--- a
+++ b/code_psd_fcnn/EEGConvNet.py
@@ -0,0 +1,28 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class EEGConvNet(nn.Module):
+	def __init__(self, reduced_sensors, sfreq=None, batch_size=32):
+		super(EEGConvNet, self).__init__()
+		
+		self.sfreq = sfreq
+		self.batch_size = batch_size
+		self.input_size = 8 if reduced_sensors else 62
+
+		self.fc_block1 = nn.Linear(48, 64)
+		self.batchnorm1 = nn.BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
+		self.fc_block2 = nn.Linear(64, 32)
+		self.fc_block3 = nn.Linear(32, 2)
+
+		# Xavier initializations
+		self.fc_block1.apply(lambda x: nn.init.xavier_normal_(x.weight, gain=1) if type(x) == nn.Linear else None)
+		self.fc_block2.apply(lambda x: nn.init.xavier_normal_(x.weight, gain=1) if type(x) == nn.Linear else None)
+		self.fc_block3.apply(lambda x: nn.init.xavier_normal_(x.weight, gain=1) if type(x) == nn.Linear else None)
+		
+	def forward(self, x):
+		x = x.reshape(x.size(0), -1)
+		x = F.dropout(F.leaky_relu(self.batchnorm1(self.fc_block1(x)), negative_slope=0.01), p=0.4, training=self.training)
+		x = F.dropout(F.leaky_relu(self.fc_block2(x), negative_slope=0.01), p=0.5, training=self.training)
+		out = self.fc_block3(x)
+		return out
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