Title :
MEG decoding across subjects
Author :
Olivetti, E. ; Kia, Seyed Mostafa ; Avesani, Paolo
Author_Institution :
Neuroinf. Lab. (NILab), Bruno Kessler Found., Trento, Italy
Abstract :
Brain decoding is a data analysis paradigm for neuroimaging experiments that is based on predicting the stimulus presented to the subject from the concurrent brain activity. In order to make inference at the group level, a straightforward but sometimes unsuccessful approach is to train a classifier on the trials of a group of subjects and then to test it on unseen trials from new subjects. The extreme difficulty is related to the structural and functional variability across the subjects. We call this approach decoding across subjects. In this work, we address the problem of decoding across subjects for magnetoen-cephalographic (MEG) experiments and we provide the following contributions: first, we formally describe the problem and show that it belongs to a machine learning sub-field called transductive transfer learning (TTL). Second, we propose to use a simple TTL technique that accounts for the differences between train data and test data. Third, we propose the use of ensemble learning, and specifically of stacked generalization, to address the variability across subjects within train data, with the aim of producing more stable classifiers. On a face vs. scramble task MEG dataset of 16 subjects, we compare the standard approach of not modelling the differences across subjects, to the proposed one of combining TTL and ensemble learning. We show that the proposed approach is consistently more accurate than the standard one.
Keywords :
data analysis; encoding; learning (artificial intelligence); magnetoencephalography; medical signal processing; neurophysiology; signal classification; MEG decoding across subjects; brain decoding; concurrent brain activity; data analysis paradigm; functional variability; machine learning subfield called transductive transfer learning; magnetoencephalographic experiments; neuroimaging experiments; scramble task MEG dataset; stable classifiers; stacked generalization; structural variability; Accuracy; Brain; Data analysis; Decoding; Face; Neuroimaging; Training; brain decoding; stacked generalization; transfer learning covariate shift;
Conference_Titel :
Pattern Recognition in Neuroimaging, 2014 International Workshop on
Conference_Location :
Tubingen
Print_ISBN :
978-1-4799-4150-6
DOI :
10.1109/PRNI.2014.6858538