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[2510.08059] Mitigating Subject Dependency in EEG Decoding with Subject-Specific Low-Rank Adapters
Summary
The paper introduces Subject-Specific Low-Rank Adapters (SuLoRA) to enhance EEG decoding by addressing subject dependency, improving model performance with fewer parameters.
Why It Matters
This research is significant as it tackles a critical challenge in brain signal decoding—subject variability. By proposing a method that maintains model robustness without extensive redesign, it opens avenues for more effective cross-subject applications in neuroscience and machine learning.
Key Takeaways
- SuLoRA effectively mitigates subject dependency in EEG decoding.
- The method outperforms traditional models with fewer parameters.
- It enables existing architectures to adapt without major redesigns.
- Demonstrated effectiveness on MEG and EEG tasks.
- Offers a practical solution for developing cross-subject foundation models.
Computer Science > Machine Learning arXiv:2510.08059 (cs) [Submitted on 9 Oct 2025 (v1), last revised 20 Feb 2026 (this version, v2)] Title:Mitigating Subject Dependency in EEG Decoding with Subject-Specific Low-Rank Adapters Authors:Timon Klein, Piotr Minakowski, Sebastian Sager, Steffen Schotthöfer View a PDF of the paper titled Mitigating Subject Dependency in EEG Decoding with Subject-Specific Low-Rank Adapters, by Timon Klein and 2 other authors View PDF HTML (experimental) Abstract:Subject-specific distribution shifts represent a fundamental obstacle to developing foundation models for brain decoding. We propose the Subject-Specific Low-Rank Adapter (SuLoRA), a drop-in replacement for standard linear or convolutional layers that captures inter-subject variability by decomposing weights into a shared, subject-invariant component and a lightweight, low-rank correction unique to each subject. This explicit separation enables existing architectures to become robust to subject shifts without architectural redesign. We evaluate SuLoRA on MEG speech perception and EEG motor imagery tasks across CNN and transformer architectures. In the speech decoding task, SuLoRA exceeds the baseline performance with half of the parameters. On motor imagery dataset, SuLoRA outperforms both subject-agnostic models and independently trained subject-specific models. SuLoRA offers a practical path towards effective cross-subject foundation models for brain signal applications. Subjects: Machin...
