Papers for

eeg system developers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Interpretability of sparse autoencoder features in EEG signals tested

When Is an SAE Feature Interpretable? A Validation Ladder for EEG Foundation Models

Abstract: Sparse autoencoders (SAEs) decompose dense model activations into discrete latents, making individual features easy to interpret--and easy to misinterpret. In EEG foundation models, this creates a tempting inference: if removing alpha-band activity strongly changes a latent's activation, one might conclude that the latent represents alpha activity. Across 27 settings spanning three backbones, three EEG datasets, and three network depths, this interpretation initially appears compelling: alpha removal changes latent firing 7.3 times more than an equal-width sham notch (95% CI [6.2, 8.7], bootstrapped over settings). However, the alpha filter also deletes far more signal than the sham. After normalizing by removed spectral energy, the ratio falls to 0.28 (95% CI [0.22, 0.36]) and exceeds one in none of the 27 settings. Latents selected for their response to alpha removal are, on clean EEG, slightly anti-correlated with relative alpha power (mean r = -0.073), giving no support for a simple alpha-detector reading. Motivated by this failure case, we propose a validation ladder for semantic interpretations of SAE latents: it asks in turn whether a latent responds, whether that response survives controlling for how much signal the intervention removes, whether it is specific rather than broadly fragile, and whether the proposed property is visible on unperturbed data--while separately testing the stronger claim that the latent matters to a task classifier. Perturbation sensitivity alone does not establish what an SAE latent represents.

Mon 28 SeptMachine Learning
The gist
Understanding what specific parts of a brain signal model represent is tricky. The authors found that simply seeing a model feature respond when a brain rhythm is filtered out doesn't mean that feature truly represents that rhythm. They showed that many effects come from simply removing a lot of signal, not because the feature directly detects a specific brain wave. They suggest a step-by-step test to better check if features really map to meaningful brain activity.
Open → 2609.34091v1

EEG model tuning improvements vary by dataset with added modules

Dataset-Dependent Effects of Cross-Depth Aggregation and Soft-Routed Experts in EEG Foundation Model Fine-Tuning

Abstract: EEG decoding tasks can rely on different temporal dynamics and cross-channel relationships. We test whether specialized modules improve a fully fine-tuned EEG foundation model by augmenting CBraMod with cross-depth Attention Residuals (AttnRes) and two soft-routed expert banks. Across matched three-seed experiments on FACED, ISRUC, SEED-V, and PhysioNet-MI, the complete model changes mean balanced accuracy relative to full fine-tuning by -0.12, +1.27, +0.77, and -1.27 points, respectively. AttnRes alone improves mean balanced accuracy on three datasets, whereas adding experts on top of AttnRes helps only FACED and SEED-V. These gains come with substantial overhead: AttnRes requires 2.11 to 2.88x runtime and 1.78 to 2.67x memory, while the complete model requires 2.41 to 3.04x runtime and 1.86 to 2.85x memory. Overall, the added modules produce dataset-dependent, sometimes opposing effects rather than consistent gains over full fine-tuning.

Tue 15 SeptMachine Learning
The gist
Different brainwave decoding tasks rely on different patterns over time and between channels. The authors tested if adding special attention and expert modules to a brainwave analysis model helps improve accuracy when fine-tuning it. They found that these additions sometimes help but can also hurt performance depending on the dataset. The new modules require much more computing power and memory, so their benefits are not consistent across tasks.
Open → 2609.17886v1