EvoBrain introduces a continual learning method with Neuro-Spectral Task Normalization and Response-Affinity Distillation to enable unified EEG decoding across heterogeneous BCI tasks.
EEG-FM-Bench : A comprehensive benchmark for the systematic evaluation of EEG foundation models
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4representative citing papers
EEG-FM-Audit is an evaluation pipeline showing that properly tuned supervised baselines can match or outperform EEG foundation models with far fewer parameters on public datasets.
NeuroAtlas benchmarks foundation models on 42 EEG datasets and reports that EEG-specific models do not consistently outperform generic time-series models, standard metrics miss clinical utility, and rankings vary by domain.
NeuralBench is a new benchmarking framework for neuroAI models on EEG data that finds foundation models only marginally outperform task-specific ones while many tasks like cognitive decoding stay highly challenging.
citing papers explorer
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EvoBrain: Continual Learning of EEG Foundation Models Across Heterogeneous BCI Tasks
EvoBrain introduces a continual learning method with Neuro-Spectral Task Normalization and Response-Affinity Distillation to enable unified EEG decoding across heterogeneous BCI tasks.
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EEG-FM-Audit: A Systematic Evaluation and Analysis Pipeline for EEG Foundation Models
EEG-FM-Audit is an evaluation pipeline showing that properly tuned supervised baselines can match or outperform EEG foundation models with far fewer parameters on public datasets.
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NeuroAtlas: Benchmarking Foundation Models for Clinical EEG and Brain-Computer Interfaces
NeuroAtlas benchmarks foundation models on 42 EEG datasets and reports that EEG-specific models do not consistently outperform generic time-series models, standard metrics miss clinical utility, and rankings vary by domain.
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NeuralBench: A Unifying Framework to Benchmark NeuroAI Models
NeuralBench is a new benchmarking framework for neuroAI models on EEG data that finds foundation models only marginally outperform task-specific ones while many tasks like cognitive decoding stay highly challenging.