README.md

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