---
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](https://github.com/ETH-DISCO/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:
```python
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).