MEG Decoding Across Subjects

by   Emanuele Olivetti, et al.

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 magnetoencephalographic (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.


page 1

page 2

page 3

page 4


Generalizing Brain Decoding Across Subjects with Deep Learning

Decoding experimental variables from brain imaging data is gaining popul...

Deep Transfer Learning for Error Decoding from Non-Invasive EEG

We recorded high-density EEG in a flanker task experiment (31 subjects) ...

Exploiting the Brain's Network Structure for Automatic Identification of ADHD Subjects

Attention Deficit Hyperactive Disorder (ADHD) is a common behavioral pro...

Boosting Template-based SSVEP Decoding by Cross-domain Transfer Learning

Objective: This study aims to establish a generalized transfer-learning ...

Subject-Independent Brain-Computer Interface for Decoding High-Level Visual Imagery Tasks

Brain-computer interface (BCI) is used for communication between humans ...

Transferring Subspaces Between Subjects in Brain-Computer Interfacing

Compensating changes between a subjects' training and testing session in...

Fast Optimal Transport Averaging of Neuroimaging Data

Knowing how the Human brain is anatomically and functionally organized a...

Please sign up or login with your details

Forgot password? Click here to reset