The electroencephalogram (EEG) rhythm and functional near-infrared spectroscopy (fNIRS) activation levels have not been compared between a healthy control group (HCG) and methamphetamine user group (MUG) with different addiction histories. This study used 64-electrode EEG and fNIRS to conduct an experiment that analyzed the resting and craving states. The EEG and fNIRS data of 56 participants were collected, including 14 healthy participants, 14 methamphetamine users with an addiction history of 0.5–5 years, 14 users with an addiction history of 5–10 years, and 14 users with an addiction history of 10–15 years. Isolated effective coherence (iCoh) within the brain network was used to process the EEG data. Statistical analysis was performed to compare differences in iCoh among the delta, theta, alpha, beta, and gamma bands and explore oxyhemoglobin activation levels in the ventrolateral prefrontal cortex, dorsolateral prefrontal cortex, orbitofrontal cortex, and frontopolar prefrontal cortex (FPC) of the control group. Finally, the Kmeans, Gaussian mixed model (GMM), linear discriminant analysis (LDA), support vector machine (SVM), Bayes, and convolutional neural networks (CNN) algorithms were used to classify methamphetamine users based on drug and neutral images. A 3-class accuracy was achieved. Changes in EEG and fNIRS activation levels of HCG and MUG with varied addiction histories were demonstrated.

麻醉深度监测对提高麻醉质量, 保障患者的手术期安全与康复具有极为重要的意义。系统地回顾了脑电信号分析算法, 主要包括脑电双频指数、麻醉趋势指数、脑状态指数等多种不同参数的算法在麻醉深度监测中的应用研究及进展情况。首先比较了这些参数的脑电分析算法在临床应用中的优缺点, 接着提出了具有优秀去噪能力的排序熵指数算法, 并引入反向映射神经网络对脑电双频指数、麻醉趋势和排序熵指数进行校正优化, 以便更精确地实现麻醉深度监护。最后展望了今后其应用于麻醉深度监测领域的发展前景。

The precise classification for the electroencephalogram (EEG) in different mental tasks in the research on brain-computer interface (BCI) is the key for the design and clinical application of the system. In this paper, a new combination classification algorithm is presented and tested using the EEG data of right and left motor imagery experiments. First, to eliminate the low frequency noise in the original EEGs, the signals were decomposed by empirical mode decomposition (EMD) and then the optimal kernel parameters for support vector machine (SVM) were determined, the energy features of the first three intrinsic mode functions (IMFs) of every signal were extracted and used as input vectors of the employed SVM. The output of the SVM will be classification result for different mental task EEG signals. The study shows that mean identification rate of the proposed algorithm is 95%, which is much better than the present traditional algorithms.

采用近似熵算法得到19名健康志愿者在5 Hz频率的光（每秒光重复诱发5次）诱发前后的脑电(EEG)θ波段近似熵值(Apen θ)，经T-检验(P<0.05)发现光诱发后Apen θ值显著下降，其中在顶部、前额、颞部和枕部位置有显著性变化，颞部和枕部变化最大，左右半脑平均Apen θ均减少，右脑区更为显著。结果表明Apen θ能够描述外部光诱发情况下的EEG波段特征，并定量给出外部光诱发程度。

通过脑电长程相关性的分析，定量研究了35 GHz毫米波辐照大鼠时产生的应激反应。通过退趋势分析法，得到反映高频成分的标度指数，显示在辐照前该成分具有布朗噪声的特性,辐照时具有长程相关性;而反映低频成分的标度指数显示在辐照前该成分具有长程相关性，辐照时成为布朗噪声。引进应激指标参量低频成份标度指标数/高频成份标度指标数,用其值的平均变化率来衡量大鼠在35 GHz毫米波作用下应激反应的剧烈程度。通过计算得到辐照时应激指标参量增加了49.9%±13.6%，说明35 GHz毫米波辐照使得大鼠脑电的高频部分变得更加有序，而低频成分变得更加无序，表明大鼠受35 GHz毫米波辐照而产生了剧烈应激反应。

使用高功率8毫米波辐照清醒的大鼠,同时测量其脑电图。用小波进行多分辨分析,将脑电信号分解成各个节律信号,分析其变化。辐照时,大鼠出现了非节律性的高幅δ波,而且在慢波中夹杂着癫痫样放电,这说明大鼠在受到辐照时产生高度节律紊乱,提示了其受到强烈刺激而进入失能状态。大鼠受到辐照时失能期为辐照第(1.3±0.5)s到(17.2±2.3)s,辐照于30 s时结束,第(62.7±2.6)s时,其脑电图开始恢复。结果表明:大鼠受到高功率毫米波辐照时最佳失能发生在表皮温升较快、表皮损伤较轻的时候,过长时间的辐照不会延长其失能时间,反而会导致其损伤加大。因此,应用高功率毫米波的失能效应时须控制一定剂量。

以心功能、血气、脑电图作为参数,评价了Ｈｅ-Ｎｅ激光麻醉对马生理功能的影响。