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Evidence record 17396 · automatically gathered

Rectifying Geometric Misalignment: Online Source-Free Adaptation for Class-Imbalanced EEG

Electroencephalography (EEG) based Brain-Computer Interfaces (BCIs) often require unsupervised domain adaptation (UDA) to generalize across subjects and sessions. While Riemannian alignment methods like the Riemannian Centering Transformation (RCT) are effective for handling covariate shifts, they implicitly assume balanced class priors. However, in realistic online BCI scenarios, the label distributions vary dynamically (label shift), causing standard alignment techniques to geometrically misal

Record details

Published: 5 August 2026
Source: arXiv cs.LG
Category: Research
Topics: Safety & alignment
Retrieved: 7 August 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (5 August 2026), “Rectifying Geometric Misalignment: Online Source-Free Adaptation for Class-Imbalanced EEG,” evidence record 17396, https://ethics.ai/record/17396 (originally published by arXiv cs.LG).

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