Detection Methods of Brain Activity under Visual Cognitive Tasks Based on Information Processing of EEG Signals

在本研究项目中，我们对基于脑电图信号信息处理的视觉认知任务下脑活动检测方法的开发进行了研究，获得了以下研究成果：脑电信号处理方法研究了基于ar模型、准ARMAX模型和人工神经网络的脑电波特征提取和识别方法。研究了脑电信号的小波分析、盲分离分析和事件相关去同步分析的应用方法。提出了一种基于随机牛顿法的自适应聚类方法，并将其应用于脑电波分析。并证实了与同种刺激相关的ERP（事件相关电位）波属于一定的类别。视刺激周期和眨眼效应对ERP信号的识别和刺激的最佳视刺激周期约为500msec，眨眼产生的脑电波噪声成分在形状上与ERP相似。眨眼对脑电波识别的影响正在研究中。视觉认知任务下脑电波的识别Fz和Pz是识别ERP的有效电极位置。尝试了基于脑电信号处理（AR模型和统计模式识别方法）的目标或非目标刺激识别。基于区域脑电信号（空间滤波信号）的准确率在80%以上。这些结果表明，基于脑电信号处理的人机界面在不久的将来是可行的。

In this research project, we have performed studies on the development of detection method of brain activity under visual cognitive tasks based on information processing of electroencephalogram (EEG) signals and obtained following research results.1. EEG signal processing methodsWe have developed feature extraction and discrimination methods or EEG waves based on AR-model, Quasi ARMAX model and artificial neural networks. And it was also investigated that applied methods or wavelet analysis, blind separation analysis and event related de-synchronization analysis for EEG signals. An adaptive clustering method based on stochastic Newton method was developed and applied to EEG wave analysis. And it was confirmed that ERP (Event Related Potential) waves related to same kind of stimulus were belonged to some classes.2. Visual stimulating period and effect of eye blinkOptimal visual stimulating period were about 500msec for the discrimination or ERP signals, And eye blinks generated the noise component of EEG waves similar to ERP in shape. The effects of eye blink for the discrimination of EEG waves are under investigation.3. Discrimination of EEG waves under visual cognitive tasksThe effective electrode positions for the discrimination of ERP were Fz and Pz. Discrimination of stimuli (target or non-target) was tried based on EEG signal processing (AR model and statistical pattern recognition method). The accuracy based on regional EEG signals (spatial filtered signals) ws above 80%. These results suggest that human interface based on EEG signal processing would be feasible in near future.