"> <h1 class="ltx_title ltx_title_document"> Geodesic Optimization for Predictive Shift Adaptation on EEG data </h1> <div class="ltx_authors"> Apolline Mellot1*, Antoine Collas1*, Sylvain Chevallier2, Alexandre Gramfort1, Denis A. Engemann3 1University Paris-Saclay, Inria, CEA, Palaiseau, France. 2TAU Inria, LISN-CNRS, University Paris-Saclay, France. 3Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann–La Roche Ltd., Basel, Switzerland. *Equal contribution. Email: apolline.mellot@inria.fr; antoine.collas@inria.fr </div> <div class="ltx_abstract"> <h6 class="ltx_title ltx_title_abstract">Abstract</h6> Electroencephalography (EEG) data is often collected from diverse contexts involving different populations and EEG devices. This variability can induce distribution shifts in the data <math alttext="X" class="ltx_Math" display="inline" id="id1.1.m1.1"><semantics id="id1.1.m1.1a"><mi id="id1.1.m1.1.1" xref="id1.1.m1.1.1.cmml">X</mi><ann

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Above I've shared:
(1) the full paper text,
(2) all paper figures labeled by figure number,
(3) the caption for the central poster figure I'm building.

TASK: This is a CONFERENCE POSTER. **NOT** an academic-paper figure.
Style requirements:

  - Multi-section layout with a clear poster structure: large title banner
    at the top with the paper title + author/affiliation strip, then 3-6
    distinct content panels arranged in columns or a grid.
  - Large legible fonts (text must be readable at 2 m viewing distance) —
    headings ≥ 60 pt visual size in the final image.
  - Use colour blocks / panel backgrounds to delineate sections (this is
    what makes it a poster, not a single-figure diagram).
  - Aspect ratio: portrait or landscape rectangle, NOT square.

If your output looks like a standard academic-paper figure (single panel,
no title banner, dense small text, no colour blocks), you've failed the
task. Render the COMPLETE poster, not just the central figure.

Just give me the final poster image.

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GOPSA EEG Domain Adaptation Method — Poster

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