import numpy as np
from scipy.io import loadmat
from mne.channels import make_standard_montage
from mne.io import RawArray
from mne import create_info
from .base import _EEGDataset, DATA_PATH
from .download import data_dl
from ..utils import get_init_args
from ..tools.docs import fill_doc
URL = "ftp://parrot.genomics.cn/gigadb/pub/"
[docs]
@fill_doc
class OpenBMI_MI(_EEGDataset):
"""BMI/OpenBMI dataset for MI.
.. admonition:: Dataset summary
====== ====== ======= ===== ======== ======
Subj Chan Time Cls Freq Sess
====== ====== ======= ===== ======== ======
54 62 4.0 s 2 1000 Hz 2
====== ====== ======= ===== ======== ======
This data set consists of EEG data from 54 subjects of a study published in
[1]_. EEG signals were recorded with a sampling rate of 1,000 Hz and
collected with 62 Ag/AgCl electrodes. The EEG amplifier used in the
experiment was a BrainAmp (Brain Products; Munich, Germany). The channels
were nasion-referenced and grounded to electrode AFz. Additionally, an EMG
electrode recorded from each flexor digitorum profundus muscle with the
olecranon used as reference. The impedances of the EEG electrodes were
maintained below 10 k during the entire experiment.
MI paradigm The MI paradigm was designed following a well-established
system protocol. For all blocks, the first 3 s of each trial began with a
black fixation cross that appeared at the center of the monitor to prepare
subjects for the MI task. Afterwards, the subject performed the imagery
task of grasping with the appropriate hand for 4 s when the right or left
arrow appeared as a visual cue. After each task, the screen remained blank
for 6 s (± 1.5 s). The experiment consisted of training and test phases;
each phase had 100 trials with balanced right and left hand imagery tasks.
During the online test phase, the fixation cross appeared at the center of
the monitor and moved right or left, according to the real-time classifier
output of the EEG signal.
References
----------
.. [1] Lee, M. H., Kwon, O. Y., Kim, Y. J., Kim, H. K., Lee, Y. E.,
Williamson, J., … Lee, S. W. (2019). EEG dataset and OpenBMI toolbox
for three BCI paradigms: An investigation into BCI illiteracy.
GigaScience, 8(5), 1-16. https://doi.org/10.1093/gigascience/giz002
Parameters
----------
%(subjects)s
%(epochs_tmin_tmax)s
%(baseline)s
%(picks)s
%(resample)s
%(rename)s
"""
def __init__(
self,
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,
) -> None:
super().__init__(
repr=get_init_args(self, locals(), rename=rename, ret_dict=True),
subject_list=list(range(1, 55)),
interval=[0.0, 4.0],
event_id={"right_hand": 1, "left_hand": 2},
subjects=subjects,
tmin=tmin,
tmax=tmax,
baseline=baseline,
picks=picks,
resample=resample,
)
self._data_url = f"{URL}10.5524/100001_101000/100542/"
self._data_path = DATA_PATH / "openbmi_mi"
def _load_run(self, data, verbose):
sfreq = data["fs"].item()
eeg_ch_names = [np.squeeze(c).item() for c in np.ravel(data["chan"])]
eeg_ch_types = ["eeg"] * len(eeg_ch_names)
emg_ch_names = [np.squeeze(c).item() for c in np.ravel(data["EMG_index"])]
emg_ch_types = ["emg"] * len(emg_ch_names)
raw_ch_names = eeg_ch_names + emg_ch_names + ["STI 014"]
raw_ch_types = eeg_ch_types + emg_ch_types + ["stim"]
info = create_info(raw_ch_names, sfreq, raw_ch_types) # type: ignore
eeg = data["x"].transpose(1, 0) * 1e-6 # to load the signal in Volts
emg = data["EMG"].transpose(1, 0) * 1e-6 # to load the signal in Volts
event_times_in_samples = data["t"].squeeze()
event_id = data["y_dec"].squeeze()
stim = np.zeros(len(data["x"]))
for i_sample, id_class in zip(event_times_in_samples, event_id):
stim[i_sample] += id_class
raw_data = np.concatenate([eeg, emg, stim[None, :]])
raw = RawArray(data=raw_data, info=info, verbose=verbose)
raw.set_montage(make_standard_montage("standard_1005"))
return raw
def _load_sess(self, filename, verbose):
mat = loadmat(filename)
train = self._load_run(mat["EEG_MI_train"][0, 0], verbose)
test = self._load_run(mat["EEG_MI_test"][0, 0], verbose)
return {"train": train, "test": test}
def _get_single_subject_raw(self, subject: int, verbose="ERROR"):
sessions = {}
for r in [1, 2]:
filename = data_dl(
f"{self._data_url}session{r}/s{subject}/sess{r:02d}_subj{subject:02d}_EEG_MI.mat",
self._data_path,
force_update=False,
)
sessions[f"session_{r}"] = self._load_sess(filename, verbose)
return sessions
