Human-Computer Interaction System for Communications and Control

The considered Human-Computer Interaction (HCI) system is based on brain-computer interface (BCI) which use measured electroencephalography (EEG) activity or other electrophysiological measures of brain functions as new non-muscular channels for control and communication with smart devices for disabled persons with neuromuscular disorders, as brain stem stroke, amyotrophic lateral sclerosis and spinal cord injury. The BCI gives possibility to control computer using brain directly, which is a very promising features for persons who cannot access traditional computer system due to their physical disabilities. The estimation of different mental tasks execution on the base of recorded electrophysiological signals is a step of building up such interface. In this research the recorded with experimental setup electrophysiological signals for execution of five different mental tasks after noise filtering are estimated on the base of clustering and classification with Bayesian Network classifier and pair-wise classifier. The validation of received results is shown.