11 citations · 17 across the 11 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
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…
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…