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fixed_interval_windows.py
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fixed_interval_windows.py
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from moabb.datasets import utils
from moabb.datasets.preprocessing import RawToFixedIntervalEvents
from moabb.paradigms.base import BaseProcessing
class BaseFixedIntervalWindowsProcessing(BaseProcessing):
"""Base class for fixed interval windows processing.
Paradigm for creating epochs at fixed interval,
ignoring the stim channel and events of the dataset.
Parameters
----------
filters: list of list (default [[7, 45]])
bank of bandpass filter to apply.
baseline: None | tuple of length 2
The time interval to consider as “baseline” when applying baseline
correction. If None, do not apply baseline correction.
If a tuple (a, b), the interval is between a and b (in seconds),
including the endpoints.
Correction is applied by computing the mean of the baseline period
and subtracting it from the data (see mne.Epochs)
channels: list of str | None (default None)
list of channel to select. If None, use all EEG channels available in
the dataset.
resample: float | None (default None)
If not None, resample the eeg data with the sampling rate provided.
length: float (default 5.0)
Length of the epochs in seconds.
stride: float (default 10.0)
Stride between epochs in seconds.
start_offset: float (default 0.0)
Start from the beginning of the raw recordings in seconds.
stop_offset: float | None (default None)
Stop offset from beginning of raw recordings in seconds.
If None, set to be the end of the recording.
marker: int (default -1)
Marker to use for the events created.
Notes
-----
.. versionadded:: 1.0.0
"""
def __init__(
self,
filters=None,
baseline=None,
channels=None,
resample=None,
length: float = 5.0,
stride: float = 10.0,
start_offset=0.0,
stop_offset=None,
marker=-1,
):
if not filters:
raise ValueError("filters must be specified")
tmin = 0.0
tmax = length
super().__init__(
filters=filters,
channels=channels,
baseline=baseline,
resample=resample,
tmin=tmin,
tmax=tmax,
)
self.length = length
self.stride = stride
self.start_offset = start_offset
self.stop_offset = stop_offset
self.marker = marker
def _to_samples(self, key):
value = getattr(self, key)
if self.resample is None:
raise ValueError(f"{key}_samples: must be specified")
if value is None:
raise ValueError(f"{key}_samples: {key} must be specified")
return int(value * self.resample)
@property
def length_samples(self):
return self._to_samples("length")
@property
def stride_samples(self):
return self._to_samples("stride")
@property
def start_offset_samples(self):
return self._to_samples("start_offset")
@property
def stop_offset_samples(self):
return self._to_samples("stop_offset")
def used_events(self, dataset):
return {"Window": self.marker}
def is_valid(self, dataset):
return True
@property
def datasets(self):
return utils.dataset_search(paradigm=None)
def _get_events_pipeline(self, dataset):
return RawToFixedIntervalEvents(
length=self.length,
stride=self.stride,
start_offset=self.start_offset,
stop_offset=self.stop_offset,
marker=self.marker,
)
class FixedIntervalWindowsProcessing(BaseFixedIntervalWindowsProcessing):
"""Fixed interval windows processing.
Paradigm for creating epochs at fixed interval,
ignoring the stim channel and events of the dataset.
Parameters
----------
fmin: float (default 7)
cutoff frequency (Hz) for the high pass filter
fmax: float (default 45)
cutoff frequency (Hz) for the low pass filter
baseline: None | tuple of length 2
The time interval to consider as “baseline” when applying baseline
correction. If None, do not apply baseline correction.
If a tuple (a, b), the interval is between a and b (in seconds),
including the endpoints.
Correction is applied by computing the mean of the baseline period
and subtracting it from the data (see mne.Epochs)
channels: list of str | None (default None)
list of channel to select. If None, use all EEG channels available in
the dataset.
resample: float | None (default None)
If not None, resample the eeg data with the sampling rate provided.
length: float (default 5.0)
Length of the epochs in seconds.
stride: float (default 10.0)
Stride between epochs in seconds.
start_offset: float (default 0.0)
Start from the beginning of the raw recordings in seconds.
stop_offset: float | None (default None)
Stop offset from beginning of raw recordings in seconds.
If None, set to be the end of the recording.
marker: int (default -1)
Marker to use for the events created.
"""
def __init__(
self,
fmin=7,
fmax=45,
baseline=None,
channels=None,
resample=None,
length: float = 5.0,
stride: float = 10.0,
start_offset=0.0,
stop_offset=None,
marker=-1,
):
super().__init__(
filters=[(fmin, fmax)],
baseline=baseline,
channels=channels,
resample=resample,
length=length,
stride=stride,
start_offset=start_offset,
stop_offset=stop_offset,
marker=marker,
)
class FilterBankFixedIntervalWindowsProcessing(BaseFixedIntervalWindowsProcessing):
"""Filter bank fixed interval windows processing.
Paradigm for creating epochs at fixed interval
with multiple narrow bandpass filters,
ignoring the stim channel and events of the dataset.
Parameters
----------
filters: list of list (default ((8, 12), (12, 16), (16, 20), (20, 24), (24, 28), (28, 32)))
bank of bandpass filter to apply.
baseline: None | tuple of length 2
The time interval to consider as “baseline” when applying baseline
correction. If None, do not apply baseline correction.
If a tuple (a, b), the interval is between a and b (in seconds),
including the endpoints.
Correction is applied by computing the mean of the baseline period
and subtracting it from the data (see mne.Epochs)
channels: list of str | None (default None)
list of channel to select. If None, use all EEG channels available in
the dataset.
resample: float | None (default None)
If not None, resample the eeg data with the sampling rate provided.
length: float (default 5.0)
Length of the epochs in seconds.
stride: float (default 10.0)
Stride between epochs in seconds.
start_offset: float (default 0.0)
Start from the beginning of the raw recordings in seconds.
stop_offset: float | None (default None)
Stop offset from beginning of raw recordings in seconds.
If None, set to be the end of the recording.
marker: int (default -1)
Marker to use for the events created.
"""
def __init__(
self,
filters=((8, 12), (12, 16), (16, 20), (20, 24), (24, 28), (28, 32)),
baseline=None,
channels=None,
resample=None,
length: float = 5.0,
stride: float = 10.0,
start_offset=0.0,
stop_offset=None,
marker=-1,
):
super().__init__(
filters=filters,
baseline=baseline,
channels=channels,
resample=resample,
length=length,
stride=stride,
start_offset=start_offset,
stop_offset=stop_offset,
marker=marker,
)