Technology

HappyFeat – The software to ease BCI workflow in clinical applications

HappyFeat is an open-source Python software aiming to simplify the use of BCI pipelines in clinical settings, and help researchers introduce network and graph-based approaches in BCI, using features based on functional connectivity. It consists of Qt-based GUIs allowing to effortlessly extract classification features based on spectral power or connectivity, and select and combine them to train a BCI classifier. HappyFeat seamlessly interfaces with BCI softwares (i.e. OpenViBE), via automatic scenario generation and manipulation, to greatly reduce experimental setup complexity.

A Desbois, T Venot, F De Vico Fallani, M-C Corsi. HappyFeat—An interactive and efficient BCI framework for clinical applications. Software Impacts (2024) https://doi.org/10.1016/j.simpa.2023.100610. (https://www.sciencedirect.com/science/article/pii/S2665963823001471)

github repository

Vizaj – A free online interactive software for visualizing spatial networks

Vizaj is an open-source 3D visualization tool for networks with fixed node position, based on Three.js. It is provided with a GUI which helps customizing the nodes, links background, support item, camera and any extra informations. The camera can be rotated by drag and drop. Right-click drag and drop translates the camera. Scrolling zooms and unzooms, etc.

T Rolland, F De Vico Fallani. Vizaj—A free online interactive software for visualizing spatial networks. Plos One (2023) https://doi.org/10.1371/journal.pone.0282181

Use it !!!

github repository

The brain-computer interface multimodal platform

We coordinate the development of the BCI platform at the Paris Brain Institute. This platform allows to design and test experimentally innovative prototypes including multimodal control (Tobii Pro Glasses), robotic effectors (Pollen Robotic 7 dof), and augmented reality (Hololens 2 microsoft).

FUCONE – Functional connectivity ensemble method to enhance BCI performance

FUCONE combines functional connectivity and covariance within a Riemannian framework to increase the robustness of brain–computer interfaces classification. Associated publication:
Chevallier, S., Corsi, MC , Yger, F., & De Vico Fallani, F. (2022) Riemannian geometry for combining functional connectivity metrics and covariance in BCI. Software Impacts. doi:10.1016/j.simpa.2022.100254

Github repository

Tutorial page

The Brain-Ventilator Interface – Online assessement of respiratory dyscomfort in sedated patients

Patent WO2013164462 (A1). T. Similowski, M. Raux, M. Chavez, J. Martinerie, and P. Pouget, “Method for characterising the physiological state of a patient from the analysis of the cerebral electrical activity of said patient, and monitoring device applying said method,” Extensions: CA2872061, EP2844139, ES2588837, FR1254089, JP2015520627, US20150119745.

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