Megocclusion

Code for analysis of MEG occlusion data

This repository contains implementing code for the decoding curves of Figure 1 in "Beyond Core Object Recognition: Recurrent processes account for object recognition under occlusion": https://www.biorxiv.org/content/10.1101/302034v2.

Please refer to: Megocclusion-vr3 data repository (RepOD. http://dx.doi.org/10.18150/repod.2004402) for downloading MEG data of occlusion.

Related articles:

author = {Karim Rajaei, Yalda Mohsenzadeh, Reza Ebrahimpour, Seyed-Mahdi Khaligh-Razavi}, title = {Beyond Core Object Recognition: Recurrent processes account for object recognition under occlusion}, journal = {BioRXiv preprint :https://doi.org/10.1101/302034}, year = {2018}

Occlusion dataset

Sample images of occluded and un-occluded (0% occlusion) objects. There are four object categories: camel, deer, car, and motor. Images are occluded at 0% (no-occlusion), 60%, and 80% occlusion.

Results

Decoding curves obtained by running demo.m, which is average across n=15 human subjects

Usage

1. Create a folder with name "MEG Data".
2. Download MEG signals for n=15 subjects form "Megocclusion-vr3" (RepOD. http://dx.doi.org/10.18150/repod.2004402) data repository and add them to "MEG Data" .
3. Download and addpath "libsvm"
4. Run demo.m