before_hook_normalize¶
- class torcheeg.transforms.before_hook_normalize(data: ndarray, eps: float = 1e-06, axis=0)[source][source]¶
A common hook function used to normalize the signal of the whole trial/session/subject before dividing it into chunks.
It is used as follows:
from functools import partial from torcheeg.datasets import SEEDFeatureDataset from torcheeg.transforms import before_hook_normalize dataset = SEEDFeatureDataset(root_path='./ExtractedFeatures', feature=['de_movingAve'], offline_transform=transforms.ToGrid (SEED_CHANNEL_LOCATION_DICT), online_transform=transforms.ToTensor(), before_trial=before_hook_normalize, label_transform=transforms.Compose([ transforms.Select('emotion'), transforms.Lambda(lambda x: x + 1) ]))
If you want to pass in parameters, use partial to generate a new function:
from functools import partial from torcheeg.datasets import SEEDFeatureDataset from torcheeg.transforms import before_hook_normalize dataset = SEEDFeatureDataset(root_path='./ExtractedFeatures', feature=['de_movingAve'], offline_transform=transforms.ToGrid (SEED_CHANNEL_LOCATION_DICT), online_transform=transforms.ToTensor(), before_trial=partial(before_hook_normalize, eps=1e-5), label_transform=transforms.Compose([ transforms.Select('emotion'), transforms.Lambda(lambda x: x + 1) ]))
- Parameters:
data (np.ndarray) – The input EEG signals or features of a trial.
axis (int) – The axis along which to normalize the data (default:
0
)eps (float) – The term added to the denominator to improve numerical stability (default:
1e-6
)
- Returns:
The normalized results of a trial.
- Return type:
np.ndarray