基于头皮和表面肌电信号假手控制系统

提出了一种假手控制系统,该系统能够综合利用头皮和表面肌电信号的信息,实时地解码手部运动的速度、加速度和轨迹,从而实现对假手的灵活控制,且无需植入电极,便于维护。     

Regional brain electrical activity in posttraumatic stress disorder after motor vehicle accident.

This study examined whether patients with posttraumatic stress disorder (PTSD) related to motor vehicle accidents (MVAs) would show an abnormal pattern of electroencephalographic (EEG) alpha asymmetries, which has been proposed for particular types of anxiety. Patients with PTSD (n = 22) or subsyndromal PTSD (n = 21), traumatized controls without PTSD (non-PTSD with MVA; n = 21), and healthy controls without MVA (n = 23) underwent measurement of EEG activity during baseline and exposure to a neutral, a positive, a negative, and an accident-related picture. Differences in brain asymmetry between groups were observed only during exposure to trauma-related material. PTSD and subsyndromal PTSD patients showed a pattern of enhanced right anterior and posterior activation, whereas non-PTSD with MVA participants showed the opposite pattern. Furthermore, posterior asymmetry in nontraumatized healthy controls varied with gender, with female participants showing a pattern of higher right posterior activation. The results support the hypothesis that symptomatic MVA survivors are characterized by a pattern of right hemisphere activation that is associated with anxious arousal and symptoms of PTSD during processing of trauma-specific information. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于独立分量聚类分析的诱发脑电特征提取技术

发展了一种独立分量聚类分析的诱发脑电特征提取方法,利用诱发成分较强的序间相似性,使用Infomax结合K均值算法对脑电信号中的诱发成分进行分类和提取.该方法可以克服传统独立分量分解方法中诱发分量识别的困难,适用于重复刺激诱发脑电的高维数据自动分析处理.将该方法用于上肢想像动作任务的诱发脑电数据分析,结果显示该方法可以有效剥离背景噪声和提取诱发分量,使得信号的费雪可分性得到显著提升,进而获得更好的识别效果.研究结果表明独立分量自动聚类技术适用于认知行为脑电信号的分析,值得进一步研究.     

Multi-Modal Domain Adaptation Variational Autoencoder for EEG-Based Emotion Recognition

Traditional electroencephalograph (EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject, which restricts the application of the affective brain computer interface (BCI) in practice. We attempt to use the multi-modal data from the past session to realize emotion recognition in the case of a small amount of calibration samples. To solve this problem, we propose a multi-modal domain adaptive variational autoencoder (MMDA-VAE) method, which learns shared cross-domain latent representations of the multi-modal data. Our method builds a multi-modal variational autoencoder (MVAE) to project the data of multiple modalities into a common space. Through adversarial learning and cycle-consistency regularization, our method can reduce the distribution difference of each domain on the shared latent representation layer and realize the transfer of knowledge. Extensive experiments are conducted on two public datasets, SEED and SEED-IV, and the results show the superiority of our proposed method. Our work can effectively improve the performance of emotion recognition with a small amount of labelled multi-modal data.      

基于脑电实验的眨眼次数与疲劳相关性研究

眨眼次数可以反映一个人的疲劳程度,但目前对于这一结论的客观验证研究较少。脑电信号是已被证实的疲劳检测的可信标准。利用脑电信号与眨眼次数的相关性,为基于眨眼次数的疲劳检测方法提供了理论依据。设计实现基于脑电的疲劳检测实验,采集被试脑电信号的同时记录被试的眼部图像数据。利用小波变换对脑电信号进行特征提取,分析被试在实验过程中疲劳程度变化情况。同时,处理被试眼部图像数据,统计被试眨眼次数,分析变化趋势。对脑电数据和眨眼次数进行相关性分析,客观验证了眨眼次数随疲劳程度增大而增加的结论。     

基于脑电信号特征的驾驶疲劳检测方法研究

目前,疲劳驾驶已成为一种严重的社会问题,然而对于疲劳驾驶的检测与预防仍缺乏有效的技术手段.采用疲劳驾驶模拟实验、结合对象辨别实验和对被试面部表情变化分析,探索了脑电信号特征与驾驶疲劳状态间的相关性.提取脑电信号的δ波、θ波、α波、β波四种脑电节律的能量值作为疲劳驾驶的特征值,采用δ波能量值与θ波能量值之和与β波能量值的比值作为疲劳指数.结果显示,疲劳指数与被试疲劳程度呈正相关,验证了利用脑电信号检测疲劳程度的合理性与客观性,为疲劳检测提供了新的思路.     

基于脑电“微状态”的驾驶员脑负荷研究

驾驶疲劳影响驾驶员的警觉度和注意能力,同时严重影响道路交通安全。该文在实验室驾驶平台中模拟疲劳驾驶的基础上,提出了一种评估驾驶过程中大脑工作负荷的方法。实验结果表明,当大脑工作负荷较小时,在theta频段左前额高电势微状态出现的频率升高;当大脑工作负荷较大时频率降低。以此为指标,可以对驾驶员的放松和警觉状态进行检测。     

HHT在脑电信号去噪中的应用

脑电信号在采集过程中极易受到噪声的干扰,如何有效地去除混入脑电信号中的噪声已经成为信号前期处理的难点。根据脑电信号和噪声的特点,使用HHT方法去除脑电信号中的工频噪声,并通过信噪比和均方误差对去噪效果进行了检测。仿真结果表明该方法不仅能够有效地抑制工频噪声,还可以较好地保持脑电信号的波形特征。     

Detection the Character Wave in Epileptic EEG by Wavelet

Electro encephalography (EEG) is an important clinical tool in theoretical study, diagnosis and treatment of several neurological disorders such as epilepsy and sleeping disorders. For the most part, human epilepsy is the intrinsic brain pathology. Its major manifestation in the epileptic seizure, which may involve a discrete part of the brain partial or the whole cerebral mass generalized. Ictal EEG is characterized by repetitive high-amplitude activity, either fast spikes, slow waves, or s…     

基于CEEMD小波包算法的降噪方法研究

针对脑电信号易受噪声干扰的特性,提出一种使用CEEMD小波包对脑电信号进行降噪的方法。首先对脑电信号进行CEEMD分解,得到一组固有模态函数分量（IMF）,然后对包含噪声的IMF分量采用小波包阈值降噪,同时保留信号的低频IMF分量,最后将使用小波包阈值降噪的IMF分量和保留的IMF分量进行累加重构,从而得到最终降噪后的脑电信号。仿真结果表明采用CEEMD小波包对脑电信号进行降噪,在抑制噪声的同时,还有效地保留了脑电信号的细节特性,达到良好的去噪特性。