Electroencephalogram Based Communication System for Locked In State Person Using Mentally Spelled Tasks with Optimized Network Model

Publication date: Available online 19 November 2019Source: Artificial Intelligence in MedicineAuthor(s): Xu Xiaoxiao, Luo Bin, S. Ramkumar, S Saravanan, M. Sundar Prakash Balaji, S. Dhanasekaran, J. ThimmiarajaAbstractDue 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 (IT...
Source: Artificial Intelligence in Medicine - Category: Bioinformatics Source Type: research