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: https://doi.org/10.1101/302034
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.
Signals were then averaged across repetitions. 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.
