Reconstruction-based EEG foundation models preferentially encode aperiodic and low-frequency components over oscillatory structure, with embeddings capturing subject identity more than task-relevant information.
Cbramod: A criss-cross brain foundation model for eeg decoding
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Introduces a channel-oriented design using per-electrode tokenization, multi-view self-distillation, and structured channel dropout within an encoding-alignment-decoding pipeline to improve EEG-to-music reconstruction over baselines.
SCOPE uses cohort-level external supervision, confidence-aware pseudo-labels, and a lightweight prototype-conditioned adapter (ProAdapter) to adapt frozen EEG foundation models in label-limited settings, reporting consistent gains across 50 experimental configurations.
MSCGC-KAN adds multi-scale causal graph convolution and Kolmogorov-Arnold feature mapping as a structured task head on a pre-trained CBraMod backbone, reporting balanced accuracy gains of 5.91 and 2.03 points on FACED and SEED-VII datasets over a linear baseline.
citing papers explorer
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Aperiodic and Low-Frequency Spectral Bias in Reconstruction based EEG Foundation Models
Reconstruction-based EEG foundation models preferentially encode aperiodic and low-frequency components over oscillatory structure, with embeddings capturing subject identity more than task-relevant information.
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Channel-Oriented Design for EEG-to-Music Reconstruction
Introduces a channel-oriented design using per-electrode tokenization, multi-view self-distillation, and structured channel dropout within an encoding-alignment-decoding pipeline to improve EEG-to-music reconstruction over baselines.
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SCOPE: Structured Prototype-Guided Adaptation for EEG Foundation Models with Limited Labels
SCOPE uses cohort-level external supervision, confidence-aware pseudo-labels, and a lightweight prototype-conditioned adapter (ProAdapter) to adapt frozen EEG foundation models in label-limited settings, reporting consistent gains across 50 experimental configurations.
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MSCGC-KAN: Multi-scale Causal Graph Convolution and Kolmogorov-Arnold Feature Mapping for EEG Emotion Recognition
MSCGC-KAN adds multi-scale causal graph convolution and Kolmogorov-Arnold feature mapping as a structured task head on a pre-trained CBraMod backbone, reporting balanced accuracy gains of 5.91 and 2.03 points on FACED and SEED-VII datasets over a linear baseline.