Genetic algorithm designed for optimization of neural network architectures for intracranial EEG recordings analysis.

The current practices of designing neural networks rely heavily on subjective judgment and heuristic steps, often dictated by the level of expertise possessed by architecture designers. To alleviate these challenges and streamline the design process, we propose an automatic method, a novel approach to enhance the optimization of neural network architectures for processing intracranial electroencephalogram (iEEG) data.We present a genetic algorithm, which optimizes neural network architecture and signal pre-processing parameters for iEEG classification.