import numpy as np
import pandas as pd
from zipfile import ZipFile
from scipy.io import loadmat
from mne.channels import make_standard_montage
from mne import create_info
from mne.io import RawArray
from .base import RawDataset, DATA_PATH
from .download import data_dl
from ..utils import get_init_args
from ..tools.docs import fill_doc
URL = "https://reshare.ukdataservice.ac.uk/"
[docs]
@fill_doc
class MODMA_128_Resting(RawDataset):
"""Multi-modal Open Dataset for Mental-disorder Analysis, Experimental Data.
.. admonition:: Dataset summary
====== ====== ======= ===== ======== ======
Subj Chan Time Cls Freq Sess
====== ====== ======= ===== ======== ======
53 128 4.5 s 2 250 Hz 1
====== ====== ======= ===== ======== ======
Multi-model open dataset for mental-disorder analysis [1]_. The dataset
includes data mainly from clinically depressed patients and matching normal
controls. 53 participants include a total of 24 outpatients (13 males and
11 females; 16-56-year-old) diagnosed with depression, as well as 29 healthy
controls (20 males and 9 females; 18-55-year-old) were recruited. No
experimental material. The participants should keep quiet and close their
eyes as much as possible. Continuous EEG signals were recorded using a
128-channel HydroCel Geodesic Sensor Net (Electrical Geodesics Inc., Oregon
Eugene, USA) and Net Station acquisition software (version 4.5.4). The
sampling frequency was 250 Hz. All raw electrode signals were referenced
to the Cz. 5 minutes of eyes-closed resting-state EEG was recorded.
Participants were required to keep awake and still without any bodily
movements, including heads or legs, and any unnecessary eye movements,
saccades, and blinks.
References
----------
.. [1] A. Seal, R. Bajpai, J. Agnihotri, A. Yazidi, E. Herrera-Viedma, and
O. Krejcar, DeprNet: A deep convolution neural network framework for
detecting depression using EEG, IEEE Trans. Instrum. Meas., vol. 70,
pp. 1-13, 2021, doi: 10.1109/TIM.2021.3053999.
Parameters
----------
%(subjects)s
%(raw_tmin_tmax)s
%(picks)s
%(resample)s
%(rename)s
"""
def __init__(
self,
subjects: list[int] | None = None,
tmin: float = 0,
tmax: float | None = None,
picks: list[str] | None = None,
resample: float | None = None,
rename: str | None = None,
) -> None:
if picks is None:
picks = [f"E{i}" for i in range(1, 129)]
super().__init__(
repr=get_init_args(self, locals(), rename=rename, ret_dict=True),
subject_list=list(range(1, 54)),
event_id={"major_depressive_disorder": 1, "healthy_controls": 2},
subjects=subjects,
tmin=tmin,
tmax=tmax,
picks=picks,
resample=resample,
)
self._data_url = (
f"{URL}854301/4/854301_EEG_128Channels_Resting_Lanzhou_2015.zip"
)
self._data_path = DATA_PATH / "modma"
self._montage = make_standard_montage("GSN-HydroCel-129")
self._info = create_info(
ch_names=self._montage.ch_names, sfreq=250.0, ch_types="eeg"
)
def _parse_zip(self):
path_zip = data_dl(self._data_url, self._data_path)
# Extract the zip file if it hasn't been extracted yet
path_folder = self._data_path / "EEG_128channels_resting_lanzhou_2015"
if not path_folder.exists():
print("The first read requires decompressing all data.")
with ZipFile(path_zip, "r") as zipf:
zipf.extractall(self._data_path)
return path_folder
[docs]
def encoding(self):
"""Return the correspondence between subjects and source files within
the datasets.
"""
path_folder = self._parse_zip()
df = pd.read_excel(
path_folder
/ "subjects_information_EEG_128channels_resting_lanzhou_2015.xlsx",
usecols="A:K",
)
df.index += 1
return df
def _get_single_subject_raw(self, subject: int, verbose="ERROR"):
path_folder = self._parse_zip()
path_list = sorted(path_folder.glob("*.mat"))
path_subject = path_list[subject - 1]
data = loadmat(path_subject, struct_as_record=False, squeeze_me=True)
eeg_data = data[list(data.keys())[3]]
eeg_data *= 1e-9
raw = RawArray(data=eeg_data, info=self._info, verbose=verbose)
raw.set_montage(self._montage)
return {"session_1": {"run_1": raw}}
def _set_label(self, subject: int):
encoding = self.encoding()
subject_type = encoding.loc[subject, "type"]
if subject_type == "MDD":
return np.array([1])
else:
return np.array([2])
