Channel Adaptation for EEG Foundation Models: A Systematic Benchmark Across Architectures, Tasks, and Training Regimes

• URL: http://arxiv.org/abs/2604.23091v1
• Date: Sat, 25 Apr 2026 01:10:40 GMT
• Title: Channel Adaptation for EEG Foundation Models: A Systematic Benchmark Across Architectures, Tasks, and Training Regimes
• Abstract summary: Scaling EEG foundation models requires pooling data across heterogeneous electrode montages.<n>We present the first systematic comparison of four channel adaptation methods across five pretrained EEG foundation models.
• Score: 0.3199881502576702
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scaling EEG foundation models requires pooling data across heterogeneous electrode montages, a prerequisite both for larger pretraining corpora and for downstream deployment. We present the first systematic comparison of four channel adaptation methods (Conv1d projection, spherical spline interpolation (SSI), source-space decomposition, and Riemannian re-centering) across five pretrained EEG foundation models (5M--157M parameters), five downstream tasks, and two training regimes with 10--15 random seeds each. We find that rigid-montage models (BENDR, Neuro-GPT) require external adaptation, while flexible models (EEGPT, CBraMod) match or exceed it natively when fine-tuned but benefit from external methods under frozen-encoder deployment. A probe-SFT asymmetry exists: external adaptation can cause severe negative transfer during fine-tuning of flexible models. The optimal method is architecture-dependent (Conv1d for BENDR, SSI/Riemannian for Neuro-GPT, source-space decomposition for depression detection), and 5M-parameter CBraMod outperforms models up to 31$\times$ larger on 4/5 datasets, consistent with independent findings that compact EEG-specific architectures can match larger models.

Related papers

• SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD [91.71050339479261]
  This report presents an end-to-end optimization practice on the Ascend NPU SuperPOD.<n>We develop a hierarchical optimization framework spanning model-level parallelism, computation-communication orchestration, and low-level kernel execution.<n>The resulting system achieves 34.22% Model FLOPs Utilization (MFU) with a 2.93x improvement over the open-source baseline recipe.
  arXiv  Detail & Related papers  (2026-07-22T13:49:17Z)
• Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models [63.96252564303143]
  Embodied-R1.5 is a unified Embodied Foundation Model (EFM) that integrates comprehensive embodied reasoning capabilities.<n>We build a large-scale data system of over 15B tokens, and design a multi-task balanced RL recipe.<n>With only 8B parameters, Embodied-R1.5 SOTA on 16 out of 24 embodied VLM benchmarks, surpassing leading models like Gemini-Robotics-ER-1.5 and GPT-5.4.
  arXiv  Detail & Related papers  (2026-06-09T18:07:50Z)
• From Physics to Surrogate Intelligence: A Unified Electro-Thermo-Optimization Framework for TSV Networks [0.5599792629509228]
  High-density through-substrate vias (TSVs) enable 2.5D/3D heterogeneous integration but introduce significant signal-integrity and thermal-reliability challenges.<n>This work presents a scalable electro-thermal modeling and optimization framework that combines physics-informed analytical modeling, graph neural network (GNN) surrogates, and full-wave sign-off validation.
  arXiv  Detail & Related papers  (2026-03-31T05:10:33Z)
• SLE-FNO: Single-Layer Extensions for Task-Agnostic Continual Learning in Fourier Neural Operators [0.4369550829556577]
  New experimental conditions or simulation regimes may differ significantly, requiring extrapolation and model updates without re-access to prior data.<n>This creates a need for continual learning (CL) frameworks that can adapt to distribution shifts while preventing catastrophic forgetting.<n>We introduce a new architecture-based approach (SLE-FNO) combining a Single-Layer Extension (SLE) with the Fourier Neural Operator (FNO) to support efficient CL.
  arXiv  Detail & Related papers  (2026-03-20T18:30:38Z)
• LuMamba: Latent Unified Mamba for Electrode Topology-Invariant and Efficient EEG Modeling [14.663411219312827]
  We propose textbfLuMamba (textbfLatent textbfUnified textbfMamba), a self-supervised framework combining topology-invariant encodings with linear-complexity state-space modeling.<n>With only 4.6M parameters, LuMamba attains 80.99% balanced accuracy on TUAB and achieves state-of-art performance on Alzheimer's detection.
  arXiv  Detail & Related papers  (2026-03-19T16:26:11Z)
• GLUE: Generative Latent Unification of Expertise-Informed Engineering Models [3.005158583027536]
  We introduce Generative Latent Unification of Expertise-Informed Engineering Models (GLUE)<n>GLUE orchestrates pre-trained, frozen subsystem generators while enforcing system-level feasibility, optimality, and diversity.<n>On a UAV design problem with five coupling constraints, we find that data-driven approaches yield diverse, high-performing designs but require large datasets to satisfy constraints reliably.
  arXiv  Detail & Related papers  (2025-12-22T15:23:19Z)
• LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models [95.77351099118323]
  Masked Diffusion Models (MDMs) present a promising paradigm for language modeling.<n>The challenge arises from the high variance in Evidence Lower Bound (ELBO)-based likelihood estimates required for preference optimization.<n>We propose Variance-Reduced Preference Optimization (VRPO), a framework that formally analyzes the variance of ELBO estimators and derives on both the bias and variance of preference optimization gradients.
  arXiv  Detail & Related papers  (2025-05-25T16:36:20Z)
• Automatically Learning Hybrid Digital Twins of Dynamical Systems [56.69628749813084]
  Digital Twins (DTs) simulate the states and temporal dynamics of real-world systems. DTs often struggle to generalize to unseen conditions in data-scarce settings. In this paper, we propose an evolutionary algorithm ($textbfHDTwinGen$) to autonomously propose, evaluate, and optimize HDTwins.
  arXiv  Detail & Related papers  (2024-10-31T07:28:22Z)
• EPS-MoE: Expert Pipeline Scheduler for Cost-Efficient MoE Inference [49.94169109038806]
  This paper introduces EPS-MoE, a novel expert pipeline scheduler for MoE that surpasses the existing parallelism schemes.<n>Our results demonstrate at most 52.4% improvement in prefill throughput compared to existing parallel inference methods.
  arXiv  Detail & Related papers  (2024-10-16T05:17:49Z)
• SZU-AFS Antispoofing System for the ASVspoof 5 Challenge [3.713577625357432]
  The SZU-AFS anti-spoofing system was designed for Track 1 of the ASVspoof 5 Challenge under open conditions. The final fusion system achieves a minDCF of 0.115 and an EER of 4.04% on the evaluation set.
  arXiv  Detail & Related papers  (2024-08-19T12:12:29Z)
• SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient [69.61083127540776]
  Deep learning applications benefit from using large models with billions of parameters. Training these models is notoriously expensive due to the need for specialized HPC clusters. We consider alternative setups for training large models: using cheap "preemptible" instances or pooling existing resources from multiple regions.
  arXiv  Detail & Related papers  (2023-01-27T18:55:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.