60 citations · 68 across the 20 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
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…
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…
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…