braindecode.preprocessing.exponential_moving_standardize

braindecode.preprocessing.exponential_moving_standardize(data, factor_new=0.001, init_block_size=None, eps=0.0001)

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: $x_t^{\prime }=(x_t-m_t)/max(\sqrt{->v_t},eps)$.

Parameters

data: np.ndarray (n_channels, n_times)

factor_new: float

init_block_size: int

Standardize data before to this index with regular standardization.

eps: float

Stabilizer for division by zero variance.

Returns

standardized: np.ndarray (n_channels, n_times)

Standardized data.

Examples using braindecode.preprocessing.exponential_moving_standardize

Data Augmentation on BCIC IV 2a Dataset

Trialwise Decoding on BCIC IV 2a Dataset

Cropped 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