Figure 1
From: Automatic diagnosis of neurological diseases using MEG signals with a deep neural network
Brief architecture of the MNet. Features extracted by the convolutional layers and the relative powers of the six frequency bands are concatenated before fully connected layer 13. Output size depends on classification patterns: two for binary classification and three for classification of two diseases and healthy subjects. Conv: convolutional layer; Fc: fully connected layer; HS: healthy subjects; EP: patients with epilepsy; SCI: patients with spinal cord injury.
