Classification of Eog Signal using Elman Recurrent Neural Network for Different Age Groups

Abstract

Disabled people in the world population were increasing constantly, So need of rehabilitative system also increasing every day. To overcome such wretched condition, we can use the biosignal techniques to device the rehabilitative devices. Rehabilitative devices may be called as Brain Computer Interface (BCI) or Human Computer Interface (HCI). We studied the performances of ten male subjects between the age group of 18 to 25 using mean features and Elman Recurrent Neural Network (ERNN). We conducted our study with two different age group from 18 to 21 and 22 to 25. The average classification accuracy of 91.00%, 93.57% were attained for the age group of 18 to 21 and 22 to 25. From the individual analysis we identified that performances from the age group 22 to 25 were appreciated then that of the age group from 18 to 21. In between the study we analyzed that subject s from the age group 22 to 25 performed all the following five tasks neatly and accurately without any deviation and disturbance compared with age group from 18 to 21. Finally from the obtained result we concluded that subject from the age group 22 to 25 was higher than that of age group from 18 to 21.