基于便携式脑—机接口的智能家电控制系统研究

发布时间：2018-06-12 03:09

本文选题：脑-机接口 + 稳态视觉诱发电位　；参考：《天津职业技术师范大学》2014年硕士论文

【摘要】：电极在头皮表面收集得到的脑电信号（Electroencephalogram，EEG），可以被理解为是神经元电生理活动的总体响应，人的认知、意识等活动形态和脑电信号具有很大的关联，存在差别的意识活动能够通过对脑电信号处理分析出来，由此可以形成一种独立于大脑外周神经和肌肉正常输出通道的通讯控制系统，即脑-机接口（Brain-Computer Interface，BCI）。视觉诱发电位（VEP）是枕叶皮层对视觉刺激产生的反应，作为一种分析和提取较为方便的脑电信号，常常作为控制系统的输入信号。基于脑-机接口的智能家电系统，是针对传统的智能家居概念提出来的，在原有的技术基础上将脑-机接口技术引入其中，可以解决残疾人的独立生活和康复治疗等问题。基于便携式脑-机接口的智能家电控制系统主要由脑电采集模块、数据分析模块、指令转化模块、指令传输网络和外部设备控制等部分组成，其中对于脑电数据的分析精度和速度是研究的重点。本文利用视觉诱发电位设计了一套仅使用脑电控制的智能家电系统，不仅可以对稳态视觉诱发电位(Steady-state visual evoked potentials, SSVEP)信号实时的采集分析处理，还能将其转换为对应的控制命令，达到无需肢体语言控制智能家电的目的。系统主要分为两大部分：基于DSP平台的脑电信号实时处理器和基于Zigbee无线网络搭建的智能家电装置。采用TI2000系列DSPTMS320F2812芯片，借助DSP高速、低功耗的特点，实现对SSVEP的数字滤波、特征提取以及分类，最后将特征信号转化为控制命令从而控制无线网络节点上的智能家电装置。在CCS3.3软件中利用C语言对TMS320F2812芯片进行算法编程，保证系统能够对SSVEP进行有效采集处理。Zigbee无线网络控制的智能家电装置系统的开发主要在IAR810软件上，实现对控制命令的准确发送和家电装置的控制。通过对基于SSVEP的智能家电控制系统进行了在线实验验证，并与搭建在上位机LabVIEW平台上的脑电处理装置相对比，在处理速度上DSP脑电处理平台处理单个任务指令的时间比传统的上位机处理平均提高了约0.98%。基于DSP的处理平台具有可移动性和便携性，结合新的物联网智能家居技术的开发，能够更好地实现了脑电控制家电装置，保证了系统的可靠性和便携性。
[Abstract]:Electroencephalogram-EEGG, which is collected by electrodes on the scalp surface, can be understood as the overall response of neurons to electrophysiological activities, and the patterns of activities such as human cognition and consciousness have a great relationship with EEG signals. Different conscious activities can be analyzed by processing EEG signals, thus forming a communication control system independent of the normal output channels of the peripheral nerves and muscles of the brain, that is, Brain-Computer Interface (Brain-Computer Interface), Brain-Computer Interface (Brain-Computer Interface), Brain-Computer Interface (Brain-Computer Interface), Brain-Computer Interface (Brain-Computer Interface). Visual evoked potential (VEP) is a response of occipital cortex to visual stimulation. As a kind of convenient EEG signal analysis and extraction, VEP is often used as input signal of control system. The intelligent home appliance system based on brain-computer interface (BCI) is put forward in view of the traditional concept of smart home. The brain-computer interface technology is introduced into the system on the basis of the original technology, which can solve the problems of independent living and rehabilitation treatment of the disabled. The intelligent home appliance control system based on portable brain-computer interface is mainly composed of EEG acquisition module, data analysis module, instruction conversion module, instruction transmission network and external equipment control, etc. The accuracy and speed of EEG data analysis is the focus of the research. In this paper, we design a set of intelligent household electrical appliances which only use EEG control by using visual evoked potential (VEP). Not only can the Steady-state visual evoked potentials, SSVEP signal be collected and analyzed in real time, but also it can be converted into the corresponding control command to achieve the purpose of controlling intelligent appliances without limb language. The system is mainly divided into two parts: a real-time EEG processor based on DSP platform and an intelligent home appliance device based on Zigbee wireless network. By using DSP TMS320F2812 chip of TI2000 series, the digital filtering, feature extraction and classification of SSVEP are realized by the characteristics of DSP high speed and low power consumption. Finally, the feature signal is converted into control command to control the intelligent appliance device on wireless network node. In the CCS3.3 software, we use C language to program the TMS320F2812 chip, so as to ensure that the system can collect and process the SSVEP effectively. Zigbee wireless network control intelligent home appliance system is mainly developed on the IAR810 software. The intelligent home appliance control system based on SSVEP is verified by online experiments and compared with the EEG processing device built on the upper computer LabVIEW platform. The processing time of DSP EEG processing platform is about 0.98 higher than that of traditional PC processing. The processing platform based on DSP has the mobility and portability, combined with the development of the new intelligent home technology of the Internet of things, it can better realize the EEG control appliance device, and ensure the reliability and portability of the system.
【学位授予单位】：天津职业技术师范大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TU855;TP273

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