  Objective
  The electrical characteristics of the EEG signals can be used for seizure
detection. Statistical independence between different brain regions is measured
by functional brain connectivity (FBC). Specific directional effects can't
consider by FBC and thus effective brain connectivity (EBC) is used to measure
causal intervention between one neuronal region and the rest of the neuronal
regions. Our main purpose is to provide a reliable automatic seizure detection
approach.
  Methods
  In this study, three new methods are provided. Deep modular neural network
(DMNN) is developed based on a combination of various EBC classification
results in the different frequencies. Another method is named "modular
effective neural networks (MENN)". This method combines the classification
results of the three different EBC in the specific frequency. "Modular
frequency neural networks (MFNN)" is another method that combines the
classification results of the specific EBC in the seven different frequencies.
  Results
  The mean accuracy of the MFNN are 97.14%, 98.53%, and 97.91% using directed
transfer function, directed coherence, and generalized partial directed
coherence, respectively. Using the MENN, the highest mean accuracy is 98.34%.
Finally, DMNN has the highest mean accuracy which is equal to 99.43. To our
best knowledge, the proposed method is a new method that provides the high
accuracy in comparison to other studies which used MIT-CHB database.
  Conclusion and significance
  The knowledge of structure-function relationships between different areas of
the brain is necessary for characterizing the underlying dynamics. Hence,
features based on EBC can provide a reliable automatic seizure detection
approach.
