Arthur Desbois, Tristan Venot, Fabrizio De Vico Fallani, Marie-Constance Corsi
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引用次数: 0
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.
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