DOI: 10.7910/dvn/bnnsrg
title: Public Dataset: ERP Differences in Processing Canonical and Noncanonical Finger-Numeral Configurations

description: PUBLIC DATA: ERP Differences in Processing Canonical and Noncanonical Finger-Numeral Configurations
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&lt;strong&gt;Citation for the data: &lt;/strong&gt;
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Soylu, F. (2019). Public dataset: ERP differences in processing canonical and noncanonical finger-numeral configurations. Harvard Dataverse. https://doi.org/10.7910/DVN/BNNSRG 
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&lt;strong&gt;Related bublication: &lt;/strong&gt;
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Soylu, F., Rivera, B., Anchan, M., & Shannon, N. (2019). ERP differences in processing canonical and noncanonical finger-numeral configurations. Neuroscience Letters, 705, 74–79. https://doi.org/10.1016/j.neulet.2019.04.032 
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&lt;strong&gt;Keywords:&lt;/strong&gt; Numerical cognition, Finger counting, Montring, Gestures, EEG, ERP
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&lt;strong&gt;Access to dataset (Harvard Dataverse): &lt;/strong&gt;https://doi.org/10.7910/DVN/BNNSRG
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&lt;strong&gt;Created by: &lt;/strong&gt;Firat Soylu (fsoylu@ua.edu) on 2018-03-11
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The data was collected in the ELDEN Lab (http://elden.ua.edu) at The University of Alabama, Tuscaloosa.
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&lt;strong&gt;DESCRIPTION OF DATA &lt;/strong&gt;
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The stimuli for the EEG session included 24 pictures of finger-number configurations; 4 finger montring, 4 finger counting, and 4 non-canonical finger configurations, separately for left and right hands, all showing the palm and matching with numerosities from one to four. The non-canonical configurations were based on a previous study comparing montring and non-canonical configurations (Di Luca et al., 2010). The configuration images were shot with a digital camera, and were edited to replace the background with a uniform black and to balance color and luminance. 
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The experiment included a total of 960 trials in 10 blocks, each block including 96 trials, generated by combining four sets of the 24 configurations, each of them randomized separately, which allowed an even distribution of different stimuli across each block while avoiding predictability. In each trial a configuration was presented for 500 ms, followed by a validation step, where a single-digit Arabic numeral was presented. Participants pressed one of the two buttons on the controller using either their left or right index finger to indicate whether the Arabic numeral shown matches the number presented in the preceding configuration. To counterbalance use of response buttons, participants used one of the two (right: match, left:no-match, or, left:match, right:no-match) response button configurations in the first five blocks, and the other one in the remaining five blocks, the order randomly chosen for each subject. 
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The dataset includes data from 38 participants. Please check the related publication for more information about the subjects. The analysis in the paper involves a comparison of participants who start counting on their index and thumb fingers. All subjects started counting on their right hands but differed in terms of which finger they started counting with:
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% Right-thumb starters (N=20)
subject_thumb_starter = {'1163', '1164', '1168', '1182', '1184', '1185', '1221', '1223', '1226', '1230', '1233', '1234', '1235', '1237', '1248', '1255', '1261', '1262', '1279', '1280'};
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% Right-index starters (N=18)
subject_index_starter = {'1161', '1165', '1169', '1170', '1172', '1174', '1176', '1177', '1178', '1179', '1180', '1181', '1183', '1220', '1222', '1224', '1225', '1227'};
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In addition to the grand-average ERPs for the entire sample, the analysis script generates separate grand-average ERPs for thumb-starters and index-starters.
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The EEG part of the experiment took place in a sound attenuated experiment room. Neurobs Presentation (www.neurobs.com) was used for stimulus presentation and data collection. EEG Data was collected using a BrainVision 32 Channel ActiChamp system (www.brainvision.com), with Easy Cap recording caps using Ag/AgCl electrodes. The 32 electrodes were attached according to the international 10-20 system at the locations Fp1/2, F7/8, F3/4, Fz, FT9/10, FC1/2, FC5/6, T7/8, C3/4, Cz, TP9/10, CP1/2, CP5/6, P7/8, P3/4, Pz, O1/2, Oz and recording-referenced to Cz. BrianVision Recorder was used to record data (electrode impedance&lt;10 kΩ, 0.5-70 Hz, 500 samples/sec). A custom MATLAB script using ERPLAB (http:// erpinfo.org/erplab/) and EEGLAB (http://sccn.ucsd.edu/eeglab) functions were used to analyze data. Inferential statistics was conducted with JASP (https://jasp-stats.org/). A Logitech F310 game controller was used as the input device.
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&lt;strong&gt;HOW TO USE &lt;/strong&gt;
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1) Because the total size of the compressed data is 4.5GB, the compressed file is divided into four parts, each less than 1GB. Download the four parts of the compressed data, "Soylu_2019_DataversePublicData_part_a b, c & d" and put them in the same folder.
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2) Open a terminal screen (in MAC & LINUX) and go to the folder where the four compressed files are located. Enter the command:&lt;br&gt;&lt;br&gt;
"cat Soylu_2019_DataversePublicData_part_* &gt; Soylu_2019_DataversePublicData.tar.gz"
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This will create a single compressed file "Soylu_2019_DataversePublicData.tar.gz"
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3) To uncompress the combined compressed file enter the command:&lt;br&gt;&lt;br&gt;
"tar -zxvf Soylu_2019_DataversePublicData.tar.gz"
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This will uncompress the folder. The uncompressed folder will have the raw data (already converted from the BrainVision format to EEGLAB), the scripts, and the necessary folders for the scripts to work. 
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&lt;strong&gt;SCRIPTS&lt;/strong&gt;&lt;br&gt;&lt;br&gt;
&lt;strong&gt;"AnalysisScript.m": &lt;/strong&gt;Execute the "AnalysisScript.m" script (under the "scripts" folder), which includes all steps of data analysis, to produce the ERP results reported in the related publication. You will need to replace "home_path" variable in the script with the path of the main uncompressed folder for the script to work. You will also need to have EEGLAB & ERPLAB and the stats & signal processing toolboxes installed on your MATLAB installation for the script to work.
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&lt;strong&gt;"BehavioralAnalysis.py": &lt;/strong&gt; This Python script can be run to generate a text file that aggregates the behavioral data across all participants, which then can be opened in a spreadsheet software for further analysis. This script uses the "elist.txt" files generated for each participant after the "AnalysisScript.m" script is executed.
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&lt;strong&gt;"CombineMeasures.py": &lt;/strong&gt; This Python script aggregates the ERP measures for the P1, N1, P3 components across all participants. The script uses the measurement text files for each participant generated after the "AnalysisScript.m" is run.