2 citations · 3 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…
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…