Abstract: EEG recognition is among the key issues in the Brain Computer Interface（BCI） technology. For the data processing and feature extraction of EEG signal, the concept of attention calculated by wavelet transform coefficient is presented to describe the concentrate intensity and the activity of brain waves, and the detection algorithms are obtained under different concentration states. An EEG signal control rail car system is designed based on ARM processor. The value of attention is sent to the system, which realizes the control of the rail car speed under different concentration states. Experimental results show that the feature of attention is better than the single frequency of FFT in terms of recognition results. This system can quickly extract the feature of EEG and real-time control rail car driving, and the performance is stable and it has a certain practical value.

Key words: Brain Computer Interface（BCI）, EEG signal, wavelet transform, ARM processor, rail car

摘要： 脑波信号识别是脑机接口（BCI）技术需要解决的关键问题之一。针对脑波信号的数据处理以及特征提取，提出了采用小波变换系数计算“专心度”的特征来描述精神力集中强烈程度以及脑电波活跃状况，并结合实际测试得出不同精神状态下的检测算法。设计了一款基于ARM处理器的脑波控制轨道小车系统，将“专心度”值发送给小车系统，实现了不同精神状态下对小车车速的控制。实验结果表明，采用专心度的特征在识别效果方面要优于FFT的单一频率特征。并且该系统能够快速提取出脑波特征并实时地控制轨道小车行驶，系统性能稳定具有一定的实用价值。

关键词: 脑机接口（BCI）, 脑波信号, 小波变换, ARM处理器, 轨道小车