MODMA_128_Resting#

class dpeeg.datasets.MODMA_128_Resting(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)[source]#

Multi-modal Open Dataset for Mental-disorder Analysis, Experimental Data.

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

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 raw in seconds. Default to use all data.

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

  • resample (float, optional) – Resample data.

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

encoding()[source]#

Return the correspondence between subjects and source files within the datasets.

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_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.