Omkar2118/datasets huggingface dataset:
High-gamma dataset discribed in Schirrmeister et al. 2017
Full name: EEG High-Gamma Dataset

High-gamma dataset discribed in Schirrmeister et al. 2017

Our “High-Gamma Dataset” is a 128-electrode dataset (of which we later only use
44 sensors covering the motor cortex, (see Section 2.7.1), obtained from 14
healthy subjects (6 female, 2 left-handed, age 27.2 ± 3.6 (mean ± std)) with
roughly 1000 (963.1 ± 150.9, mean ± std) four-second trials of executed
movements divided into 13 runs per subject.  The four classes of movements were
movements of either the left hand, the right hand, both feet, and rest (no
movement, but same type of visual cue as for the other classes).  The training
set consists of the approx.  880 trials of all runs except the last two runs,
the test set of the approx.  160 trials of the last 2 runs.  This dataset was
acquired in an EEG lab optimized for non-invasive detection of high- frequency
movement-related EEG components (Ball et al., 2008; Darvas et al., 2010).

Depending on the direction of a gray arrow that was shown on black back-
ground, the subjects had to repetitively clench their toes (downward arrow),
perform sequential finger-tapping of their left (leftward arrow) or right
(rightward arrow) hand, or relax (upward arrow).  The movements were selected
to require little proximal muscular activity while still being complex enough
to keep subjects in- volved.  Within the 4-s trials, the subjects performed the
repetitive movements at their own pace, which had to be maintained as long as
the arrow was showing.  Per run, 80 arrows were displayed for 4 s each, with 3
to 4 s of continuous random inter-trial interval.  The order of presentation
was pseudo-randomized, with all four arrows being shown every four trials.
Ideally 13 runs were performed to collect 260 trials of each movement and rest.
The stimuli were presented and the data recorded with BCI2000 (Schalk et al.,
2004).  The experiment was approved by the ethical committee of the University
of Freiburg.

## References

[1] Schirrmeister, Robin Tibor, et al. "Deep learning with convolutional
neural networks for EEG decoding and visualization." Human brain mapping 38.11
(2017): 5391-5420.
Motor Imagery dataset from Weibo et al 2014.
Full name: MI Weibo et al 2014.

Dataset from the article *Evaluation of EEG oscillatory patterns and
    cognitive process during simple and compound limb motor imagery* [1]_.

It contains data recorded on 10 subjects, with 60 electrodes.

This dataset was used to investigate the differences of the EEG patterns
between simple limb motor imagery and compound limb motor
imagery. Seven kinds of mental tasks have been designed, involving three
tasks of simple limb motor imagery (left hand, right hand, feet), three
tasks of compound limb motor imagery combining hand with hand/foot
(both hands, left hand combined with right foot, right hand combined with
left foot) and rest state.

At the beginning of each trial (8 seconds), a white circle appeared at the
center of the monitor. After 2 seconds, a red circle (preparation cue)
appeared for 1 second to remind the subjects of paying attention to the
character indication next. Then red circle disappeared and character
indication (‘Left Hand’, ‘Left Hand & Right Foot’, et al) was presented on
the screen for 4 seconds, during which the participants were asked to
perform kinesthetic motor imagery rather than a visual type of imagery
while avoiding any muscle movement. After 7 seconds, ‘Rest’ was presented
for 1 second before next trial (Fig. 1(a)). The experiments were divided
into 9 sections, involving 8 sections consisting of 60 trials each for six
kinds of MI tasks (10 trials for each MI task in one section) and one
section consisting of 80 trials for rest state. The sequence of six MI
tasks was randomized. Intersection break was about 5 to 10 minutes.

References
-----------
[1] Yi, Weibo, et al. "Evaluation of EEG oscillatory patterns and
       cognitive process during simple and compound limb motor imagery."
       PloS one 9.12 (2014). https://doi.org/10.1371/journal.pone.0114853
Motor Imagery dataset from Zhou et al 2016.

Dataset from the article *A Fully Automated Trial Selection Method for
Optimization of Motor Imagery Based Brain-Computer Interface* [1]_.
This dataset contains data recorded on 4 subjects performing 3 type of
motor imagery: left hand, right hand and feet.

Every subject went through three sessions, each of which contained two
consecutive runs with several minutes inter-run breaks, and each run
comprised 75 trials (25 trials per class). The intervals between two
sessions varied from several days to several months.

A trial started by a short beep indicating 1 s preparation time,
and followed by a red arrow pointing randomly to three directions (left,
right, or bottom) lasting for 5 s and then presented a black screen for
4 s. The subject was instructed to immediately perform the imagination
tasks of the left hand, right hand or foot movement respectively according
to the cue direction, and try to relax during the black screen.

References
----------

[1] Zhou B, Wu X, Lv Z, Zhang L, Guo X (2016) A Fully Automated
       Trial Selection Method for Optimization of Motor Imagery Based
       Brain-Computer Interface. PLoS ONE 11(9).
       https://doi.org/10.1371/journal.pone.0162657