2 citations · 3 across the 8 of their papers we have counts for
12 papers
Scaling subjects in cross-modal alignment: video decoding with EEG foundation model
Dung Truong, Kuntal Kokate, Arnaud Delorme
Naturalistic visual decoding from EEG has long been constrained by cohort size: existing scaling literature caps out at a few dozen subjects, leading prior work to conclude that sc…
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…
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…
EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding
Bruno Aristimunha, Dung Truong, Pierre Guetschel +16
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…
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…
Deep learning applied to EEG data with different montages using spatial attention
Dung Truong, Muhammad Abdullah Khalid, Arnaud Delorme
The ability of Deep Learning to process and extract relevant information in complex brain dynamics from raw EEG data has been demonstrated in various recent works. Deep learning mo…
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…
A streamable large-scale clinical EEG dataset for Deep Learning
Dung Truong, Manisha Sinha, Kannan Umadevi Venkataraju +2
Deep Learning has revolutionized various fields, including Computer Vision, Natural Language Processing, as well as Biomedical research. Within the field of neuroscience, specifica…
NEMAR: An open access data, tools, and compute resource operating on NeuroElectroMagnetic data
Arnaud Delorme, Dung Truong, Choonhan Youn +5
To take advantage of recent and ongoing advances in large-scale computational methods, and to preserve the scientific data created by publicly funded research projects, data archiv…
Assessing learned features of Deep Learning applied to EEG
Dung Truong, Scott Makeig, Arnaud Delorme
Convolutional Neural Networks (CNNs) have achieved impressive performance on many computer vision related tasks, such as object detection, image recognition, image retrieval, etc.…
Deep Convolutional Neural Network Applied to Electroencephalography: Raw Data vs Spectral Features
Dung Truong, Michael Milham, Scott Makeig +1
The success of deep learning in computer vision has inspired the scientific community to explore new analysis methods. Within the field of neuroscience, specifically in electrophys…