corteg-eeg-ecog-cross-modality

name	corteg-eeg-ecog-cross-modality
description	CORTEG: Cross-modality transfer framework that adapts pretrained scalp-EEG foundation models to intracranial ECoG recordings. Combines EEG FM backbone with electrode-aware KNNSoftFourier spatial adapter, dual-stream tokenizer (low-frequency + high-gamma), and leave-one-subject-out fine-tuning. Enables competitive ECoG decoding with only 10-30 minutes of calibration data per patient. Activation: CORTEG, EEG foundation model, ECoG decoding, cross-modality transfer, scalp-to-intracranial, brain-computer interface, cross-patient learning, electrode-aware adapter.
arxiv_id	2605.10337
published	2026-05-11
authors	Liuyin Yang, Qiang Sun, Bob Van Dyck, Eva Calvo Merino, Marc M. Van Hulle
tags	["eeg-foundation-models","ecog-decoding","cross-modality-transfer","brain-computer-interface","transfer-learning"]

CORTEG: Cross-Modality Representation Transfer from Scalp to Intracranial Brain Recordings

Foundation models pretrained on scalp EEG can be adapted for ECoG decoding, enabling cross-patient learning and competitive performance with minimal calibration data.

Source: arXiv: 2605.10337

Core Methodology

Key Innovation

The first framework to demonstrate that large pretrained scalp-EEG foundation models can be effectively transferred to the intracranial ECoG domain, bridging the gap between non-invasive and invasive recordings.

Technical Framework

1. EEG Foundation Model Backbone: Leverage large pretrained models (e.g., from LAEEG, BENDR, or similar) that have learned generalizable features from scalp EEG data
2. Electrode-Aware KNNSoftFourier Spatial Adapter: A novel spatial adapter that maps ECoG electrode positions to EEG foundation model input space, using k-nearest neighbor interpolation in the Fourier domain with soft assignment weights
3. Dual-Stream Tokenizer: Separate processing streams for low-frequency (LFP-like, <100Hz) and high-gamma (>100Hz) activity, as these bands carry complementary information in ECoG
4. Leave-One-Subject-Out Fine-Tuning: Train on N-1 patients, fine-tune minimally on held-out patient (10-30 min data, single GPU)
5. Cross-Patient Decoding: Achieve competitive performance by sharing information across patients through the pretrained backbone

Key Results

• Successful cross-modality transfer: Scalp EEG foundation models adapt to ECoG with minimal fine-tuning
• 10-30 minute calibration: Competitive decoding performance on new patients with very limited data
• Dual-stream tokenization: Low-frequency + high-gamma streams outperform single-stream baselines
• Cross-patient learning: Information sharing across patients significantly outperforms patient-specific models trained from scratch

Implementation Patterns

CORTEG Framework:
1. Load pretrained EEG FM (weights frozen initially)
2. Add KNNSoftFourier spatial adapter for ECoG electrode layout
3. Add dual-stream tokenizer (LF path + HG path)
4. Train decoder head on N-1 subjects
5. Fine-tune spatial adapter + head on held-out subject (10-30 min)

Applications

• Clinical BCI: Rapid calibration for new patients using pre-existing EEG foundation models
• ECoG-based decoding: Motor imagery, speech decoding, cursor control
• Minimally-invasive interfaces: Bridge scalp EEG research to ECoG applications
• Cross-patient generalization: Leverage population-level knowledge for individual patients

Activation Keywords

• CORTEG framework
• EEG foundation model transfer
• ECoG cross-modality decoding
• KNNSoftFourier spatial adapter
• dual-stream brain tokenizer
• scalp-to-intracranial adaptation
• cross-patient ECoG learning
• brain-computer interface calibration

Related Skills

• neuroatlas-eeg-foundation-benchmark
• eeg-foundation-model-adapters
• nerve-network-aware-bilinear-fc-tokenization
• neural-encoding-evaluation-ground-truth
• what-do-eeg-foundation-models-capture