braindecode.augmentation.SensorsRotation#

class braindecode.augmentation.SensorsRotation(probability, sensors_positions_matrix, axis='z', max_degrees=15, spherical_splines=True, random_state=None)[source]#

Interpolates EEG signals over sensors rotated around the desired axis with an angle sampled uniformly between -max_degree and max_degree.

Suggested in [1]

Parameters:

probability (float) – Float setting the probability of applying the operation.
sensors_positions_matrix (numpy.ndarray) –
Matrix giving the positions of each sensor in a 3D cartesian coordinate system. Should have shape (3, n_channels), where n_channels is the number of channels. Standard 10-20 positions can be obtained from mne through:
```
>>> ten_twenty_montage = mne.channels.make_standard_montage(
...    'standard_1020'
... ).get_positions()['ch_pos']
```
axis ('x' | 'y' | 'z', optional) – Axis around which to rotate. Defaults to ‘z’.
max_degree (float, optional) – Maximum rotation. Rotation angles will be sampled between -max_degree and max_degree. Defaults to 15 degrees.
spherical_splines (bool, optional) – Whether to use spherical splines for the interpolation or not. When False, standard scipy.interpolate.Rbf (with quadratic kernel) will be used (as in the original paper). Defaults to True.
random_state (int | numpy.random.Generator, optional) – Seed to be used to instantiate numpy random number generator instance. Defaults to None.

References

[1]

Krell, M. M., & Kim, S. K. (2017). Rotational data augmentation for electroencephalographic data. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 471-474).

Methods

get_augmentation_params(*batch)[source]#

Return transform parameters.

Parameters:

X (tensor.Tensor) – The data.
y (tensor.Tensor) – The labels.

Returns:

params – Contains four elements:

sensors_positions_matrixnumpy.ndarray
Matrix giving the positions of each sensor in a 3D cartesian coordinate system. Should have shape (3, n_channels), where n_channels is the number of channels.
axis’x’ | ‘y’ | ‘z’
Axis around which to rotate.
anglesarray-like
Array of float of shape (batch_size,) containing the rotation angles (in degrees) for each element of the input batch, sampled uniformly between -max_degrees``and ``max_degrees.
spherical_splinesbool
Whether to use spherical splines for the interpolation or not. When False, standard scipy.interpolate.Rbf (with quadratic kernel) will be used (as in the original paper).

Return type:

dict

static operation(X, y, sensors_positions_matrix, axis, angles, spherical_splines)[source]#

Interpolates EEG signals over sensors rotated around the desired axis with the desired angle.

Suggested in [1]

Parameters:

X (torch.Tensor) – EEG input example or batch.
y (torch.Tensor) – EEG labels for the example or batch.
sensors_positions_matrix (numpy.ndarray) –
Matrix giving the positions of each sensor in a 3D cartesian coordinate system. Should have shape (3, n_channels), where n_channels is the number of channels. Standard 10-20 positions can be obtained from mne through:
```
>>> ten_twenty_montage = mne.channels.make_standard_montage(
...    'standard_1020'
... ).get_positions()['ch_pos']
```
axis ('x' | 'y' | 'z') – Axis around which to rotate.
angles (array-like) – Array of float of shape (batch_size,) containing the rotation angles (in degrees) for each element of the input batch.
spherical_splines (bool) – Whether to use spherical splines for the interpolation or not. When False, standard scipy.interpolate.Rbf (with quadratic kernel) will be used (as in the original paper).

References

[1]