MultiSessEEGData#

class dpeeg.MultiSessEEGData(data: list[EEGData])[source]#

class dpeeg.MultiSessEEGData(data: dict[str, EEGData])

Multi-session eegdata.

The multi-session EEGData is actually equivalent to the EEGData collection wrapped by the Python built-in dict. It does not have to be multi- session data, it can be any other EEGData collection. This name is only used here to indicate this type of data. The actual format is similar to:

multi_sess_eegdata = { ‘session_id’: EEGData }

Warning

Do not set the key value to the keywords “train” or “test”; otherwise, please use SplitEEGData.

Parameters:: data (list or dict of EEGData) – Initialize EEG data. If it is a list of EEGData, add a key to each data in the order of the list. If it is a dict of EEGData, use the dict to initialize.

Examples

Data type is list:

>>> eegdata = dpeeg.EEGData(edata=np.random.randn(16, 3, 10),
...                         label=np.random.randint(0, 3, 16))
>>> dpeeg.MultiSessEEGData([eegdata.copy(), eegdata.copy()])
{'session_1': [edata=(16, 3, 10), label=(16,)],
 'session_2': [edata=(16, 3, 10), label=(16,)]}

or the data type is custom dict:

>>> dpeeg.MultiSessEEGData({'xx': eegdata.copy(), 'yy': eegdata.copy()})
{'xx': [edata=(16, 3, 10), label=(16,)],
 'yy': [edata=(16, 3, 10), label=(16,)]}

Methods

`clear`
`copy`	Creates a deep copy of the current object.
`fromkeys`	Create a new dictionary with keys from iterable and values set to value.
`get`	Return the value for key if key is in the dictionary, else default.
`items`
`keys`
`pop`	If the key is not found, return the default if given; otherwise, raise a KeyError.
`popitem`	Remove and return a (key, value) pair as a 2-tuple.
`setdefault`	Insert key with a value of default if key is not in the dictionary.
`trials`	Returns the sum of the number of trials for all data or the number of trials for each data.
`update`	If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
`values`

trials(sum: bool = True) → list[int] | int[source]#: Returns the sum of the number of trials for all data or the number of trials for each data.