braindecode.preprocessing.exponential_moving_standardize#

braindecode.preprocessing.exponential_moving_standardize(data: ndarray[Any, dtype[_ScalarType_co]], factor_new: float = 0.001, init_block_size: int | None = None, eps: float = 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: $x_t^{\prime }=(x_t-m_t)/max(\sqrt{->v_t},eps)$.

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 – Standardized data.

Return type:

np.ndarray (n_channels, n_times)

Examples using braindecode.preprocessing.exponential_moving_standardize#

Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset

Fingers flexion cropped decoding on BCIC IV 4 ECoG Dataset

Fingers flexion decoding on BCIC IV 4 ECoG Dataset

Fingers flexion decoding on BCIC IV 4 ECoG Dataset