6 papers
Channel Adaptation for EEG Foundation Models: A Systematic Benchmark Across Architectures, Tasks, and Training Regimes
Kuntal Kokate, Bruno Aristimunha, Dung Truong +1
Scaling EEG foundation models requires pooling data across heterogeneous electrode montages, a prerequisite both for larger pretraining corpora and for downstream deployment. We pr…
EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding
Bruno Aristimunha, Dung Truong, Pierre Guetschel +17
Current electroencephalogram (EEG) decoding models are typically trained on small numbers of subjects performing a single task. Here, we introduce a large-scale, code-submission-ba…
Data Normalization Strategies for EEG Deep Learning
Dung Truong, Arnaud Delorme
Normalization is a critical yet often overlooked component in the preprocessing pipeline for EEG deep learning applications. The rise of large-scale pretraining paradigms such as s…
From Theory to Application: Fine-Tuning Large EEG Model with Real-World Stress Data
Siwen Wang, Shitou Zhang, Wan-Lin Chen +2
Recent advancements in Large Language Models have inspired the development of foundation models across various domains. In this study, we evaluate the efficacy of Large EEG Models…
Automatic EEG Independent Component Classification Using ICLabel in Python
Arnaud Delorme, Dung Truong, Luca Pion-Tonachini +1
ICLabel is an important plug-in function in EEGLAB, the most widely used software for EEG data processing. A powerful approach to automated processing of EEG data involves decompos…
Hierarchical Event Descriptor library schema for EEG data annotation
Dora Hermes, Tal Pal Attia, Sándor Beniczky +11
Standardizing terminology to annotate electrophysiological events can improve both computational research and clinical care. Sharing data enriched with standard terms can facilitat…