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This is the third version for Megocclusion dataset: RepOD. Data for the mask and no-mask conditions were mislabeled in the previous version (Megocclusion-vr2: RepOD. This is now fixed in the new version.


This data is from a MEG study investigating the temporal dynamics of object recognition under the challenging condition of occlusion. For more information about experimental design and data acquisition method refer to the preprint article: Karim Rajaei, Yalda Mohsenzadeh, Reza Ebrahimpour, Seyed-Mahdi Khaligh-Razavi. Beyond Core Object Recognition: Recurrent processes account for object recognition under occlusion. doi:

Please refer to: for an implementing code that generate the decoding curve of Figure 1 in Rajaei 2018/2019 doi:

Participants and MEG experimental design

Fifteen young volunteers (22-38 year-old, all right-handed; 7 female) participated in the experiment. During the experiment, participants completed eight runs; each run consisted of 192 trials and lasted for approximately eight minutes (total experiment time for each participant = ~70min). Each trial started with 1sec fixation followed by 34ms (2 x screen frame rate (17ms) = 34ms) presentation of an object image (6° visual angle). In half the trials, we employed backward masking in which a dynamic mask was presented for 102ms shortly after the stimulus offset—inter-stimulus-interval (ISI) of 17ms. In each run, each object image (i.e. camel, deer, car, motor) was repeated 8 times under different levels of occlusions without backward masking; and another 8 repetitions with backward masking. In other words, each condition (i.e. a combination of object-image, occlusion-level, mask or no-mask) was repeated 64 times over the duration of the whole experiment.

MEG acquisition

To acquire brain signals with millisecond temporal resolution, we used 306-sensors MEG system (Elekta Neuromag, Stockholm). The sampling rate was 1000Hz and band-pass filtered online between 0.03 and 330 Hz. To reduce noise and correct for head movements, raw data were cleaned by spatiotemporal filters [Maxfilter software, Elekta, Stockholm; (Taulu and Simola, 2006)]. Further pre-processing was conducted by Brainstorm toolbox (Tadel et al., 2011). Trials were extracted -200ms to 1000ms relative to the stimulus onset. For each participant, there is a mat file containing MEG signals for 24 conditions. Each condition consists of a matrix of 306-sensors x 1201-timepoints x 64-repetitions.

Publisher: RepOD

Publication year: 2019

Related publication:

Type of resource: Dataset

Area of study: Medical and health sciences

License for files: CC0-1.0

Files in this dataset


Author Affiliation
Rajaei, Karim School of Cognitive Sciences (SCS), Institute for Research in Fundamental Sciences (IPM), Niavaran, P.O. Box 19395-5746, Tehran, Iran
Khaligh-Razavi, Seyed-Mahdi Computer Science and AI Lab (CSAIL), MIT, Cambridge, MA, US

Cite this dataset as:

Rajaei, K.; Khaligh-Razavi, S. (2019) Megocclusion-vr3. RepOD.

Publicly available in RepOD since: 2019-03-14 14:14 (CET)

Download the dataset citation