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EEG data
No full textBelow you can see and download the preprocessed (band-pass filtered 0.1–200 Hz and notch-filtered at 50 Hz), epoched (around both stimulus and response) EEG data of the project in ".mat" format readable in Matlab.
"chan_locations" provide channel labels.
"EEG_signals" provide the data samples for the 64 channels aligned to the stimulus onset time (row=1) and response time (row=2) for session 1 (column =1) and session 2 (column =2) for the 240 stimuli presented to the participant. The sampling frequency is 1000 Hz and the simulus-aligned data show the samples from -500 to 1499 ms after the stimulus onset time and response-aligned data show the samples from -1499 before to 500 ms after the response time.
* There is only one session of data for subject #13.
"Exp_data" provides all the information about the screen, stimuli and the participants' responses for the two sessions (columns).
"ResponseData" within "Exp_data" provides information about each presented stimulus and response for every trial. Rows of "ResponseData.Values" are the participant's reaction time, correct/incorrect responses (1/0), the button pressed (in ASCII format), the level of simulus coherence (0.22,0.30,0.45,0.55) and stimulus category (unfamiliar=1; famous=2; self=3; personally familiar=4).
Please contact us if you have any questions at:
[email protected]
[email protected]
Please cite as:
Hamid Karimi-Rouzbahani, Farzad Ramezani, Alexandra Woolgar, Anina Rich, Masoud Ghodrati, "Perceptual difficulty modulates the direction of information flow in familiar face recognition", NeuroImage, Volume 233, 2021, 117896
EEG data
No full textBelow you can see and download the preprocessed (band-pass filtered 0.1–200 Hz and notch-filtered at 50 Hz), epoched (around both stimulus and response) EEG data of the project in ".mat" format readable in Matlab.
"chan_locations" provide channel labels.
"EEG_signals" provide the data samples for the 64 channels aligned to the stimulus onset time (row=1) and response time (row=2) for session 1 (column =1) and session 2 (column =2) for the 240 stimuli presented to the participant. The sampling frequency is 1000 Hz and the simulus-aligned data show the samples from -500 to 1499 ms after the stimulus onset time and response-aligned data show the samples from -1499 before to 500 ms after the response time.
* There is only one session of data for subject #13.
"Exp_data" provides all the information about the screen, stimuli and the participants' responses for the two sessions (columns).
"ResponseData" within "Exp_data" provides information about each presented stimulus and response for every trial. Rows of "ResponseData.Values" are the participant's reaction time, correct/incorrect responses (1/0), the button pressed (in ASCII format), the level of simulus coherence (0.22,0.30,0.45,0.55) and stimulus category (unfamiliar=1; famous=2; self=3; personally familiar=4).
Please contact us if you have any questions at:
[email protected]
[email protected]
Please cite as:
Hamid Karimi-Rouzbahani, Farzad Ramezani, Alexandra Woolgar, Anina Rich, Masoud Ghodrati, "Perceptual difficulty modulates the direction of information flow in familiar face recognition", NeuroImage, Volume 233, 2021, 117896
EEG datasets
No full textHere you can download the two of the EEG datasets (Datsets 1 and 2) used in the two publications.
The datasets are in Matlab ".mat" format.
Please read the readme files within the datasets to understand how to use them.
Please cite:
Karimi-Rouzbahani, H., Shahmohammadi, M., Vahab, E., Setayeshi, S. and Carlson, T., 2021.
Temporal variabilities provide additional category-related information in object category
decoding: a systematic comparison of informative EEG features. Neural Computation.
The third dataset can be downloaded from this address: https://purl.stanford.edu/tc919dd538
EEG datasets
No full textHere you can download the two of the EEG datasets (Datsets 1 and 2) used in the two publications.
The datasets are in Matlab ".mat" format.
Please read the README files within the dataset folders to understand how to use them.
Please cite:
Karimi-Rouzbahani, H., Shahmohammadi, M., Vahab, E., Setayeshi, S. and Carlson, T., 2021.
Temporal variabilities provide additional category-related information in object category
decoding: a systematic comparison of informative EEG features. Neural Computation.
The third dataset can be downloaded from this address: https://purl.stanford.edu/tc919dd538
Scripts_Temporal variabilities provide additional category-related
No full textYou can download the MATLAB ".m" scripts used in "Karimi-Rouzbahani, H., Shahmohammadi, M., Vahab, E., Setayeshi, S. and Carlson, T., 2021.
Temporal variabilities provide additional category-related information in object category
decoding: a systematic comparison of informative EEG features. Neural Computation." below.
Read the README file within the zip file to understand how the scripts work.
Please cite as:
Karimi-Rouzbahani, H., Shahmohammadi, M., Vahab, E., Setayeshi, S. and Carlson, T., 2021.
Temporal variabilities provide additional category-related information in object category
decoding: a systematic comparison of informative EEG features. Neural Computation
EEG datasets
No full textHere you can download the two of the EEG datasets (Datsets 1 and 2) used in the two publications.
The datasets are in Matlab ".mat" format.
Please read the README files within the dataset folders to understand how to use them.
Please cite:
Karimi-Rouzbahani, H., Shahmohammadi, M., Vahab, E., Setayeshi, S. and Carlson, T., 2021.
Temporal variabilities provide additional category-related information in object category
decoding: a systematic comparison of informative EEG features. Neural Computation.
The third dataset can be downloaded from this address: https://purl.stanford.edu/tc919dd538
Familiar-Unfamiliar Face Categorization Task demo
No full textDemo video of the Familiar-Unfamiliar Face Categorization Task: Participants performed a familiar vs. unfamiliar face categorization task by categorizing dynamically updating sequences of either familiar or unfamiliar face images in two recording sessions. Image sequences were presented in rapid serial visual presentation (RSVP) fashion at a frame rate of 60 Hz frames per second (i.e.,16.67 ms per frame without gaps). Each trial consisted of a single sequence of up to 1.2 seconds (until response) with a series of images from the same stimulus (i.e., selection from either familiar or unfamiliar face categories) at one of the four possible phase coherence levels. We instructed participants to fixate at the center of the monitor and respond as accurately and quickly as possible by pressing one of two keyboard keys (left and right arrow keys) to identify the image as familiar or unfamiliar using the right index and middle fingers, respectively (the response key counterbalanced). As soon as a response was given, the RSVP sequence stopped, followed by an inter-trial interval of 1–1.2 s (random with uniform distribution). The maximum time for the RSVP sequence was 1.2 secs. If participants failed to respond within the 1.2 secs period, the trial was marked as a no-choice trial and was excluded from further analysis. The following demo presents 10 sample trials from the experiment with 7 responded trials and 3 no-choice trials (trials 1, 2 and 7)
Analysis of information exchange in the multiple-demand network using fMRI-MEG fusion and connectivity
No full text
Scripts_Temporal variabilities provide additional category-related
No full textYou can download the MATLAB ".m" scripts used in "Karimi-Rouzbahani, H., Shahmohammadi, M., Vahab, E., Setayeshi, S. and Carlson, T., 2021.
Temporal variabilities provide additional category-related information in object category
decoding: a systematic comparison of informative EEG features. Neural Computation." below.
Read the README file within the zip file to understand how the scripts work.
Please cite as:
Karimi-Rouzbahani, H., Shahmohammadi, M., Vahab, E., Setayeshi, S. and Carlson, T., 2021.
Temporal variabilities provide additional category-related information in object category
decoding: a systematic comparison of informative EEG features. Neural Computation
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