Research data supporting: Effects of alertness on perceptual detection and discrimination
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Change log
Description
Four datasets of perceptual decision-making: 1 - auditory masking detection; 2 - TMS sensorimotor detection; 3 - auditory spatial discrimination; and 4 - auditory phoneme discrimination. Data was split by four methods of alertness classification: Micro-measures, EEG θ/α power, reaction times, and the proportion of omissions.
For more information and folder organisation refer to the README file located within the "main_folder".
Version
Software / Usage instructions
MATLAB, R
To open .FDT and .SET files use EEGLAB, an open-source MATLAB toolbox. You can download it from: https://sccn.ucsd.edu/eeglab/download.php.
Then add to MATLAB’s path and launch:
addpath('path_to_eeglab_folder');
eeglab;
Once launched in MATLAB, you can simply use the graphical interface (File > Load existing dataset) or the command:
EEG = pop_loadset('filename', 'your_file.set', 'filepath', 'path_to_folder/');
Note: you only need to load the .SET file, and it will automatically handle the associated .FDT file if they're in the same directory.
An open-source alternative is the MNE-Python package (https://mne.tools/stable/install/index.html). It can read EEGLAB files using its read_raw_eeglab() function:
testing_data_folder = mne.datasets.testing.data_path()
eeglab_raw_file = testing_data_folder / "EEGLAB" / "test_raw.set"
eeglab_raw = mne.io.read_raw_eeglab(eeglab_raw_file)
Publisher
Rights and licensing
Except where otherwised noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)