Functional connectivity ensemble method to enhance BCI performance (FUCONE)
Objective: Relying on the idea that functional connectivity provides important insights on the
underlying dynamic of neuronal interactions, we propose a novel framework that combines …
underlying dynamic of neuronal interactions, we propose a novel framework that combines …
[HTML][HTML] Personal authentication and cryptographic key generation based on electroencephalographic signals
EA Abdel-Ghaffar, M Daoudi - Journal of King Saud University-Computer …, 2023 - Elsevier
Brain signals have recently been proposed as a strong biometric due to their characteristics
such as, uniqueness, permanence, universality, and confidentiality. There are many factors …
such as, uniqueness, permanence, universality, and confidentiality. There are many factors …
First steps towards quantum machine learning applied to the classification of event-related potentials
Low information transfer rate is a major bottleneck for brain-computer interfaces based on
non-invasive electroencephalography (EEG) for clinical applications. This led to the …
non-invasive electroencephalography (EEG) for clinical applications. This led to the …
Case-Based and Quantum Classification for ERP-Based Brain–Computer Interfaces
GH Cattan, A Quemy - Brain Sciences, 2023 - mdpi.com
Low transfer rates are a major bottleneck for brain–computer interfaces based on
electroencephalography (EEG). This problem has led to the development of more robust …
electroencephalography (EEG). This problem has led to the development of more robust …
Magnetoencephalography-based interpretable automated differential diagnosis in neurodegenerative diseases
D Klepachevskyi, A Romano, B Aristimunha… - medRxiv, 2024 - medrxiv.org
Automating the diagnostic process steps has been of interest for research grounds and to
help manage the healthcare systems. Improved classification accuracies, provided by ever …
help manage the healthcare systems. Improved classification accuracies, provided by ever …
Electroencephalography and Magnetoencephalography
MC Corsi - Machine learning for brain disorders, 2023 - Springer
In this chapter, we present the main characteristics of electroencephalography (EEG) and
magnetoencephalography (MEG). More specifically, this chapter is dedicated to the …
magnetoencephalography (MEG). More specifically, this chapter is dedicated to the …
Ensemble learning based on functional connectivity and Riemannian geometry for robust workload estimation
Context Passive Brain-Computer Interface (pBCI) has recently gained in popularity through
its applications, eg workload and attention assessment. Nevertheless, one of the main …
its applications, eg workload and attention assessment. Nevertheless, one of the main …
RIGOLETTO--RIemannian GeOmetry LEarning: applicaTion To cOnnectivity. A contribution to the Clinical BCI Challenge--WCCI2020
This short technical report describes the approach submitted to the Clinical BCI Challenge-
WCCI2020. This submission aims to classify motor imagery task from EEG signals and relies …
WCCI2020. This submission aims to classify motor imagery task from EEG signals and relies …
[PDF][PDF] Premier pas vers un apprentissage supervisé quantique appliqué aux potentiels évoqués cognitifs
AQ GrégoireCattan, A Andreev - researchgate.net
Low information transfer rate is a major bottleneck for brain-computer interfaces based on
non-invasive electroencephalography (EEG) for clinical applications. This led to the …
non-invasive electroencephalography (EEG) for clinical applications. This led to the …
Raisonnement par Cas applique aux Interfaces Cerveau-Machines: Etude pilote
G Cattan, A Quemy - 2021 - hal.science
L'analyse du signal des interfaces cerveau-machines (ICM) basée sur l'
électroencéphalographie (EEG) est complexe et constitue un obstacle pour les …
électroencéphalographie (EEG) est complexe et constitue un obstacle pour les …