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HappyFeat—An interactive and efficient BCI framework for clinical applications

Year: 2023
Type: article
Abstract: Brain–Computer Interface (BCI) systems allow to perform actions by translating brain activity into commands. Such systems require training a classification algorithm to discriminate between mental states, using specific features from the brain signals. This step is crucial and presents specific constraints in clinical contexts. HappyFeat is an open-source software making BCI experiments easier in such contexts: effortlessly extracting and selecting adequate features for training, in a single GUI. Novel features based on Functional Connectivity can be used, allowing graph-oriented approaches. We describe HappyFeat's mechanisms, showing its performances in typical use cases, and showcasing how to compare different types of features.
DOI: 10.1016/j.simpa.2023.100610
Institutions: Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Inserm, Sorbonne Université, Assistance Publique – Hôpitaux de Paris, Institut du Cerveau
Language: English
Open Access status: gold
Estimated APC paid: $273
Funders: Institut national de recherche en informatique et en automatique (INRIA), European Commission, Agence Nationale de la Recherche, Horizon 2020 Framework Programme, European Research Council, Horizon 2020, H2020 European Research Council
Awards: G1258743643, Non-invasive decoding of brain communication patterns to ease motor restoration after stroke, G3103753459, G434337789, G7240651115, Institut de Neurosciences Translationnelles de Paris
OpenAlex ID: W4308675399
FWCI: 1.246
Cites: 29
Cited by: 9
Related to: 10