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REDDI: A Riemannian Ensemble Learning Framework for Interpretable Differential Diagnosis of Neurodegenerative Diseases

Neurodegenerative diseases such as Mild Cognitive Impairment (MCI), Multiple Sclerosis (MS), Parkinson s Disease (PD), and Amyotrophic Lateral Sclerosis (ALS) are becoming more prevalent. Each of these diseases, despite its specific …

Understanding Brain-Computer Interfaces training: a longitudinal and multimodal dataset

Brain-Computer Interfaces (BCIs) are devices that translate brain activity into commands for control or communication that present many clinical ap...

Weighted-stochastic Avalanche Transition Matrix (ws-ATM): a tool to investigate brain dynamic and its neuropathological alterations

Background and Objectives: Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder that, beyond motor neuron loss, involves distributed cortical network dysfunction and marked clinical heterogeneity, motivating biologically …

EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding

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-based competition comprising two challenges. First, the Transfer …

Neural mechanisms of training in Brain-Computer Interface: A Biophysical modeling approach

Brain-computer interface (BCI) is a system that translates neural activity into commands, allowing direct communication between the brain and external devices. Despite its clinical application, BCI systems fail to robustly capture subjects’ intent …

pyRiemann/pyRiemann: v0.8

version 0.8

Community Detection from Multiple Observations: from Product Graph Model to Brain Applications

This paper proposes a multilayer graph model for the community detection from multiple observations. This is a very frequent situation, when different estimators are applied to infer graph edges from signals at its nodes, or when different signal …

Introducing the modularity graph: an application to brain functional networks

In signal processing, exploring complex systems through network representations has become an area of growing interest. This study introduces the modularity graph, a new graph-based feature, to highlight the relationship across the graph communities. …

Dynamic reconfiguration of aperiodic brain activity supports cognitive functioning in epilepsy: a neural fingerprint identification

Background Temporal lobe epilepsy (TLE) is characterized by alterations of brain dynamic at large scale associated with altered cognitive functioning. Interindividual variability of brain activity is a source of heterogeneity in this disorder. Here, …

Geometric Neural Network based on Phase Space for BCI decoding

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, especially in Brain-Computer Interface (BCI), where the brain activity is decoded to …