60 citations · 68 across the 20 of their papers we have counts for
24 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…
What masking geometry works best for EEG foundation models?
Pierre Guetschel, Bruno Aristimunha, Yassine El Ouahidi +3
EEG foundation models hold promise for scalable brain-signal decoding across clinical and cognitive neuroscience applications, yet their pre-training pipelines remain poorly unders…
EEGPrep: a validated Python implementation of the EEGLAB preprocessing pipeline
Arnaud Delorme, Suraj Ranganath, Christian Kothe +3
Objective. Automated EEG preprocessing is common in research and clinical work, but few pipelines have been tested systematically. In a recent benchmark, the default EEGLAB pipelin…
EEGDash: An open-source platform for machine learning on public neurophysiological data
Bruno Aristimunha, Aviv Dotan, Pierre Guetschel +8
Public recordings of electroencephalography (EEG), magnetoencephalography (MEG), intracranial EEG (iEEG), functional near-infrared spectroscopy (fNIRS) and electromyography (EMG) a…
From EEG Cleaning to Decoding: The Role of Artifact Rejection in MI-based BCIs
Davoud Hajhassani, Bruno Aristimunha, Paul-Adrien Graignic +5
Motor imagery (MI) BCIs are sensitive to EEG artifacts, yet the practical impact of automated artifact rejection on downstream MI decoding performance remains unclear. While most w…
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…
ENIGMA: EEG-to-Image in 15 Minutes Using Less Than 1% of the Parameters
Reese Kneeland, Wangshu Jiang, Ugo Bruzadin Nunes +3
To be practical for real-life applications, models for brain-computer interfaces must be easily and quickly deployable on new subjects, effective on affordable scanning hardware, a…
Adaptive Split-MMD Training for Small-Sample Cross-Dataset P300 EEG Classification
Weiyu Chen, Arnaud Delorme
Detecting single-trial P300 from EEG is difficult when only a few labeled trials are available. When attempting to boost a small target set with a large source dataset through tran…
Alljoined-1.6M: A Million-Trial EEG-Image Dataset for Evaluating Affordable Brain-Computer Interfaces
Jonathan Xu, Ugo Bruzadin Nunes, Wangshu Jiang +5
We present a new large-scale electroencephalography (EEG) dataset as part of the THINGS initiative, comprising over 1.6 million visual stimulus trials collected from 20 participant…
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…
Quantifying Data Requirements for EEG Independent Component Analysis Using AMICA
Gwenevere Frank, Seyed Yahya Shirazi, Jason Palmer +3
Independent Component Analysis (ICA) is an important step in EEG processing for a wide-ranging set of applications. However, ICA requires well-designed studies and data collection…
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…
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…
Cycling on the Freeway: The Perilous State of Open Source Neuroscience Software
Britta U. Westner, Daniel R. McCloy, Eric Larson +4
Most scientists need software to perform their research (Barker et al., 2020; Carver et al., 2022; Hettrick, 2014; Hettrick et al., 2014; Switters and Osimo, 2019), and neuroscient…
An Exploration of Optimal Parameters for Efficient Blind Source Separation of EEG Recordings Using AMICA
Gwenevere Frank, Seyed Yahya Shirazi, Jason Palmer +3
EEG continues to find a multitude of uses in both neuroscience research and medical practice, and independent component analysis (ICA) continues to be an important tool for analyzi…
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…
The Past, Present, and Future of the Brain Imaging Data Structure (BIDS)
Russell A. Poldrack, Christopher J. Markiewicz, Stefan Appelhoff +111
The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is mean…
A Comparison of Neuroelectrophysiology Databases
Priyanka Subash, Alex Gray, Misque Boswell +22
As data sharing has become more prevalent, three pillars - archives, standards, and analysis tools - have emerged as critical components in facilitating effective data sharing and…
A Framework to Evaluate Independent Component Analysis applied to EEG signal: testing on the Picard algorithm
Gwenevere Frank, Scott Makeig, Arnaud Delorme
Independent component analysis (ICA), is a blind source separation method that is becoming increasingly used to separate brain and non-brain related activities in electroencephalog…
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…