Hubert Banville
Hubert Banville
Inria, Parietal, Université Paris-Saclay, InteraXon
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Cited by
Cited by
Deep learning-based electroencephalography analysis: A systematic review
Y Roy, H Banville, I Albuquerque, A Gramfort, J Faubert
Journal of Neural Engineering, 2019
Uncovering the structure of clinical EEG signals with self-supervised learning
H Banville, O Chehab, A Hyvärinen, DA Engemann, A Gramfort
Journal of Neural Engineering 18 (4), 046020, 2021
Recent advances and open challenges in hybrid brain-computer interfacing: a technological review of non-invasive human research
H Banville, TH Falk
Brain-Computer Interfaces 3 (1), 9-46, 2016
MuLES: An open source EEG acquisition and streaming server for quick and simple prototyping and recording
R Cassani, H Banville, TH Falk
Proceedings of the 20th International Conference on Intelligent User …, 2015
Self-supervised representation learning from electroencephalography signals
H Banville, I Albuquerque, A Hyvärinen, G Moffat, DA Engemann, ...
2019 IEEE 29th International Workshop on Machine Learning for Signal …, 2019
Using affective brain-computer interfaces to characterize human influential factors for speech quality-of-experience perception modelling
R Gupta, K Laghari, H Banville, TH Falk
Human-centric Computing and Information Sciences 6 (1), 1-19, 2016
Mental Task Evaluation for Hybrid NIRS-EEG Brain-Computer Interfaces
H Banville, R Gupta, TH Falk
Computational Intelligence and Neuroscience 2017, 2017
PhySyQX: A database for physiological evaluation of synthesised speech quality-of-experience
R Gupta, HJ Banville, TH Falk
2015 IEEE workshop on applications of signal processing to audio and …, 2015
Multimodal physiological quality-of-experience assessment of text-to-speech systems
R Gupta, HJ Banville, TH Falk
IEEE Journal of Selected Topics in Signal Processing 11 (1), 22-36, 2016
Wearable computing device with electrophysiological sensors
CA Aimone, ST Mackenzie, GD Moffat, H Banville, NH Proulx
US Patent 10,452,144, 2019
Robust learning from corrupted EEG with dynamic spatial filtering
H Banville, SUN Wood, C Aimone, DA Engemann, A Gramfort
NeuroImage, 118994, 2022
Toward Mental Workload Measurement Using Multimodal EEG–fNIRS Monitoring
H Banville, M Parent, S Tremblay, TH Falk
Neuroergonomics, 245-246, 2018
Hybrid Brain-Computer Interfaces: Improving Mental Task Classification Performance through Fusion of Neurophysiological Modalities.
HJ Banville
Université du Québec, Institut national de la recherche scientifique, 2015
A reusable benchmark of brain-age prediction from M/EEG resting-state signals
DA Engemann, A Mellot, R Höchenberger, H Banville, D Sabbagh, ...
bioRxiv, 2021
Using fNIRS to Characterize Human Influential Factors: Towards Models of Quality of Experience Perception for Text-to-Speech Systems
R Gupta, HJ Banville, I Albuquerque, TH Falk
cortex 403, 309-312, 2000
Enabling real-world EEG applications with deep learning
H Banville
Université Paris-Saclay, 2022
Learning with self-supervision on EEG data
A Gramfort, H Banville, O Chehab, A Hyvärinen, D Engemann
2021 9th International Winter Conference on Brain-Computer Interface (BCI), 1-2, 2021
Wearable computing apparatus with movement sensors and methods therefor
GD Moffat, CA Aimone, HJ BANVILLE, NH Proulx
US Patent App. 16/959,833, 2020
System and method for brain modelling
CA Aimone, G Moffat, HJ BANVILLE, S Wood, S Padmanaban, K Sam, ...
US Patent App. 16/858,093, 2020
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