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Brain Decoding

Alexandre Gramfort is currently assistant professor at Telecom ParisTech. His research interests include mathematical modeling and the computational aspects of brain imaging (MEG, EEG, fMRI, dMRI). He is generally interested in biomedical signal and image processing with the methods of scientific computing, data mining and machine learning.

The general objective is the development of new brain decoding methods that use the spatial relation information in fRMI signals: (1) the development of a new feature selection method for brain decoding based on clustering ensemble algorithms that preserve the spatial relation among voxels and yield a consensus parcelation of the brain image; (2) the design of a graph representation of the brain image that captures all the information in a given parcelation; (3) the design of graph kernels able to measure the semantics inherit in the graph structure and with a low computational cost in its

The paper "Decoding affect in videos employing the MEG brain signal." by Mojtaba Khomami Abadi, Seyed Mostafa Kia, Ramanathan Subramanian, Paolo Avesani, and Nicu Sebe has been accepted by the committee of the 2nd International Workshop on Emotion Representation, Analysis and Synthesis in Continuous Time and Space.

EmoSPACE 2013 is an event in conjunction with the IEEE FG 2013 Shanghai, China, 22/26 April, 2013

Seyed Mostafa Kia joins NILab with an internship as a master student (Cognitive Neuroscience of UniTn).

Sandro Vega Pons joins NILab with a RESTATE Marie Curie Grant. Topic of research: graph kernels for brain decoding.

1st prize in Decoding Word and Category Specific Representation. Joint team with Nathan Weisz (PI MEG, LNIF/CIMeC) and Yuan Tao (PhD Candidate, CLIC/CIMeC).

 

The project "Brain Decoding of Affective Multimedia Contents (DECAF)" a joint proposal with Nicu Sebe of DISI-UniTnhas been approved by the ethics comitee of UniTN.

Abstract: The project investigates the characterization of affect (valence and arousal) using the Magnetoencephalogram (MEG) brain signal. We attempt single-trial classification of movie and music videos with MEG responses. The research questions are

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