Animal non-animal categorization data

On this page is a collection of 32-channel data from 14 subjects (7 males, 7 females) acquired using the Neuroscan software. Subjects are performing a go-nogo categorization task and a go-no recognition task on natural photographs presented very briefly (20 ms). Each subject responded to a total of 2500 trials. Data is CZ referenced and is sampled at 1000 Hz (total data size is 4Gb; more details are given later). This page contains the original data for historical and archival purpose. The current data is availlable on Openneuro.

Downloading the data

14 subjects recorded in the original study are available for download here (total 3.4 Gb; thanks to Marc Macé who helped retrieving the data). Each archive file contains two consecutive days of recording for one subject. Use Winzip under windows and tar under Linux to uncompress archive files.

The most interesting data in this task is the animal/non-animal categorization. These files represent a subset of the files distributed above (see also the task description on the previous page). Files containing data where subjects performed this categorization only are available in the stimulus folder of the NEMAR dataset (the NEMAR dataset contains the same data but organized according to the BIDS data format).

Experimental Procedure

Participants seated in a dimly lit room at 110 cm from a computer screen piloted from a PC computer. Two tasks alternated: a categorization task and a recognition task. In both tasks, target images and non-target images were equally likely presented. Participants were tested in two recording phases. The first day was composed of 13 series, the second day of 12 series, with 100 images per series (see details of the series below). To start a series, subjects had to press a touch-sensitive button. A small fixation point (smaller than 0.1° of visual angle) was drawn in the middle of a black screen. Then, an 8 bit color vertical photograph (256 pixels wide by 384 pixels high which roughly correspond to 4.5° of visual angle in width and 6.5° in height) was flashed for 20 ms (2 frames of a 100 Hz SVGA screen) using a programmable graphic board (VSG 2.1, Cambridge Research Systems). This short presentation time avoid that subjects use exploratory eye movement to respond. Participants gave their responses following a go/nogo paradigm. For each target, they had to lift their finger from the button as quickly and accurately as possible (releasing the button restored a focused light beam between an optic fiber led and its receiver; the response latency of this apparatus was under 1 ms). Participants were given 1000 ms to respond, after what any response was considered as a nogo response. The stimulus onset asynchrony (SOA) was 2000 ms plus or minus a random delay of 200 ms. For each distractor, participants had to keep pressing the button during at least 1000 ms (nogo response).

More specifically, in the animal categorization task, participants had to respond whenever there was an animal in the picture. In the recognition task, the session started with a learning phase. A probe image was flashed 15 times during 20 ms intermixed with two presentations of 1000 ms after the fifth and the tenth flashes, allowing an ocular exploration of the image; with an inter-stimulus of 1000 ms. Participants were instructed to carefully examine and learn the probe image in order to recognize it in the following series. The test phase started immediately after the learning phase. The probe image constituted the unique target of the series. Both tasks were organized in series of 100 images; 50 targets images were mixed with 50 non-targets in the animal categorization task; 50 copies of an unique photographs were mixed at random with 50 non-targets in the recognition task.


The pictures were photographs of natural scenes (Corel CD-ROM library; images available for viewing on the download page). The images of each category were chosen to be as varied as possible. The animal category included pictures of mammals, birds, fishes, arthropods, and reptiles. There was no a priori information about the size, position or number of the targets in a single photograph. There were also a wide range of non-target images, with outdoor and indoor scenes, natural landscapes or city scenes, pictures of food, fruits, vegetables, trees and flowers... In the categorization task, 500 distractors and 500 targets were seen by every subject but randomly distributed among all 10 series. In the recognition task, 750 distrators and 210 target photographs were used (15 target photographs per subject). Target photographs were chosen according to the results of a previous study (Fabre-Thorpe et al., 2001). The first group of 70 images (5 per subjects) was composed of the animal images out of 1000 which were correctly categorized by all subjects and were associated with the fastest RTs). The second group of 70 images was composed of animal images which had the lowest accuracy and associated with the longest RTs). In the last group, pictures contained no animal, i.e. these pictures were distractors of the categorization task. 

EEG Recording

Electric brain potentials were recorded from 32 electrodes mounted on a elastic cap (Oxford Instruments). Electrode Cz was used as reference and a mastoid electrode was used as ground (details of electrode positions are available on the download page). Data acquisition was made at 1000 Hz (corresponding to a sample bin of 1 ms) using a SynAmps recording system coupled with a PC computer. Impedances were kept below 5 kOhms.

Data organization

25 groups of file, each group of file corresponding to 100 trials (13 group of file for session of day 1 and 12 groups of file for session of day 2), were recorded for each subject. In the list below, "xxxDffNN" indicates the base file name for each group of file (each group of file containing 3 files of different type as described below). "xxx" indicates the initials of each subject; "D" represents the day of recording (1 or 2); "NN" represents the base file number; "ff" is meaningless. For instance "cba1ff04" is the base file name for file number 4 of subject "cba" on day 1. For each base file name, 3 files are present in the archive, one file with the extension ".CNT" for the raw data, one file with the extension ".DAT" and ".EXP" containing additional information about the data trials (see next paragraph). For day 1 the file generated for each subject "xxx" are
  • xxx1ff01 - File 1 contains categorization task (of animal)
  • xxx1ff02 - File 2 contains recognition of unique "hard" animal image
  • xxx1ff03 - File 3 contains recognition of unique "easy" animal image
  • xxx1ff04 - File 4 contains categorization task (of animal)
  • xxx1ff05 - File 5 contains recognition of unique non-animal image
  • xxx1ff06 - File 6 contains recognition of unique "hard" animal image
  • xxx1ff07 - File 7 contains categorization task (of animal)
  • xxx1ff08 - File 8 contains recognition of unique "easy" animal image
  • xxx1ff09 - File 9 contains recognition of unique non-animal image
  • xxx1ff10 - File 10 contains categorization task (of animal)
  • xxx1ff11 - File 11 contains recognition of unique "easy" animal image
  • xxx1ff12 - File 12 contains recognition of unique "hard" animal image
  • xxx1ff13 - File 13 contains categorization task (of animal)
For day 2,
  • xxx2ff01 - File 1 contains categorization task (of animal)
  • xxx2ff02 - File 2 contains recognition of unique non-animal animal image
  • xxx2ff03 - File 3 contains recognition of unique "hard" animal image
  • xxx2ff04 - File 4 contains categorization task (of animal)
  • xxx2ff05 - File 5 contains recognition of unique "easy" animal image
  • xxx2ff06 - File 6 contains recognition of unique non-animal image
  • xxx2ff07 - File 7 contains categorization task (of animal)
  • xxx2ff08 - File 8 contains recognition of unique "hard" animal image
  • xxx2ff09 - File 9 contains recognition of unique "easy" image
  • xxx2ff10 - File 10 contains categorization task (of animal)
  • xxx2ff11 - File 11 contains recognition of unique non-animal image
  • xxx2ff12 - File 12 contains categorization task (of animal)
Each base file name above corresponds to the presentation of 100 images (50 targets and 50 distractors) and generates 3 files with different extensions ".CNT", ".DAT" and ".EXP" as described below:
  • a .CNT file generated by the Neuroscan software contains RAW continuous data and simple events indicating the time of presentation of stimuli
  • a .DAT file containing information for each data epoch (this file is compatible with the Neuroscan software but is generated by the computer presenting stimuli). The first 20 lines of a .DAT file contain standard fields for the experiment (note that the name of the task (line 2) is "animal/non-animal" in all files and should be ignored (this is not accurate since some sessions did not involve "animal/non-animal" categorization)). The rest of the file is organized in 5 columns
    • Event (column 1): trial number (starting at 0)
    • Resp (column 2): image type (1=target; 0=distractor)
    • Type (column 3): trial type. The first 2 digits "12" are irrelevant. The last two digits indicate trial numbers (redundant with column 1). The third digit is the most important one and can take the following values
      • 0 = target in the animal categorization task
      • 1 = target in the "easy" animal recognition task
      • 2 = target in the "hard" animal recognition task
      • 3 = target in the non-animal recognition task
      • 4 (not used)
      • 5 = distractor in the animal categorization task
      • 6 = distractor in the "easy" animal recognition task
      • 7 = distractor in the "hard" animal recognition task
      • 8 = distractor in the non-animal recognition task
      For instance 12513 indicates that the images presented in the selected data trial is a distractor image in the animal categorization task (trial number is 13).
    • Correct (column 4): 0 is incorect (subject responded on a distractor or failed to respond on a target) and 1 is correct (subject responded on a target or did not respond on a distractor).
    • Latency (column 5): reaction time in millisecond. 1000 indicates no response or a response after the 1 second deadline.
  • a .EXP file containing extra information compared to a .DAT file (this file format is custom to our laboratory and is not compatible with the Neuroscan software). A .EXP file contains the following columns
    • Column 1: trial number
    • Column 2: COREL draw image series
    • Column 3: COREL draw image numbers. Note that images shown in the experiment are available for viewing. Fill form below and press SUBMIT on the download page so you may actually see images corresponding to each each trial.
    • Column 4: Subject response code "C" correct distractor; "P" missed distrator; "R" correct target; "M" missed target
    • Column 5: 0=distractor; 1=target
    • Column 6: Trial type (same as in .DAT file; see .DAT file information above)
    • Column 7: Response time in milliseconds (1000=no response or a response after the 1 second deadline)
    • Column 8: Inter-Stimulus-Interval in milliseconds

Reading/processing the data

The publicly available eeglab software allows you to import this data under Matlab. To import the raw Neuroscan CNT files, first use menu "File > Import data > From Neuroscan CNT file". Simply press enter. Then extract data epochs using menu "Edit > Extract epochs" (simply press OK). Then use menu "File > Import epoch > From Neuroscan .DAT file" to import epoch information. You are now ready to analyse the data (you might want to start by concatenating files from each subject (menu "Edit > Meger dataset")). Electrode locations and electrode names (as stored in the original .CNT raw data file along with the 10-20 system correspondence) are available as an Excel file here (a channel location file compatible with the EEGLAB software is also available delorme_locfile.loc, and it may be read in EEGLAB using menu "Edit > Channel location".

Preprocessed data

The The EEGLAB study tutorial also contains a "STUDY" containing some of the target and distracter trials for 10 subjects in this task. The study is availaible here (380 Mb. The EEGLAB script that was used to process these subjects and generate the datasets for the study is available here. Note that this data has already been pre-processed and the raw Neuroscan .CNT files have been removed.


Images presented during the experiment are available here for target images and here for non-target images (you may not download or copy these images; The Corel images on this site are for viewing only and may not be downloaded or saved. They were purchased by the CERCO CNRS laboratory to use for psychophysics research, and under the terms of our licensing agreement, we cannot sell or give away these images).


ERP analysis on this data has been published in
Delorme, A., Rousselet, G., Mace, M., Fabre-Thorpe M. Interaction of Bottom-up and Top-down processing in the fast visual analysis of natural scenes. Cognitive Brain Research, 103-113. Author's PDF, Science direct
Note that this data has also been used to generate brain dynamic animation in
Delorme, A., Makeig, S., Fabre-Thorpe, M. Sejnowski, T. (2002) From Single-trials EEG to Brain Area Dynamics, Neurocomputing, 44-46, 1057-1064. Author's PDF, Science Direct
which is the first paper to compute synchronization between brain source activities separated using ICA.

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