eeg-foundation-model-adapters

name: eeg-foundation-model-adapters description: "EEG foundation models with domain adaptation using lightweight adapters. Covers pre-trained EEG encoders, task-specific fine-tuning with adapters, cross-dataset generalization, and efficient deployment. Use when working with EEG foundation models, neural signal pre-training, adapter-based fine-tuning, or cross-dataset EEG classification." version: 1.0.0 author: Research Synthesis license: MIT metadata: hermes: tags: [eeg, foundation-model, adapters, neural-signals, transfer-learning] source_paper: "EEG Foundation Models with Domain Adaptation (arXiv:2604.xxxxx)" citations: 0

EEG Foundation Models with Adapters

Overview

Leveraging pre-trained EEG foundation models with lightweight adapter modules for efficient domain adaptation across different EEG tasks, datasets, and recording conditions.

Core Concepts

Foundation Model Architecture

• Self-supervised pre-training on large EEG corpora
• Multi-channel temporal encoding
• Cross-subject representation learning
• Contrastive learning for neural patterns

Adapter-Based Fine-Tuning

• Lightweight adapter modules (1-5% of parameters)
• Task-specific adaptation without full model retraining
• Domain shift mitigation across datasets
• Efficient deployment with frozen backbone

Implementation Patterns

# Adapter-based EEG classification
class EEGAdapterModel:
    def __init__(self, foundation_model, num_tasks):
        self.backbone = foundation_model  # Frozen pre-trained EEG encoder
        self.adapters = nn.ModuleList([
            AdapterLayer(dim=768) for _ in range(num_tasks)
        ])
        self.classifiers = nn.ModuleList([
            nn.Linear(768, num_classes) for _ in range(num_tasks)
        ])
    
    def forward(self, eeg_signal, task_id):
        features = self.backbone(eeg_signal)  # Frozen encoder
        adapted = self.adapters[task_id](features)
        return self.classifiers[task_id](adapted)

Key Benefits

1. Data Efficiency: 10-100x less task-specific data needed
2. Cross-Dataset Generalization: Adapt to new EEG systems
3. Computational Efficiency: Train only 1-5% of parameters
4. Multi-Task Learning: Single backbone, multiple adapters

Use Cases

• Motor imagery classification across subjects
• Sleep stage scoring across labs
• Epileptic seizure detection across hospitals
• Cognitive load estimation across tasks

Activation Keywords

• EEG foundation model
• neural signal pre-training
• adapter fine-tuning EEG
• cross-dataset EEG classification
• self-supervised EEG learning
• efficient EEG model deployment

References

• Related: eeg2vision-multimodal-reconstruction, meta-learning-in-context-brain-decoding