Electroencephalogram based communication system for locked in state person using mentally spelled tasks with optimized network model.

Journal: Artificial intelligence in medicine
Published Date: Jan 1, 2020

Abstract

Due to growth in population, Individual persons with disabilities are increasing daily. To overcome the disability especially in Locked in State (LIS) due to Spinal Cord Injury (SCI), we planned to design four states moving robot from four imagery tasks signals acquired from three electrode systems by placing the electrodes in three positions namely T1, T3 and FP1. At the time of the study we extract the features from Continuous Wavelet Transform (CWT) and trained with Optimized Neural Network model to analyze the features. The proposed network model showed the highest performances with an accuracy of 93.86 % then that of conventional network model. To confirm the performances we conduct offline test. The offline test also proved that new network model recognizing accuracy was higher than the conventional network model with recognizing accuracy of 97.50 %. To verify our result we conducted Information Transfer Rate (ITR), from this analysis we concluded that optimized network model outperforms the other network models like conventional ordinary Feed Forward Neural Network, Time Delay Neural Network and Elman Neural Networks with an accuracy of 21.67 bits per sec. By analyzing classification performances, recognizing accuracy and Information Transformation Rate (ITR), we concluded that CWT features with optimized neural network model performances were comparably greater than that of normal or conventional neural network model and also the study proved that performances of male subjects was appreciated compared to female subjects.

Authors

  • Xu Xiaoxiao
    School of Entrepreneurship, Wuhan University of Technology, Wuhan Hubei Province, 430070, China. Electronic address: mvnmfjhgku4@163.com.
  • Luo Bin
    Medicine - remote mapping associated laboratory, Wuhan University, China; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, China.
  • S Ramkumar
    School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, Virudhunagar (Dt), India.
  • S Saravanan
    Department of Electrical and Electronics Engineering, Muthayammal Engineering College Rasipuram, Rasipuram, India.
  • M Sundar Prakash Balaji
    Department of ECE, RVS Technical Campus, Coimbatore, India.
  • S Dhanasekaran
    School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, Virudhunagar (Dt), India.
  • J Thimmiaraja
    School of Computing, Kalasalingam Academy of Research and Education, Krishnankoil, Virudhunagar (Dt), India.

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