new features for version 0.2.1

RELEASE.md

@@ -1,7 +1,16 @@
 # Version X.X.X (only via git install)
 
+# Version 0.2.1 (latest)
+
 **Functionality**
 
+- **augmentation module**:
+  - add Circular augmenter in compose module.
+  - add phase swap augmentation in functional module.
+- **models module**:
+    - models can be initialized with a custom seed.
+    - add EEGConformer.
+    - add xEEGNet.
 - **dataloading module**:
     - EEGDataset now supports EEG with multiple labels (1 per window partition).
 - **ssl module**:
@@ -14,7 +23,7 @@
 * reduced unittest overall time
 
 
-# Version 0.2.0 (latest)
+# Version 0.2.0
 
 **Functionality**
 

docs/selfeeg.augmentation.rst

@@ -58,6 +58,7 @@ Functions
     moving_avg
     permutation_signal
     permute_channels
+    phase_swap
     random_FT_phase
     random_slope_scale
     scaling

docs/selfeeg.models.rst

@@ -40,6 +40,7 @@ Classes
     :template: classtemplate.rst
 
     DeepConvNetEncoder
+    EEGConformerEncoder
     EEGInceptionEncoder
     EEGNetEncoder
     EEGSymEncoder
@@ -49,6 +50,7 @@ Classes
     StagerNetEncoder
     STNetEncoder
     TinySleepNetEncoder
+    xEEGNetEncoder
 
 
 models.zoo module
@@ -65,6 +67,7 @@ Classes
 
     ATCNet
     DeepConvNet
+    EEGConformer
     EEGInception
     EEGNet
     EEGSym
@@ -74,3 +77,4 @@ Classes
     StagerNet
     STNet
     TinySleepNet
+    xEEGNet

selfeeg/augmentation/__init__.py

@@ -25,6 +25,7 @@
     moving_avg,
     permutation_signal,
     permute_channels,
+    phase_swap,
     random_FT_phase,
     random_slope_scale,
     scaling,

selfeeg/augmentation/functional.py

@@ -35,6 +35,7 @@
     "moving_avg",
     "permutation_signal",
     "permute_channels",
+    "phase_swap",
     "random_FT_phase",
     "random_slope_scale",
     "scaling",
@@ -114,7 +115,7 @@ def shift_horizontal(
     batch_equal: bool = True,
 ) -> ArrayLike:
     """
-    shifts temporally the elements of the ArrayLike object.
+    Shifts temporally the elements of the ArrayLike object.
 
     Shift is applied along the last dimension.
     The empty elements at beginning or the ending part
@@ -438,6 +439,90 @@ def shift_frequency(
     return _shift_frequency(x, shift_freq, Fs, forward, random_shift, batch_equal, t, h)
 
 
+def phase_swap(x: ArrayLike) -> ArrayLike:
+    """
+    Apply the phase swap data augmentation to the ArrayLike object.
+
+    The phase swap data augmentation consists in merging the amplitude
+    and phase components of biosignals from different sources to help
+    the model learn their coupling.
+    Specifically, the amplitude and phase of two randomly selected EEG samples
+    are extracted using the Fourier transform.
+    New samples are then generated by applying the inverse Fourier transform,
+    combining the amplitude from one sample with the phase from the other.
+    See the following paper for more information [phaseswap]_.
+
+    Parameters
+    ----------
+    x : ArrayLike
+        A 3-dimensional torch tensor or numpy array.
+        The last two dimensions must refer to the EEG (Channels x Samples).
+
+    Returns
+    -------
+    x: ArrayLike
+        The augmented version of the input Tensor or Array.
+
+    Note
+    ----
+    `Phase swap` ignores the class of each sample.
+
+
+    References
+    ----------
+    .. [phaseswap] Lemkhenter, Abdelhak, and Favaro, Paolo.
+      "Boosting Generalization in Bio-signal Classification by
+      Learning the Phase-Amplitude Coupling". DAGM GCPR (2020).
+
+    """
+
+    Ndim = len(x.shape)
+    if Ndim != 3:
+        raise ValueError("x must be a 3-dimensional array or tensor")
+
+    N = x.shape[0]
+
+    if isinstance(x, torch.Tensor):
+        # Compute fft, module and phase
+        xfft = torch.fft.fft(x)
+        amplitude = xfft.abs()
+        phase = xfft.angle()
+        x_aug = torch.clone(xfft)
+
+        # Random shuffle indeces
+        idx_shuffle = torch.randperm(N).to(device=x.device)
+        idx_shuffle_1 = idx_shuffle[: (N // 2)]
+        idx_shuffle_2 = idx_shuffle[(N // 2) : (N // 2) * 2]
+
+        # Apply phase swap
+        x_aug[idx_shuffle_1] = amplitude[idx_shuffle_1] * torch.exp(1j * phase[idx_shuffle_2])
+        x_aug[idx_shuffle_2] = amplitude[idx_shuffle_2] * torch.exp(1j * phase[idx_shuffle_1])
+
+        # Reconstruct the signal
+        x_aug = (torch.fft.ifft(x_aug)).real.to(device=x.device)
+
+    else:
+
+        xfft = np.fft.fft(x)
+        amplitude = np.abs(xfft)
+        phase = np.angle(xfft)
+        x_aug = np.copy(xfft)
+
+        # Random shuffle indeces
+        idx_shuffle = np.random.permutation(N)
+        idx_shuffle_1 = idx_shuffle[: (N // 2)]
+        idx_shuffle_2 = idx_shuffle[(N // 2) : (N // 2) * 2]
+
+        # Apply phase swap
+        x_aug[idx_shuffle_1] = amplitude[idx_shuffle_1] * np.exp(1j * phase[idx_shuffle_2])
+        x_aug[idx_shuffle_2] = amplitude[idx_shuffle_2] * np.exp(1j * phase[idx_shuffle_1])
+
+        # Reconstruct the signal
+        x_aug = (np.fft.ifft(x_aug)).real
+
+    return x_aug
+
+
 def flip_vertical(x: ArrayLike) -> ArrayLike:
     """
     changes the sign of all the elements of the input.
@@ -456,9 +541,8 @@ def flip_vertical(x: ArrayLike) -> ArrayLike:
     -------
     >>> import torch
     >>> import selfeeg.augmentation as aug
-    >>> x = torch.zeros(16,32,1024) + torch.sin(torch.linspace(0, 8*np.pi,1024))
-    >>> xaug = aug.flip_vertical(x)
-    >>> print(torch.equal(xaug, x*(-1))) # should return True
+    >>> x = torch.randn(64,32,512)
+    >>> xaug = aug.phase_swap(x)
 
     """
     x_flip = x * (-1)

selfeeg/models/__init__.py

@@ -9,6 +9,7 @@
 from .encoders import (
     BasicBlock1,
     DeepConvNetEncoder,
+    EEGConformerEncoder,
     EEGInceptionEncoder,
     EEGNetEncoder,
     EEGSymEncoder,
@@ -17,11 +18,13 @@
     StagerNetEncoder,
     STNetEncoder,
     TinySleepNetEncoder,
+    xEEGNetEncoder,
 )
 
 from .zoo import (
     ATCNet,
     DeepConvNet,
+    EEGConformer,
     EEGInception,
     EEGNet,
     EEGSym,
@@ -31,4 +34,5 @@
     StagerNet,
     STNet,
     TinySleepNet,
+    xEEGNet,
 )