Enhance AugmentedDataset (#541)

* enhance augmented dataset

* [pre-commit.ci] auto fixes from pre-commit.com hooks

* update whats new

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

docs/source/whats_new.rst

@@ -22,6 +22,7 @@ Enhancements
 - Option to interpolate channel in paradigms' `match_all` method (:gh:`480` by `Gregoire Cattan`_)
 - Adding leave k-Subjects out evaluations (:gh:`470` by `Bruno Aristimunha`_)
 - Update Braindecode dependency to 0.8 (:gh:`542` by `Pierre Guetschel`_)
+- Improve transform function of AugmentedDataset (:gh:`541` by `Quentin Barthelemy`_)
 
 Bugs
 ~~~~

moabb/pipelines/features.py

@@ -1,7 +1,7 @@
 import mne
 import numpy as np
 import scipy.signal as signal
-from numpy import concatenate, ndarray
+from numpy import ndarray
 from sklearn.base import BaseEstimator, TransformerMixin
 from sklearn.preprocessing import StandardScaler
 
@@ -99,20 +99,17 @@ def fit(self, X: ndarray, y: ndarray):
 
     def transform(self, X: ndarray):
         if self.order == 1:
-            X_fin: ndarray = X
+            X_new: ndarray = X
         else:
-            X_p = X[:, :, : -self.order * self.lag]
-            X_p = concatenate(
-                [X_p]
-                + [
+            X_new = np.concatenate(
+                [
                     X[:, :, p * self.lag : -(self.order - p) * self.lag]
-                    for p in range(1, self.order)
+                    for p in range(0, self.order)
                 ],
                 axis=1,
             )
-            X_fin = X_p
 
-        return X_fin
+        return X_new
 
 
 class StandardScaler_Epoch(BaseEstimator, TransformerMixin):