README.md

metadata

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

Cavanagh2018b: EEG Parkinson's Classification Dataset in Resting-State

The Cavanagh2018b dataset contains resting-state EEG recordings from the same group of people used in the Cavanagh2018a study. Participants included 28 individuals diagnosed with Parkinson's disease and 28 age- and sex-matched control participants. Each subject underwent a 2-minute resting-state EEG recording session with eyes open. These recordings were collected in a seated posture, prior to or following the auditory oddball task, under the same EEG setup (64-channel cap, 500 Hz sampling rate).

This dataset serves as a complementary baseline condition for evaluating spontaneous brain dynamics in Parkinson's disease. Though not central to the novelty task paradigm, resting-state data may be useful for investigations into low-frequency oscillations or non-task-based classification approaches of Parkinson's disease.

Paper

Cavanagh, J. F., Kumar, P., Mueller, A. A., Richardson, S. P., & Mueen, A. (2018). Diminished EEG habituation to novel events effectively classifies Parkinson's patients. Clinical Neurophysiology, 129(2), 409-418.

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

The IMPORT_ME_REST.xlsx file contains some information about the patients with PD (sex, age, medication status at first recording), as well as their matched control patients.

Filename Format

[PID]_[SESSION]_PD_REST.mat

PID is the patient ID (e.g. 813), 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 = "813_2_PD_REST.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, time_len). E.g. a recording of 7 minutes with 67 channels and a sampling rate of 500 Hz will have shape (67, 210000).
  • 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 time-dimension time_len. The duration of each event is 1 second. Hence, with a 500 Hz sampling rate, the EEG data event_data corresponding to the i-th event would be

      start_index = mat["EEG"]["event"][i]["latency"]
      event_data = numpy.transpose(mat["EEG"]["data"], [1, 2]).reshape([#channels, #trials * trial_len])[:, start_index:start_index+500] # shape (#channels, 500)
      

    • type: The type of event. Can be "S 1" / "S 2" (patient has eyes open), or "S 3" / "S 4" (patient has eyes closed).
  • chanlocs: A list of channel descriptors
  • nbchan: Number of channels
  • srate: Sampling Rate (Hz)

License

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