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Brain Decoding


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



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