EvoBrain introduces a continual learning method with Neuro-Spectral Task Normalization and Response-Affinity Distillation to enable unified EEG decoding across heterogeneous BCI tasks.
A simple review of EEG founda- tion models: Datasets, advancements and future perspectives
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Controlled comparison finds that a pretrained time-series foundation model can be effectively used as a frozen temporal feature extractor in EEG foundation models, with task-specific performance differences.
Fine-tuning brain foundation models on EEG data yields improved accuracy for real-time cognitive load estimation in BCIs while using SHAP to show consistent focus on prefrontal regions linked to cognitive control.
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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Temporal Feature Extractors in EEG Foundation Models: A Controlled Comparison Including a Pretrained Time-Series Model
Controlled comparison finds that a pretrained time-series foundation model can be effectively used as a frozen temporal feature extractor in EEG foundation models, with task-specific performance differences.
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Cognitive Load Estimation Using Brain Foundation Models and Interpretability for BCIs
Fine-tuning brain foundation models on EEG data yields improved accuracy for real-time cognitive load estimation in BCIs while using SHAP to show consistent focus on prefrontal regions linked to cognitive control.