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: \(x'_t=(x_t - m_t) / max(\sqrt{->v_t}, eps)\).

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