Research On The Control System Of Smart Home Based On Portable Brain Computer Interface

The EEG (Electroencephalogram, EEG) collected from the scalp is the overall reflection ofneuron in the electrical activity. It is relevant to the human awareness activities. Throughanalyzing the EEG signals, we could identify different awareness activities of human brain, thusform a communication control system, which does not depend on peripheral nerves outside thebrain and normal muscle output channels, namely Brain-Computer Interface (BCI).Visualevoked potential (VEP) is the electrical activity generated by occipital cortex with visual stimuli.It is a kind of EEG that is easy to be extracted and analyzed and it is often used as an input signalof the control system. Based on the Brain-Computer Interface systems, smart appliances systemis put forward against the traditional concept. After we have introduced the Brain-ComputerInterface technology to its original technology, the solution for independent living andrehabilitation of persons with disabilities and other problems will be found. Smart appliancessystem which is based on the Brain-Computer Interface consists of signal acquisition, signalfeature extraction, signal classification, command output, and external device control and so on.The acquisition precision, speed and stability of the Brain-Computer Interface system are coreproblem to be solved.This paper contains a set of intelligent appliance control system which is based on visualevoked potentials. It can extract Steady-state visual evoked potential (SSVEP) signal timely andconvert the signal to a corresponding control commands so as to control the smart appliances.The system is divided into two parts: the first is a real-time EEG signal processor based on DSPplatform, and the second is the smart home appliances based on Zigbee wireless network. DSP isfeatured by high-speed, low power consumption, and suitable for handling complex digitalsignals. Using the DSP TMS320F2812chip of TI2000Series, we try to achieve the digitalfiltering, feature extraction and classification towards SSVEP. Finally the characteristic signalsare converted into control commands so as to control smart appliances devices on the wirelessnetwork nodes. The programming of TMS320F2812chip implement in CCS3.3software whichmainly uses C language, so the system can acquire SSVEP effectively. The development of smarthome appliances devices with Zigbee wireless network control system mainly lies in the IAR810software. We aim to achieve the accurate transmission of the commands and the control ofappliances through the wireless network.We have carried out an online experiment towards the smart appliances control systembased on SSVEP, then compare with EEG processing device that build on a PC with LabVIEWplatform. We can easily find out that the processing speed of DSP increases about0.98%whencompared with the traditional PC. With the mobility and portability of the processing platformbased on DSP, we combine with the development of new smart home networking technology. So that we can enable EEG signal to control the home appliances under the guarantee of thereliability and portability of the system.