Integrating Neural Signal and Embedded System for Controlling Small Motor

Abstract

Nowadays, controlling electronic devices without the use of hands is essential to provide a communication interface for disable persons to have control over their environment and to enable multi-tasking operation for normal person. Various methods of controlling electronic devices without the use of hands have been investigated by researchers, for examples sipand-puff, electro-oculogram (EOG signals), light emitter and others [Ding et al., 2005, Kumar et al., 2002; Breau et al., 2004]. In our previous study, EOG signal was found to be suitable for activating a television using a specific protocol [Harun et al., 2009], however, it could not be used when a person is not facing the system. Thus, a method that is more flexible has to be investigated. The use of neural signals to directly control a machine via a brain computer interface (BCI) has been studied since 1960s. Using an appropriate electrode placement and digital signal processing technique, useful information can be extracted from neural signals [Holzner et al, 2009; Jian et al., 2010; Gupta et al., 1996.] One of the events that can be detected from this signal is eye blink. It can be used as a mechanism to activate and control a machine which can help disable people to do their everyday routines. Most BCI systems employs a computer to process neural signals and perform control. Since portable system offers benefits such as flexibility, mobility and convenience to use, it is more preferred than a fixed system. An embedded system can be designed to provide portability feature. To include this feature, a microcontroller is required to control its operation and provide a communication link between human and machine. This chapter discusses how neural signal and embedded system can be combined together to activate a fan connected to a motor. It covers the introduction to neural signal, neural signal processing, embedded system and EEG based fan system hardware and software.