Decoding invariant visual information with MEG sensor and source data

Magnetoencephalography (MEG) decoding analysis is a powerful tool for studying the human visual system. Previous work has shown that sizeand position-invariant visual signals can be decoded from MEG sensor-level data. Source localization results would allow us to answer more precise anatomical questions about invariant object recognition, but these interpretations may be limited by the accuracy of the source localization. Here we compare MEG decoding analysis using features in sensor and source space in a sizeand position-invariant visual decoding task in order to both assess the promise of decoding in source space and attempt to gain a better spatiotemporal profile of invariant object recognition in humans.

[1]  Joel Z. Leibo,et al.  The dynamics of invariant object recognition in the human visual system. , 2014, Journal of neurophysiology.

[2]  David A. Tovar,et al.  Representational dynamics of object vision: the first 1000 ms. , 2013, Journal of vision.

[3]  Ethan Meyers,et al.  The neural decoding toolbox , 2013, Front. Neuroinform..

[4]  Riitta Salmelin,et al.  Tracking neural coding of perceptual and semantic features of concrete nouns , 2012, NeuroImage.

[5]  Emery N. Brown,et al.  A spatiotemporal dynamic distributed solution to the MEG inverse problem , 2011, NeuroImage.

[6]  T. Carlson,et al.  High temporal resolution decoding of object position and category. , 2011, Journal of vision.

[7]  Richard M. Leahy,et al.  Brainstorm: A User-Friendly Application for MEG/EEG Analysis , 2011, Comput. Intell. Neurosci..

[8]  John C. Mosher,et al.  Anatomically and Functionally Constrained Minimum-Norm Estimates , 2010 .

[9]  Polina Golland,et al.  A distributed spatio-temporal EEG/MEG inverse solver , 2009, NeuroImage.

[10]  Thomas Serre,et al.  A feedforward architecture accounts for rapid categorization , 2007, Proceedings of the National Academy of Sciences.

[11]  Patrick Suppes,et al.  Single-Trial Classification of MEG Recordings , 2007, IEEE Transactions on Biomedical Engineering.

[12]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[13]  Nikos Makris,et al.  Automatically parcellating the human cerebral cortex. , 2004, Cerebral cortex.

[14]  Anders M. Dale,et al.  Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex , 2001, IEEE Transactions on Medical Imaging.

[15]  E. Somersalo,et al.  Visualization of Magnetoencephalographic Data Using Minimum Current Estimates , 1999, NeuroImage.

[16]  Denis Fize,et al.  Speed of processing in the human visual system , 1996, Nature.

[17]  M. Potter.  Short-term conceptual memory for pictures. , 1976, Journal of experimental psychology. Human learning and memory.