Performance Analysis of EEG Signals using Conventional and Hybrid Artificial Neural Network
S. Ram Kumar1, K. S. Dhanalakshmi2, K. Sathesh Kumar3, K. Shankar4, G. Emayavaramban5, P. Srirama Krishnan6, K. Ponmozhi7
1S. Ram Kumar, Department of Computing, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
2K. S. Dhanalakshmi, Department of Electronics and Electrical Technology, Krishnankoil (Tamil Nadu), India.
3K. Sathesh Kumar, Department of Computing, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
4K. Shankar, Department of Computer Applications, Alagappa Uiversity, Karaikudi (Tamil Nadu), India.
5G. Emayavaramban, Department of Electric and Electronic Engineering, Karpagam Academy of Higher Education, Coimbatore (Tamil Nadu), India.
6P. Srirama Krishnan, Department of Computing, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
7K. Ponmozhi, Department of Computing, Kalasalingam Academy of Research and Education College, Krishnankoil (Tamil Nadu), India.
Manuscript received on 01 December 2019 | Revised Manuscript received on 19 December 2019 | Manuscript Published on 31 December 2019 | PP: 569-576 | Volume-8 Issue-4S2 December 2019 | Retrieval Number: D11061284S219/2019©BEIESP | DOI: 10.35940/ijrte.D1106.1284S219
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Brain Computer Interface (BCI) using Electrooencephalogram (EEG) is one of the versatile tools to measure the brain thoughts and convert them to operate the external devices in the deficiency of biological controls. These techniques are used to develop the rehabilitative devices for the individual person who affected with locked in syndrome (LIS). The main reason for LIS is due to death of motor neurons. To overcome the real world problem we conduct our experiment with eight normal patients for four tasks using three electrode system and ADI T26 bio amplifier with Lab chart. Four task signals are applied to statistical method to retrieve the twenty two features and trained with the feed forward neural network (FFNN) and feed forward neural network with wolf Grey optimization algorithm (FFNNWGOA) to see network model which was more perfectly supported to identify the tasks. The study showed that statistical features through feed forward neural network with grey wolf optimized algorithm classifier produced maximum performances of 94.06% compared to other feed forward neural network classifier model and also we identified that optimized features demonstrated maximum performances in minimum time duration during training in all the following twenty trials.
Keywords: Brain Computer Interface, Feed Forward Neural Network, Locked in State, Electrooencephalogram, Feed Forward Neural Network with Wolf Grey Optimization algorithm.
Scope of the Article: Artificial Intelligence and Machine Learning