Home // BRAININFO 2017, The Second International Conference on Neuroscience and Cognitive Brain Information // View article
Authors:
Lei Zhang
Keywords: Brain stimulation; Chaotic systems; Artificial Neural networks; Dynamic control; Henon map.
Abstract:
This paper presents an Artificial Neural Network (ANN) based chaotic system design to simulate the chaotic patterns of brain activities, as well as the dynamic control of the designed system in order to generate accurate stimulation signals for non-invasive brain stimulation. Deep brain stimulation has been practically used to treat neurological diseases, such as Parkinson's Disease and Epilepsy. However, the underpinning theory of this treatment is still unclear. Moreover, the treatment requires high risk and high cost brain surgery. Previous research shows that brain activities captured by Electroencephalogram (EEG) demonstrate chaotic patterns. Chaotic systems, such as Henon map can be represented by a set of mathematical equations, and therefore are predictable and controllable. ANN resembles biological neural network in the brain. The designed ANN model is trained with Henon map chaotic outputs and the optimized architecture is selected based on the training results. By combining the ANN design and dynamic control theory, this research provides a simulation model for the implementation of dynamic control of brain stimulation in order to generate accurate and effective non-invasive brain stimulation signals, and reduce the cost and risk of the treatment.
Pages: 14 to 21
Copyright: Copyright (c) IARIA, 2017
Publication date: July 23, 2017
Published in: conference
ISSN: 2519-8653
ISBN: 978-1-61208-579-1
Location: Nice, France
Dates: from July 23, 2017 to July 27, 2017