MNE-Python, a toolkit for neurophysiological data
A summary of the MNE-Python changes introduced during the two last releases and highlights for future directions.
MNE-Python software is an open-source Python package for exploring, visualizing, and analyzing human neurophysiological data such as MEG, EEG, sEEG, ECoG, and more. It includes modules for data input/output, preprocessing, visualization, source estimation, time-frequency analysis, connectivity analysis, machine learning, and statistics.
I am a research engineer in the Parietal team at INRIA-Saclay working on human neuro-physiological data and machine learning. Contributing to open-source projects like: MNE-Python, OpenMEEG, scikit-learn, and others.
I obtained my PhD in computer vision applied to medical imaging, jointly from the Universitat de Girona and the Universite de Bourgogne France-Comte in 2013. After my PhD, and before coming to Parietal as an engineer, I was a postdoctoral fellow and teaching assistant at Universite de Bourgogne France-Comte.
I enjoy following technology trends, learning new skills, and sharing them. I also care about pedagogy and education as I strongly believe that any skill can be acquired by anyone with ease if transferred properly. This is why I have been involved in organizing pedagogical activities such as underwater robotics workshops for kids and enthusiasts, First LEGO League competitions, and lately software carpentry workshops.