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 exponential moving mean \(m_t\) at time t as a weighted average: \(m_t = \frac{\sum_{i=0}^t (1-\alpha)^i x_{t-i}}{\sum_{i=0}^t (1-\alpha)^i}\) where \(\alpha\) is factor_new.

Then, compute exponential moving variance \(v_t\) at time t as a weighted average of the squared demeaned signal: \(v_t = \frac{\sum_{i=0}^t (1-\alpha)^i (x_{t-i} - m_{t-i})^2}{\sum_{i=0}^t (1-\alpha)^i}\).

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