HighGamma

class dpeeg.datasets.HighGamma(subjects: list[int] | None = None, tmin: float = 0, tmax: float | None = None, baseline: tuple[int, int] | None = None, picks: list[str] | None = None, resample: float | None = None, rename: str | None = None)

High-gamma dataset described in Schirrmeister et al. 2017.

Dataset summary

Subj	Chan	Time	Cls	Freq	Sess
14	128	4.0 s	4	500 Hz	2

This data set consists of EEG data from 14 subjects of a study published in [1]. High-Gamma Dataset is a 128-electrode dataset (of which we later only use 44 sensors covering the motor cortex, 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 (as session 1), the test set of the approx. 160 trials of the last 2 runs (as session 2). This dataset was acquired in an EEG lab optimized for non-invasive detection of high- frequency movement-related EEG components.

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. 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.

Parameters:

• subjects (list of int, optional) – List of subject number. If None, all subjects will be loaded.

• tmin-tmax (float) – Start and end time of the epochs in seconds, relative to the time locked event. The closest or matching samples corresponding to the start and end time are included. Default is start and end time of epochs, respectively.

• baseline (tuple of int, optional) – The time interval to consider as “baseline” when applying baseline correction. If None, do not apply baseline correction.

• picks (list of str, optional) – Channels to include. If None, pick all channels.

• resample (float, optional) – Resample data.

• rename (str, optional) – Rename the dataset.