The control signal generated by classifiers was applied as control signals to navigate the assistive devices such as mouse, keyboard, and wheelchair activities for disabled people.Įarlier days people with disability face lot of difficulty in communication due to neuromuscular attack. The achieved results showed that the ERNN model has a greater potential to classify, identify, and recognize the EOG signal compared with distributed time delay network for most of the subjects. The accuracy of the classifier was analyzed by single-trial analysis and performances of the classifier were observed using bit transfer rate (BTR) for twenty subjects to check the feasibility of designing the HCI. Average classification accuracy with 90.82% and 90.56% was achieved using two network models.

Afterwards, extracted features were classified using Elman and distributed time delay neural network. The extracted signals were preprocessed by applying notch filter with a range of 50 Hz to remove the external interferences the features were extracted by applying convolution theorem. Voluntarily twenty subjects participated in this study. Analog Digital Instrument T26 with a five-electrode system was used in this method. The major plan of this study is to test out the feasibility of nine states of HCI by using modern techniques to overcome the problem faced by the paralyzed.

This communication is due to HCI, which facilitates lives of paralyzed patients who do not have any problems with their cognitive functioning. These signals permit communication between people and electronic-controllable devices. Human-computer interfaces (HCI) allow people to control electronic devices, such as computers, mouses, wheelchairs, and keyboards, by bypassing the biochannel without using motor nervous system signals.

Testout lab 8.2.4