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Marco Congedo

Researcher at University of Grenoble

Publications -  160
Citations -  10647

Marco Congedo is an academic researcher from University of Grenoble. The author has contributed to research in topics: Blind signal separation & Riemannian geometry. The author has an hindex of 39, co-authored 155 publications receiving 8610 citations. Previous affiliations of Marco Congedo include Centre national de la recherche scientifique & French Institute for Research in Computer Science and Automation.

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Journal ArticleDOI

The effects of neurofeedback training in the cognitive division of the anterior cingulate gyrus

TL;DR: There appears to be a multidimensional executive circuit that increases in the same frequency in apparent synchrony with the AC and it may be possible to train this sub-cortical region using LNFB.

" Brain Invaders": a prototype of an open-source P300-based video game working with the OpenViBE platform

TL;DR: The prototype of a pure-BCI video game based on the well known vintage video game "Space Invaders", which makes use of a state of the art P300 oddball paradigm to select the alien to be destroyed at a regular pace, based on current probabilities assigned to each alien by a learning machine continuously analyzing and classifying the user's electroencephalographic stream.
Proceedings ArticleDOI

Common Spatial Pattern revisited by Riemannian geometry

TL;DR: This paper presents a link between the well known Common Spatial Pattern (CSP) algorithm and Riemannian geometry in the context of Brain Computer Interface (BCI) and proposes an improvement of classical CSP method.
Journal ArticleDOI

Group independent component analysis of resting state EEG in large normative samples

TL;DR: EEG (Electroencephalography) resting state was studied by means of group blind source separation (gBSS), employing a test-retest strategy in two large-sample normative databases, resulting in a new tool that may serve as an adjunct to diagnosis and assessment of abnormal brain functioning and aid in research on normal resting state networks.