README.md

metadata

license: pddl
tags:
  - eeg
  - medical
  - clinical
  - classification
  - mtbi
  - tbi
  - oddball

Cavanagh2019: EEG mTBI Classification Dataset with Auditory Oddball Task

The Cavanagh2019 dataset includes EEG recordings collected during a 3-stimulus auditory oddball paradigm in participants with mild traumatic brain injury (mTBI) and matched healthy controls. A total of 96 participants took part: 45 sub-acute mTBI patients (tested within 2 weeks post-injury), 26 healthy controls, and 25 chronic TBI patients (mild to moderate severity). Sub-acute mTBI and control participants completed two or three EEG sessions - at 3-14 days after the injury, and again after approximately 2 months - while chronic TBI participants completed a single session.

The task involved 260 trials: 70% standard tones (440 Hz), 15% target tones (660 Hz), and 15% novel naturalistic sounds. Stimuli were presented binaurally, and participants were instructed to count target tones while ignoring the others. EEG was recorded from 60 channels at a 500 Hz sampling rate.

Paper

Cavanagh, J. F., Wilson, J. K., Rieger, R. E., Gill, D., Broadway, J. M., Remer, J. H. S., Fratzke, V., Mayer, A. R., & Quinn, D. K. (2019). ERPs predict symptomatic distress and recovery in sub-acute mild traumatic brain injury. Neuropsychologia, 132, 107125.

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 that produced the paper's results.
• scripts/BigAgg_Data.mat contains information about the subjects.
• scripts/QUALITY_CHECK.xlsx and scripts/QUINN_QUALITY_CHECK.xlsx contain information about bad quality recordings.

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

from scipy.io import loadmat
mat = loadmat(filepath, simplify_cells=True)

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

Subject information can be read from scripts/BigAgg_Data.mat from the following fields (among others):

• DEMO: information about mTBI and control subjects
  • ID: subject IDs, as included in the filename of the corresponding EEG recording under data/
  • Group_CTL1: for each subject, whether it belongs to the control group (which is the case if and only if the corresponding Group_CTL1-entry is 1) or not
  • Sex_F1: gender of the subject (1 means female, everything else means male)
  • Age: age of the subject
• Q_DEMO: information about chronic TBI subjects
  • URSI: subject IDs, as included in the filename of the corresponding EEG recording under data/
  • Sex_F1: gender of the subject (1 means female, everything else means male)
  • Age: age of the subject
• NP: mTBI and control subjects' TOMM, TOPF, HVLT and other scores
• Q_NP: chronic TBI subjects' TOMM, TOPF, HVLT and other scores
• QUEX: mTBI and control BDI and other scores
• Q_QUEX: chronic TBI BDI and other scores
• TBIfields: information about mTBI subjects' injury
• Q_TBIfields: information about chronic TBI subjects' injury

Filename Format

[PID]_[SESSION]_3AOB.mat    (or [PID]_[SESSION]_QUINN_3AOB.mat for chronic TBI participants)

PID is the patient ID (e.g. 3001), while SESSION distinguishes different days of recording (can be 1, 2 or 3 for patients with mTBI or control patients and is always 1 for patients with chronic TBI).

Fields in each File

Let mat be an EEG .mat file from the data/ directory. 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 247 trials/epochs with 60 channels, each trial having a duration of 4 seconds and a sampling rate of 500 Hz will have shape (60, 2000, 247).
  • 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). The duration of each event is 200ms. Hence, with a 500 Hz sampling rate, the EEG data event_data corresponding to the i-th event is

      start_index = mat["EEG"]["event"][i]["latency"]
      event_data = numpy.transpose(mat["EEG"]["data"], [1, 2]).reshape([num_channels, num_trials * trial_len])[:, start_index:start_index+100]    # shape (#channels, 100)
      

    • type: The type of event. Can be "S200" (660 Hz tone), "S201" (440 Hz tone) or "S202" (naturalistic).
  • chanlocs: A list of channel descriptors
  • nbchan: Number of channels
  • trials: Number of trials/epochs in this recording
  • 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).