Tavakolan2017 — Three-class upper extremity motor imagery EEG dataset (re-hosted from Dryad)
Authors/Creators
- 1. Simon Fraser University
Description
Re-hosted EEG data from Tavakolan et al. (2017), originally deposited on Dryad (DOI: 10.5061/dryad.6qs86) under a CC0 public domain dedication. Repackaged into per-subject ZIP archives for easier programmatic access.
Paper
M. Tavakolan, Z. Frehlick, X. Yong, and C. Menon, “Classifying three imaginary states of the same upper extremity using time-domain features,” PLoS ONE, vol. 12, no. 3, e0174161, 2017.
DOI: 10.1371/journal.pone.0174161
Paradigm
Three-class motor imagery of the same upper extremity (right arm):
| Class | StimulusCode | Description |
|---|---|---|
| REST | 1 | Relax without any movement imagery |
| MI-GRASP | 2 | Imagine opening and closing all fingers to grab an object |
| MI-ELBOW | 3 | Imagine moving the forearm up and down (elbow flexion/extension) |
A fourth class (Reach-Hold the Glass, StimulusCode 4) was also recorded but excluded from the paper’s analysis. It is included in the raw data.
Each trial: 3 s visual cue (imagery period), followed by a 4–6 s rest interval. 20 trials per class per session.
Participants
- 12 healthy right-handed subjects
- 4 sessions per subject on separate days
- Total: 48 recordings (12 subjects × 4 sessions)
Recording setup
| Parameter | Value |
|---|---|
| System | EGI Geodesic Net Amps 400 series |
| Cap | 32-channel GSN-HydroCel Geodesic Sensor Net |
| Channels | 32 EEG (E1–E32), Cz as online reference |
| Sampling rate | 1000 Hz |
| Bandpass | 0.1–100 Hz |
| Impedance | < 50 kΩ |
| Line frequency | 60 Hz (Canada) |
| File format | BCI2000 (.DAT) |
File structure
12 per-subject ZIP files (P01.zip–P12.zip). Each contains 4 session ZIPs, each holding one BCI2000 .DAT file:
P01.zip P01_Se01.zip → BCI2000 .DAT (session 1) P01_Se02.zip → BCI2000 .DAT (session 2) P01_Se03.zip → BCI2000 .DAT (session 3) P01_Se04.zip → BCI2000 .DAT (session 4) ... P12.zip
The BCI2000 .DAT files contain 280 source channels; only the first 32 are EEG. Channel gain: 0.0238419 μV per raw ADC unit. Events are in the StimulusCode state variable.
Reading the data
pip install BCI2kReader
from moabb.datasets import Tavakolan2017
from moabb.paradigms import MotorImagery
dataset = Tavakolan2017()
paradigm = MotorImagery(
events=["rest", "right_hand", "right_elbow_flexion"],
n_classes=3,
)
X, y, metadata = paradigm.get_data(dataset=dataset)
Re-hosting rationale
The original Dryad deposit requires OAuth API credentials for programmatic download, which creates a barrier for automated benchmarking pipelines. This Zenodo re-host provides direct URL access. The data is identical to the Dryad deposit — no modifications were made.
License
CC0 1.0 Universal (Public Domain Dedication), as per the original Dryad deposit.
Notes
Files
P01.zip
Files
(5.2 GB)
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Additional details
Related works
- Is derived from
- 10.5061/dryad.6qs86 (DOI)
- Is part of
- https://github.com/NeuroTechX/moabb (URL)
- Is supplement to
- 10.1371/journal.pone.0174161 (DOI)