This is the dataset for corresponding Journal Article - The dynamics of invariant object recognition in the human visual system.

The human visual system can rapidly recognize objects despite transformations that alter their appearance. The precise timing of when the brain computes neural representations that are invariant to particular transformations, however, has not been mapped in humans. Here we employ magnetoencephalography decoding analysis to measure the dynamics of size- and position-invariant visual information development in the ventral visual stream. With this method we can read out the identity of objects beginning as early as 60 ms. Size- and position-invariant visual information appear around 125 ms and 150 ms, respectively, and both develop in stages, with invariance to smaller transformations arising before invariance to larger transformations. Additionally, the magnetoencephalography sensor activity localizes to neural sources that are in the most posterior occipital regions at the early decoding times and then move temporally as invariant information develops. These results provide previously unknown latencies for key stages of human-invariant object recognition, as well as new and compelling evidence for a feed-forward hierarchical model of invariant object recognition where invariance increases at each successive visual area along the ventral stream.

Dataset files can be downloaded here - http://dx.doi.org.ezproxy.canberra.edu.au/10.7910/DVN/KRUPXZ

11 subjects’ MEG data from Isik et al., 2014. Data is available in raw .fif format or in Matlab raster format that is compatible with the neural decoding toolbox (readout.info).

For Matlab code to pre-process this MEG data, and run the decoding analyses please visit

https://bitbucket.org/lisik/meg_decoding