The Use of a Brain Computer Interface Remote Control to Navigate a Recreational Device

Abstract

People suffering from paralysis caused by serious neural disorder or spinal cord injury also need to be given a means of recreation other than general living aids. Although there have been a proliferation of brain computer interface (BCI) applications, developments for recreational activities are scarcely seen. The objective of this study is to develop a BCI-based remote control integrated with commercial devices such as the remote controlled Air Swimmer. The brain is visually stimulated using boxes flickering at preprogrammed frequencies to activate a brain response. After acquiring and processing these brain signals, the frequency of the resulting peak, which corresponds to the user’s selection, is determined by a decision model. Consequently, a command signal is sent from the computer to the wireless remote controller via a data acquisition (DAQ) module. A command selection training (CST) and simulated path test (SPT) were conducted by 12 subjects using the BCI control system and the experimental results showed a recognition accuracy rate of 89.51% and 92.31% for the CST and SPT, respectively. The fastest information transfer rate demonstrated a response of 105 bits/min and 41.79 bits/min for the CST and SPT, respectively. The BCI system was proven to be able to provide a fast and accurate response for a remote controller application.

Brain Computer Interface for Neurodegenerative Person Using Electroencephalogram

“…Twenty two features are gathered using a Chebyshev2 band pass filters by segregate the signal in the limits of four Hz to clean the noisy data. The twenty-two frequency ranges are (13)(14)(15)(16)…”

“…EEG is one of most important research area for developing the assistive device for disabled, so most of the researcher focuses to develop the BCI for inactive [3], [4]. Some of the important BCI developed for immobilized users were virtual Keyboard [5]- [8], Home Appliances [9], Mouse Controller [10], Music Player [11], [12], Remote Control System [13], Wheelchair [14]- [18]. In this study we discuss the online performance of the wheelchair in real time by executing four mental tasks by three trained users and one untrained user.…”

Performance Analysis of EEG Signals using Conventional and Hybrid Artificial Neural Network

“…Some of the relevant reserach carried out in this area were listed down. S.C.Chen et al (2013) proposed remote control system using fast fourier transform and steady state evoked potential trained with common selection training and simulated path test from ten male subjects and two female subjects and obtained the accuracy of 89.51% and 92.31% with information transfer rate of f105 bits per minutes and 41.79 bits per minute [22]. A.…”

User Experience Design for Brain-Computer Interfaces to Support Interaction in Points of Interest

“…Another example is Command Selection Training (CST), which determines the subject's initial response to an oscillating visual stimulus. The brain produces a corresponding response and this response can then be used to create a BCI command [12].…”