# Authors: Bruno Aristimunha <b.aristimunha@gmail.com>
# Cedric Rommel <cedric.rommel@inria.fr>
#
# License: BSD (3-clause)
from numpy import prod
from torch import nn
from .modules import Expression, Ensure4d
from .eegnet import _glorot_weight_zero_bias
from .eegitnet import _InceptionBlock, _DepthwiseConv2d
def _transpose_to_b_1_c_0(x):
return x.permute(0, 3, 1, 2)
[docs]class EEGInception(nn.Sequential):
"""EEG Inception.
EEG Inception for ERP-based classification described in [Santamaria2020]_.
The code for the paper and this model is also available at [Santamaria2020]_
and an adaptation for PyTorch [2]_.
The model is strongly based on the original InceptionNet for an image. The main goal is
to extract features in parallel with different scales. The authors extracted three scales
proportional to the window sample size. The network had three parts:
1-larger inception block largest, 2-smaller inception block followed by 3-bottleneck
for classification.
One advantage of the EEG-Inception block is that it allows a network
to learn simultaneous components of low and high frequency associated with the signal.
The winners of BEETL Competition/NeurIps 2021 used parts of the model [beetl]_.
The model is fully described in [Santamaria2020]_.
Notes
-----
This implementation is not guaranteed to be correct, has not been checked
by original authors, only reimplemented from the paper based on [2]_.
Parameters
----------
in_channels : int
Number of EEG channels.
n_classes : int
Number of classes.
input_size_ms : int
Size of the input, in milliseconds. Set to 1000 in [Santamaria2020]_.
sfreq : float
EEG sampling frequency.
drop_prob : float
Dropout rate inside all the network.
scales_time: list(int)
Windows for inception block, must be a list with proportional values of
the input_size_ms.
According to the authors: temporal scale (ms) of the convolutions
on each Inception module.
This parameter determines the kernel sizes of the filters.
n_filters : int
Initial number of convolutional filters. Set to 8 in [Santamaria2020]_.
activation: nn.Module
Activation function, default: ELU activation.
batch_norm_alpha: float
Momentum for BatchNorm2d.
depth_multiplier: int
Depth multiplier for the depthwise convolution.
pooling_sizes: list(int)
Pooling sizes for the inception block.
References
----------
.. [Santamaria2020] Santamaria-Vazquez, E., Martinez-Cagigal, V.,
Vaquerizo-Villar, F., & Hornero, R. (2020).
EEG-inception: A novel deep convolutional neural network for assistive
ERP-based brain-computer interfaces.
IEEE Transactions on Neural Systems and Rehabilitation Engineering , v. 28.
Online: http://dx.doi.org/10.1109/TNSRE.2020.3048106
.. [2] Grifcc. Implementation of the EEGInception in torch (2022).
Online: https://github.com/Grifcc/EEG/tree/90e412a407c5242dfc953d5ffb490bdb32faf022
.. [beetl]_ Wei, X., Faisal, A.A., Grosse-Wentrup, M., Gramfort, A., Chevallier, S.,
Jayaram, V., Jeunet, C., Bakas, S., Ludwig, S., Barmpas, K., Bahri, M., Panagakis,
Y., Laskaris, N., Adamos, D.A., Zafeiriou, S., Duong, W.C., Gordon, S.M.,
Lawhern, V.J., Śliwowski, M., Rouanne, V. & Tempczyk, P.. (2022).
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence &
Heterogenous EEG Data Sets. <i>Proceedings of the NeurIPS 2021 Competitions and
Demonstrations Track</i>, in <i>Proceedings of Machine Learning Research</i>
176:205-219 Available from https://proceedings.mlr.press/v176/wei22a.html.
"""
def __init__(
self,
in_channels,
n_classes,
input_window_samples=1000,
sfreq=128,
drop_prob=0.5,
scales_samples_s=(0.5, 0.25, 0.125),
n_filters=8,
activation=nn.ELU(),
batch_norm_alpha=0.01,
depth_multiplier=2,
pooling_sizes=(4, 2, 2, 2),
):
super().__init__()
self.in_channels = in_channels
self.n_classes = n_classes
self.input_window_samples = input_window_samples
self.drop_prob = drop_prob
self.sfreq = sfreq
self.n_filters = n_filters
self.scales_samples_s = scales_samples_s
self.scales_samples = tuple(
int(size_s * self.sfreq) for size_s in self.scales_samples_s)
self.activation = activation
self.alpha_momentum = batch_norm_alpha
self.depth_multiplier = depth_multiplier
self.pooling_sizes = pooling_sizes
self.add_module("ensuredims", Ensure4d())
self.add_module("dimshuffle", Expression(_transpose_to_b_1_c_0))
# ======== Inception branches ========================
block11 = self._get_inception_branch_1(
in_channels=in_channels,
out_channels=self.n_filters,
kernel_length=self.scales_samples[0],
alpha_momentum=self.alpha_momentum,
activation=self.activation,
drop_prob=self.drop_prob,
depth_multiplier=self.depth_multiplier,
)
block12 = self._get_inception_branch_1(
in_channels=in_channels,
out_channels=self.n_filters,
kernel_length=self.scales_samples[1],
alpha_momentum=self.alpha_momentum,
activation=self.activation,
drop_prob=self.drop_prob,
depth_multiplier=self.depth_multiplier,
)
block13 = self._get_inception_branch_1(
in_channels=in_channels,
out_channels=self.n_filters,
kernel_length=self.scales_samples[2],
alpha_momentum=self.alpha_momentum,
activation=self.activation,
drop_prob=self.drop_prob,
depth_multiplier=self.depth_multiplier,
)
self.add_module("inception_block_1", _InceptionBlock((block11, block12, block13)))
self.add_module("avg_pool_1", nn.AvgPool2d((1, self.pooling_sizes[0])))
# ======== Inception branches ========================
n_concat_filters = len(self.scales_samples) * self.n_filters
n_concat_dw_filters = n_concat_filters * self.depth_multiplier
block21 = self._get_inception_branch_2(
in_channels=n_concat_dw_filters,
out_channels=self.n_filters,
kernel_length=self.scales_samples[0] // 4,
alpha_momentum=self.alpha_momentum,
activation=self.activation,
drop_prob=self.drop_prob
)
block22 = self._get_inception_branch_2(
in_channels=n_concat_dw_filters,
out_channels=self.n_filters,
kernel_length=self.scales_samples[1] // 4,
alpha_momentum=self.alpha_momentum,
activation=self.activation,
drop_prob=self.drop_prob
)
block23 = self._get_inception_branch_2(
in_channels=n_concat_dw_filters,
out_channels=self.n_filters,
kernel_length=self.scales_samples[2] // 4,
alpha_momentum=self.alpha_momentum,
activation=self.activation,
drop_prob=self.drop_prob
)
self.add_module(
"inception_block_2", _InceptionBlock((block21, block22, block23)))
self.add_module("avg_pool_2", nn.AvgPool2d((1, self.pooling_sizes[1])))
self.add_module("final_block", nn.Sequential(
nn.Conv2d(
n_concat_filters,
n_concat_filters // 2,
(1, 8),
padding="same",
bias=False
),
nn.BatchNorm2d(n_concat_filters // 2,
momentum=self.alpha_momentum),
activation,
nn.Dropout(self.drop_prob),
nn.AvgPool2d((1, self.pooling_sizes[2])),
nn.Conv2d(
n_concat_filters // 2,
n_concat_filters // 4,
(1, 4),
padding="same",
bias=False
),
nn.BatchNorm2d(n_concat_filters // 4,
momentum=self.alpha_momentum),
activation,
nn.Dropout(self.drop_prob),
nn.AvgPool2d((1, self.pooling_sizes[3])),
))
spatial_dim_last_layer = (
input_window_samples // prod(self.pooling_sizes))
n_channels_last_layer = self.n_filters * len(self.scales_samples) // 4
self.add_module("classification", nn.Sequential(
nn.Flatten(),
nn.Linear(
spatial_dim_last_layer * n_channels_last_layer,
self.n_classes
),
nn.Softmax(1)
))
_glorot_weight_zero_bias(self)
@staticmethod
def _get_inception_branch_1(in_channels, out_channels, kernel_length,
alpha_momentum, drop_prob, activation,
depth_multiplier):
return nn.Sequential(
nn.Conv2d(
1,
out_channels,
kernel_size=(1, kernel_length),
padding="same",
bias=True
),
nn.BatchNorm2d(out_channels, momentum=alpha_momentum),
activation,
nn.Dropout(drop_prob),
_DepthwiseConv2d(
out_channels,
kernel_size=(in_channels, 1),
depth_multiplier=depth_multiplier,
bias=False,
padding="valid",
),
nn.BatchNorm2d(
depth_multiplier * out_channels,
momentum=alpha_momentum
),
activation,
nn.Dropout(drop_prob),
)
@staticmethod
def _get_inception_branch_2(in_channels, out_channels, kernel_length,
alpha_momentum, drop_prob, activation):
return nn.Sequential(
nn.Conv2d(
in_channels,
out_channels,
kernel_size=(1, kernel_length),
padding="same",
bias=False
),
nn.BatchNorm2d(out_channels, momentum=alpha_momentum),
activation,
nn.Dropout(drop_prob),
)