README.md

metadata

license: pddl
tags:
  - eeg
  - medical
  - clinical
  - classification
  - parkinson
  - simon task

Singh2018: EEG Parkinson's Classification Dataset with Modified Simon Task

The Singh2018 dataset includes EEG recordings collected during a cognitive control task from 28 individuals with Parkinson's disease (PD) and 28 demographically matched healthy controls. Each participant completed a modified Simon reaction-time task designed to elicit response conflict and error-related cognitive control processes. PD patients participated in two sessions (ON and OFF dopaminergic medication), spaced one week apart, while controls participated in a single session.

EEG was recorded from 60 scalp electrodes using a Brain Vision system at a sampling rate of 500 Hz.

Paper

Singh, A., Richardson, S. P., Narayanan, N., & Cavanagh, J. F. (2018). Mid-frontal theta activity is diminished during cognitive control in Parkinson's disease. Neuropsychologia, 117, 113-122.

DISCLAIMER: We (DISCO) are NOT the owners or creators of this dataset, but we merely uploaded it here, to support our's (EEG-Bench) and other's work on EEG benchmarking.

Dataset Structure

• data/ contains the annotated experiment EEG data.
• scripts/ contains MATLAB scripts used by the authors to process the dataset for their paper's purposes. Included for reference.
• PD_CONFLICT_VARS.xlsx contains some information about the patients with PD (sex, age, medication status at first recording), as well as their matched control patients.
• DataDictionary.txt contains information about the naming of the events. Note that the uploader of this dataset is missing confirmation that DataDictionary.txt provides a correct description of event types, though the order and types of events seem to match and highly resemble that of the Albrecht2019 dataset, from which this DataDictionary.txt file was taken.

Filename Format

[PID]_Session_[SESSION]_PDDys_CC.mat

PID is the patient ID (e.g. 801), while SESSION distinguishes different days of recording (can be 1 or 2 for patients with PD and is always 1 for patients without PD). All patients with PID <= 829 have Parkinson's Disease and all patients with PID >= 890 do NOT have Parkinson's Disease and hence belong to the control group.

Fields in each File

A .mat file can be read in python as follows:

from scipy.io import loadmat
filename = "801_Session_2_PDDys_CC.mat"
mat = loadmat(filename, simplify_cells=True)

(A field "fieldname" can be read from mat as mat["fieldname"].)

Then mat contains (among others) the following fields and subfields

• EEG
  • data: EEG data of shape (#channels, trial_len, #trials). E.g. a recording of 860 trials/epochs with 60 channels, each trial having a duration of 6.5 seconds and a sampling rate of 500 Hz will have shape (60, 3250, 860).
  • event: Contains a list of dictionaries, each entry (each event) having the following description:
    • latency: The onset of the event, measured as the index in the merged time-dimension #trials x trial_len (note #trials being the outer and trial_len being the inner array when merging).
    • type: The type of event. This type will consist of up to 3 digits. In order to decode them, please refer to the Triggers section in DataDictionary.txt. Typically, a trial starts with a Stimulus Trigger, followed by a Response Trigger (or 105 if no response was given, i.e. the patient did not press a button) and then a Feedback Trigger.
  • chanlocs: A list of channel descriptors
  • nbchan: Number of channels
  • trials: Number of trials/epochs in this recording
  • srate: Sampling Rate (Hz)

Additionally, the field and bad_chans lists bad channels of this recording.

License

By the original authors of this work, this work has been licensed under the PDDL v1.0 license (see LICENSE.txt).