11 citations · 17 across the 11 of their papers we have counts for
16 papers
Scaling Laws for EEG Decoding: How Much Data Is Enough?
José Maurício Nunes de Oliveira, Bruna J. Lopes, Léo Burgund +2
Deep learning has become a cornerstone of EEG-based brain decoding, with a growing number of architectures proposed every day. However, how the performance of these different model…
Neuron-Level Architecture Growth: A Controlled Evaluation for EEG Time-Series Decoding
Adam Mounir, Stella Douka, Arnault H. Caillet +2
Convolutional EEG decoders are trained at a fixed width, usually set by their authors on other data. Growing methods add neurons during training where the loss could decrease the m…
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…
Automated Artifact Removal in EEG Age Prediction: A systematic comparison
Davoud Hajhassani, Paul-Adrien Graignic, Bruno Aristimunha +8
Automated EEG artifact removal may improve downstream analysis but can also alter predictive information. We benchmarked nine automated artifact removal methods against a common no…
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…
Learning aligned EEG representations with subject-specific encoders
Bruna J. Lopes, Gabriel Schwartz, Sylvain Chevallier +2
Cross-subject EEG decoding promises more training data, but it also exposes neural networks to strong inter-subject distribution shifts. We study whether task supervision and archi…
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…
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…
Fine-Tuning Strategies for Continual Online EEG Motor Imagery Decoding: Insights from a Large-Scale Longitudinal Study
Martin Wimpff, Bruno Aristimunha, Sylvain Chevallier +1
This study investigates continual fine-tuning strategies for deep learning in online longitudinal electroencephalography (EEG) motor imagery (MI) decoding within a causal setting i…
Combining Euclidean Alignment and Data Augmentation for BCI decoding
Gustavo H. Rodrigues, Bruno Aristimunha, Sylvain Chevallier +1
Automated classification of electroencephalogram (EEG) signals is complex due to their high dimensionality, non-stationarity, low signal-to-noise ratio, and variability between sub…
The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark
Sylvain Chevallier, Igor Carrara, Bruno Aristimunha +6
Objective. This study conduct an extensive Brain-computer interfaces (BCI) reproducibility analysis on open electroencephalography datasets, aiming to assess existing solutions and…
Geometric Neural Network based on Phase Space for BCI-EEG decoding
Igor Carrara, Bruno Aristimunha, Marie-Constance Corsi +3
Objective: The integration of Deep Learning (DL) algorithms on brain signal analysis is still in its nascent stages compared to their success in fields like Computer Vision. This i…
A Systematic Evaluation of Euclidean Alignment with Deep Learning for EEG Decoding
Bruna Junqueira, Bruno Aristimunha, Sylvain Chevallier +1
Electroencephalography (EEG) signals are frequently used for various Brain-Computer Interface (BCI) tasks. While Deep Learning (DL) techniques have shown promising results, they ar…
Evaluating the structure of cognitive tasks with transfer learning
Bruno Aristimunha, Raphael Y. de Camargo, Walter H. Lopez Pinaya +3
Electroencephalography (EEG) decoding is a challenging task due to the limited availability of labelled data. While transfer learning is a promising technique to address this chall…