README.md

metadata

license: pddl
tags:
  - eeg
  - medical
  - clinical
  - classification
  - parkinson
  - reward processing

Brown2020: EEG Parkinson's Classification Dataset with Reward Processing Task

The Brown2020 dataset comprises EEG recordings from a reinforcement learning task aimed at assessing reward processing in individuals with Parkinson's disease (PD) and healthy controls. A total of 56 participants took part: 28 individuals diagnosed with PD and 28 age- and sex-matched control participants. Each PD participant completed two sessions (ON and OFF dopaminergic medication), spaced one week apart. Control participants completed a single session.

Participants performed a reinforcement learning task involving probabilistic feedback. On each trial, a pair of colored stimuli was presented, with each stimulus associated with a predefined probability of reward. Conditions were manipulated along two dimensions: difficulty (90/10% vs. 70/30% reward probability) and volition (free choice vs. instructed choice). The EEG was time-locked to the feedback screen, allowing for the measurement of reward-related event-related potentials (ERPs).

EEG data were recorded using a 64-channel Brain Vision system at a sampling rate of 500 Hz.

Paper

Brown, D. R., Richardson, S. P., & Cavanagh, J. F. (2020). An EEG marker of reward processing is diminished in Parkinson’s disease. Brain research, 1727, 146541.

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.
• MEASURES.xlsx and ONOFF.mat contain subject-specific information like NAART test results, BDI and whether they were ON or OFF medication at their first visit (ONOFF.mat). See PD_RewP_Script.m for information on how to decode these files.
• scripts/ contains the MATLAB files used to execute the experiment.
• images/ contains the stimuli and visuals presented to the patients.

Filename Format

[PID]_Session_[SESSION]_PDDys_VV_withcueinfo.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_VV_withcueinfo.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 119 trials/epochs with 60 channels, each trial having a duration of 8 seconds and a sampling rate of 500 Hz will have shape (60, 4000, 119).
  • 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. It can be either:

      • "S 1": An instruction to freely choose a stimulus is shown

      • "S 2": An instruction to select the stimulus with a box around it is shown

      • "S 3": A stimulus pair is shown on the screen

      • "S 4": The patient presses the left button

      • "S 5": The patient presses the right button

      • "S 6": A message is shown that the button pressed by the patient did not match the stimulus with a box around it (perhaps this event is also shown when the button is pressed too early)

      • "S 7": A message is shown that the patient did not press any button in the required time-interval (4 seconds)

      • "S 10": A red 0 (representing "no reward") is shown on the screen

      • "S 11": A green 1 (representing "a reward of 1 point") is shown on the screen

        Typically, a trial starts with an instruction (S 1 or S 2), followed by a pair of stimuli shown on the screen (S 3), a button being pressed by the patient (S 4 or S 5) and a reward being displayed (S 10 or S 11).

  • 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).