braindecode.preprocessing.exponential_moving_standardize#
- braindecode.preprocessing.exponential_moving_standardize(data, factor_new=0.001, init_block_size=None, eps=0.0001)[source]#
Perform exponential moving standardization.
Compute the exponental moving mean \(m_t\) at time t as \(m_t=\mathrm{factornew} \cdot mean(x_t) + (1 - \mathrm{factornew}) \cdot m_{t-1}\).
Then, compute exponential moving variance \(v_t\) at time t as \(v_t=\mathrm{factornew} \cdot (m_t - x_t)^2 + (1 - \mathrm{factornew}) \cdot v_{t-1}\).
Finally, standardize the data point \(x_t\) at time t as: \(x'_t=(x_t - m_t) / max(\sqrt{->v_t}, eps)\).
- Parameters
- Returns
standardized – Standardized data.
- Return type
np.ndarray (n_channels, n_times)
Examples using braindecode.preprocessing.exponential_moving_standardize
#
Hyperparameter tuning with scikit-learn
Data Augmentation on BCIC IV 2a Dataset
Searching the best data augmentation on BCIC IV 2a Dataset
Trialwise Decoding on BCIC IV 2a Dataset
Fingers flexion decoding on BCIC IV 4 ECoG Dataset
Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset
Cropped Decoding on BCIC IV 2a Dataset
How to train, test and tune your model