A brain machine interface (BMI) design for controlling the navigation of a power wheelchair is proposed. Real-time experiments with four able bodied subjects are carried out using the BMI-controlled wheelchair. The BMI is based on only two electrodes and operated by motor imagery of four states. A recurrent neural classifier is proposed for the classification of the four mental states. The real-time experiment results of four subjects are reported and problems emerging from asynchronous control are discussed.
Hypoacusis is the most prevalent sensory disability in the world and consequently, it can lead to impede speech in human beings. One best approach to tackle this issue is to conduct early and effective hearing screening test using Electroencephalogram (EEG). EEG based hearing threshold level determination is most suitable for persons who lack verbal communication and behavioral response to sound stimulation. Auditory evoked potential (AEP) is a type of EEG signal emanated from the brain scalp by an acoustical stimulus. The goal of this review is to assess the current state of knowledge in estimating the hearing threshold levels based on AEP response. AEP response reflects the auditory ability level of an individual. An intelligent hearing perception level system enables to examine and determine the functional integrity of the auditory system. Systematic evaluation of EEG based hearing perception level system predicting the hearing loss in newborns, infants and multiple handicaps will be a priority of interest for future research.