PhysioNet_MI

class dpeeg.datasets.PhysioNet_MI(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, imagined: bool = True, executed: bool = False, rename: str | None = None)

Physionet Motor Imagery dataset.

Dataset summary

Subj	Chan	Time	Cls	Freq	Sess
109	64	3.0 s	4	160 Hz	1

Physionet MI dataset: https://physionet.org/pn4/eegmmidb/

This data set consists of over 1500 one- and two-minute EEG recordings, obtained from 109 volunteers [2].

Subjects performed different motor/imagery tasks while 64-channel EEG were recorded using the BCI2000 system (http://www.bci2000.org) [1]. Each subject performed 14 experimental runs: two one-minute baseline runs (one with eyes open, one with eyes closed), and three two-minute runs of each of the four following tasks:

1. A target appears on either the left or the right side of the screen. The subject opens and closes the corresponding fist until the target disappears. Then the subject relaxes.

2. A target appears on either the left or the right side of the screen. The subject imagines opening and closing the corresponding fist until the target disappears. Then the subject relaxes.

3. A target appears on either the top or the bottom of the screen. The subject opens and closes either both fists (if the target is on top) or both feet (if the target is on the bottom) until the target disappears. Then the subject relaxes.

4. A target appears on either the top or the bottom of the screen. The subject imagines opening and closing either both fists (if the target is on top) or both feet (if the target is on the bottom) until the target disappears. Then the subject relaxes.

References

[1]

Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N. and Wolpaw, J.R., 2004. BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Transactions on biomedical engineering, 51(6), pp.1034-1043.

[2]

Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E. and PhysioBank, P., PhysioNet: components of a new research resource for complex physiologic signals Circulation 2000 Volume 101 Issue 23 pp. E215-E220.

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.

• imagined (bool (default True)) – if True, return runs corresponding to motor imagination.

• executed (bool (default False)) – if True, return runs corresponding to motor execution.

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

get_data(progressbar: bool = True, verbose='ERROR') → dict[int, MultiSessEEGData]

Return the data correspoonding to a list of subjects.

The returned data is a dictionary with the following structure:

data = {‘subject_id’ : {‘session_id’ : EEGData}}

get_epochs(progressbar: bool = True, verbose='ERROR') → dict[int, dict[str, Epochs]]

Return the epochs correspoonding to a list of subjects.

The returned data is a dictionary with the following structure:

data = {‘subject_id’ : {‘session_id’ : Epochs}}

get_raw(progressbar: bool = True, verbose='ERROR') → dict[int, dict[str, dict[str, Raw]]]

Return the raw correspoonding to a list of subjects.

The returned data is a dictionary with the following structure:

data = {‘subject_id’: {‘session_id’: {‘run_id’: Raw}}}

subjects are on top, then we have sessions, then runs. A session is a recording done in a single day, without removing the EEG cap. A session is constitued of at least one run. A run is a single contigous recording. Some dataset break session in multiple runs.