after_hook_linear_dynamical_system¶
- class torcheeg.transforms.after_hook_linear_dynamical_system(data: List[ndarray | Tensor], V0: float = 0.01, A: float = 1, T: float = 0.0001, C: float = 1, sigma: float = 1)[source][source]¶
A common hook function used to normalize the signal of the whole trial/session/subject after dividing it into chunks and transforming the divided chunks.
It is used as follows:
from torcheeg.datasets import DEAPDataset from torcheeg.transforms import after_hook_linear_dynamical_system dataset = DEAPDataset(root_path='./data_preprocessed_python', offline_transform=transforms.Compose([ transforms.BandDifferentialEntropy(), transforms.ToGrid(DEAP_CHANNEL_LOCATION_DICT) ]), online_transform=transforms.ToTensor(), after_trial=after_hook_linear_dynamical_system, label_transform=transforms.Compose([ transforms.Select('valence'), transforms.Binary(5.0), ]))
If you want to pass in parameters, use partial to generate a new function:
from functools import partial from torcheeg.datasets import DEAPDataset from torcheeg.transforms import after_hook_linear_dynamical_system dataset = DEAPDataset(root_path='./data_preprocessed_python', offline_transform=transforms.Compose([ transforms.BandDifferentialEntropy(), transforms.ToGrid(DEAP_CHANNEL_LOCATION_DICT) ]), online_transform=transforms.ToTensor(), after_trial=partial(after_hook_linear_dynamical_system, V0=0.01, A=1, T=0.0001, C=1, sigma=1), label_transform=transforms.Compose([ transforms.Select('valence'), transforms.Binary(5.0), ]))
- Parameters:
data (list) – A list of
np.ndarray
ortorch.Tensor
, one of which corresponds to an EEG signal in trial.V0 (float) – The initial variance of the linear dynamical system (default:
0.01
)A (float) – The coefficient of the linear dynamical system (default:
1
)T (float) – The term added to the diagonal of the covariance matrix (default:
0.0001
)C (float) – The coefficient of the linear dynamical system (default:
1
)sigma (float) – The variance of the linear dynamical system (default:
1
)
- Returns:
The normalized results of a trial. It is a list of
np.ndarray
ortorch.Tensor
, one of which corresponds to an EEG signal in trial.- Return type:
list