EvoBrain introduces a continual learning method with Neuro-Spectral Task Normalization and Response-Affinity Distillation to enable unified EEG decoding across heterogeneous BCI tasks.
CodeBrain: Bridging Decoupled Tokenizer and Multi-Scale Architecture for EEG Foundation Model
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abstract
Electroencephalography (EEG) provides real-time insights into brain activity and supports diverse applications in neuroscience. While EEG foundation models (EFMs) have emerged to address the scalability issues of task-specific models, current approaches still yield clinically uninterpretable and weakly discriminative representations, inefficiently capturing global dependencies and neglecting important local neural events. We present CodeBrain, a two-stage EFM designed to fill this gap. In the first stage, we introduce the TFDual-Tokenizer, which decouples heterogeneous temporal and frequency EEG signals into discrete tokens, quadratically expanding the representation space to enhance discriminative power and offering domain-specific representation-level interpretability by suggesting potential links to neural events and spectral rhythms. In the second stage, we propose the multi-scale EEGSSM architecture, which combines structured global convolution with sliding window attention to efficiently capture both sparse long-range and local dependencies, reflecting the brain's small-world topology. Pretrained on the largest public EEG corpus, CodeBrain achieves strong generalization across eight downstream tasks and ten datasets under distribution shifts, supported by comprehensive ablations, scaling-law analyzes, and interpretability evaluations. The code and the pretrained weights are available at https://github.com/jingyingma01/CodeBrain.
years
2026 4verdicts
UNVERDICTED 4representative citing papers
FUSED integrates EEG foundation models into source-free domain adaptation via dual-branch co-adaptation, consensus filtering, and two-stage pseudo-label refinement to achieve state-of-the-art cross-subject EEG decoding.
MST combines Morlet wavelet tokenization, long-context baseline removal, and frequency-specific spatial projections with a Transformer backbone to outperform pretrained EEG models on SEED datasets for cross-subject emotion decoding.
PRiSE-EEG is a prior-guided EEG foundation model that allocates shared and specialized experts across depth using CKA-derived sigmoid mappings and reports strong cross-paradigm results on 12 benchmarks.
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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Foundation Model Guided Dual-Branch Co-Adaptation for Source-Free EEG Decoding
FUSED integrates EEG foundation models into source-free domain adaptation via dual-branch co-adaptation, consensus filtering, and two-stage pseudo-label refinement to achieve state-of-the-art cross-subject EEG decoding.
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Dive into Waves: Morlet Spectral Transformer for Cross-Subject Emotion Decoding from EEG
MST combines Morlet wavelet tokenization, long-context baseline removal, and frequency-specific spatial projections with a Transformer backbone to outperform pretrained EEG models on SEED datasets for cross-subject emotion decoding.
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PRiSE-EEG: A Prior-Guided Foundation Model with Depth-Stratified Experts for Cross-Paradigm EEG Representation Learning
PRiSE-EEG is a prior-guided EEG foundation model that allocates shared and specialized experts across depth using CKA-derived sigmoid mappings and reports strong cross-paradigm results on 12 benchmarks.