脑电图数据的相关性分析

目的:本文对客观记录的脑电图数据进行相关性分析,为正确理解人脑的不同局部以及局部与整体之间的相关性提供基础实验依据和理论根据,为脑电图研究的其它分析做基础分析.方法:分别对10导联电极和21导联电极的两组脑电图数据做互相关和自相关的相关性分析,得出在不同条件、事件下脑的各导联电极的自相关分析结果和左右对称导联电极的互相关分析结果,最后得出相关性值随实验条件和事件的具体变化.数据处理和统计分析采用独立设计的脑电图分析工具箱和相关性分析程序.结果:脑电图数据经过相关性分析后得到的相关图具有和脑电波相似的波形,具有周期性,时间滞后对应的标准化的相关图显示出相关性值的第一个主峰是最大的,主峰频率一般在8到13Hz的脑电波频段内;最后得到的相关性值随事件的变化而改变的结果说明实验事件对相关性的影响.结论:自相关和互相关都是随着时间滞后的增大而逐渐减小;波具有较强的相关性;相关图在从零延迟开始时间滞后所出现的第一个波峰是主峰,反应出相对较强的相关性;从大量数据的分析最后得到的相关性与事件的关系表明相关性是受事件影响的.     

不同麻醉方法对二氧化碳气腹致应激激素和循环功能的影响

目的：观察比较全麻和全麻复合硬膜外麻醉在腹腔镜胆囊切除术（LC）时,对CO2气腹致应激激素和循环功能的影响。方法：40例LC患者随机分为全麻组（A组,n=20）和全麻复合硬膜外麻醉组（B组,n=20）,连续监测SBP、DBP、MAP、HR、ECG、SpO2、PETCO2,记录两组患者麻醉前（T1）、插管后（T2）、气腹后10min（T3）、气腹后20min（T4）和放气后10min（T5）的SBP、DBP、MAP、HR、同时抽静脉血测定血糖（Glu）、血浆皮质醇（Cor）、促肾上腺皮质激素（ACTH）和血管紧张素-Ⅱ（AT-Ⅱ）。结果：T1时两组所有检测值比较差异无显著性（P＆gt;0.05）;在T2、T3、T4、T5时A组SBP、DBP、MAP值明显升高,B组较基础值下降,组间差异显著（P＆lt;0.05）;气腹建立后A组HR,Glu、Cor、ACTH、AT-Ⅱ明显上升（P＆lt;0.05）,B组无显著变化（P＆gt;0.05）,（除ACTH外）组间差异显著（P＆lt;0.05）。结论：全麻复合硬膜外麻醉用于LC手术时,能相对更好的的抑制CO2气腹所致应激激素的升高,使循环更稳定。     

急性轻中度缺氧EEG信号的复杂度研究

建立了一个急性高空缺氧实验模型,记录了四种不同高度条件下从缺氧前(正常呼吸)到缺氧后30分钟时的EEG,分析了其复杂度。发现缺氧引起复杂度明显变化,随时间和高度增加,一定程度缺氧可使EEG复杂度低于正常。表明EEG复杂度对脑缺氧较为敏感,可用于缺氧程度进行评估,有望成为临床诊断的一个指标。     

Analysis and classification of speech imagery EEG for BCI

Electroencephalogram (EEG) is generally used in brain–computer interface (BCI), including motor imagery, mental task, steady-state evoked potentials (SSEPs) and P300. In order to complement existing motor-based control paradigms, this paper proposed a novel imagery mode: speech imagery. Chinese characters are monosyllabic and one Chinese character can express one meaning. Thus, eight Chinese subjects were required to read two Chinese characters in mind in this experiment. There were different shapes, pronunciations and meanings between two Chinese characters. Feature vectors of EEG signals were extracted by common spatial patterns (CSP), and then these vectors were classified by support vector machine (SVM). The accuracy between two characters was not superior. However, it was still effective to distinguish whether subjects were reading one character in mind, and the accuracies were between 73.65% and 95.76%. The results were better than vowel speech imagery, and they were suitable for asynchronous BCI. BCI systems will be also extended from motor imagery to combine motor imagery and speech imagery in the future.     

多导睡眠记录在新生儿脑电图监测中的应用

新生儿期的脑电图（electroencephalogram EEG）不易看出睡眠、觉醒的变化,临床需要用脑电图监测眼球运动并应用肌电图、心电图、呼吸等多导描记综合判断。     

脑电监测SD大鼠异氟醚全身麻醉模型的建立及脑电分析

目的:建立脑电监测SD大鼠异氟醚全身麻醉模型并分析脑电监测结果。方法:随机选取SD大鼠20只,先行脑电电极置入术,术后使用密闭吸入麻醉动物行为学观察圆筒,观察异氟醚引起的麻醉诱导、维持、觉醒状态并记录诱导、觉醒时间。将记录的行为学结果对照典型脑电图波形改变判断麻醉深度。结果:实验SD大鼠均检测出脑电图,通过对照行为学观察发现动态脑电监测结果同异氟醚麻醉过程进展一致。在麻醉过程中SD大鼠出现典型的全身麻醉脑电循环。结论:动态脑电监测和SD大鼠行为学观察可以准确反应全身麻醉深度。     

A two-dimensional approach for lossless EEG compression

In this paper, we study various lossless compression techniques for electroencephalograph (EEG) signals. We discuss a computationally simple pre-processing technique, where EEG signal is arranged in the form of a matrix (2-D) before compression. We discuss a two-stage coder to compress the EEG matrix, with a lossy coding layer (SPIHT) and residual coding layer (arithmetic coding). This coder is optimally tuned to utilize the source memory and the i.i.d. nature of the residual. We also investigate and compare EEG compression with other schemes such as JPEG2000 image compression standard, predictive coding based shorten, and simple entropy coding. The compression algorithms are tested with University of Bonn database and Physiobank Motor/Mental Imagery database. 2-D based compression schemes yielded higher lossless compression compared to the standard vector-based compression, predictive and entropy coding schemes. The use of pre-processing technique resulted in 6% improvement, and the two-stage coder yielded a further improvement of 3% in compression performance.     

Local discriminative spatial patterns for movement-related potentials-based EEG classification

A novel discriminant method, termed local discriminative spatial patterns (LDSP), is proposed for movement-related potentials (MRPs)-based single-trial electroencephalogram (EEG) classification. Different from conventional discriminative spatial patterns (DSP), LDSP explicitly considers local structure of EEG trials in the construction of scatter matrices in the Fisher-like criterion. The underlying manifold structure of two-dimensional spatio-temporal EEG signals contains more discriminative information. LDSP is an extension to DSP in the sense that DSP can be formulated as a special case of LDSP. By constructing an adjacency matrix, LDSP is calculated as a generalized eigenvalue problem, and so is computationally straightforward. Experiments on MRPs-based single-trial EEG classification show the effectiveness of the proposed LDSP method.     

脑电监测SD大鼠异氟醚全身麻醉模型的建立及脑电分析

目的建立脑电监测SD大鼠异氟醚全身麻醉模型并分析脑电监测结果。方法：随机选取SD大鼠20只,先行脑电电极置入术,术后使用密闭吸入麻醉动物行为学观察圆筒,观察异氟醚引起的麻醉诱导、维持、觉醒状态并记录诱导、觉醒时间。将记录的行为学结果对照典型脑电图波形改变判断麻醉深度。结果：实验SD大鼠均检测出脑电图,通过对照行为学观察发现动态脑电监、}测结果同异氟醚麻醉过程进展一致。在麻醉过程中SD大鼠出现典型的全身麻醉脑电循环。结论：动态脑电监测和SD大鼠行为学观察可以准确反应全身麻醉深度。     

Eigenspace Time Frequency Based Features for Accurate Seizure Detection from EEG Data

Background

Epilepsy is a neurological disorder that affects over 2% of the world population. Epilepsy patients suffer from recurring seizures that can be very harmful. The unpredictability of seizures is a major concern for medical practitioners because uncontrollable seizures can lead to sudden death and morbidity. A system that could warn patients and doctors alike about the impending seizure event would dramatically enhance the quality of life for patients.

EEG Based Evaluation of Examination Stress and Test Anxiety Among College Students

BackgroundAdolescence is a crucial chapter in life and the presence of stress, depression, and anxiety at this stage is a great concern. Prolonged stress is one of the risk factors that may induce suicidal thoughts, destructive ideation, abuse of alcohol, and drugs in adulthood. Based on a record from National Crime Records Bureau, In India over 2320 children were committed suicide per year because of failure in examinations. This raised number implies the severity of this issue and its major impact on society.ObjectivesThe main objective of this paper is to analyze the cognitive stress in students during examination period using EEG biomarkers.Methods and ResultsEEG signal was acquired in two different test conditions such as before examination with 12 minutes and after examination with 3 minutes from 14 subjects with eight electrodes located using wireless Enobio device (Neuro electrics) with 10-20 international lead system. The three brain waves such as theta, alpha, beta relative band energies were considered, and EEG band ratios such as heart rate, neural activity, arousal index, vigilance index and cognitive performance attentional resource index extracted between before and after examination condition using db4 wavelet family with 6 level decomposition. The statistical results suggest that after examination the relative sub-band energies α, β, and θ were decreased significantly (p < 0.05) as compared to before examination. Also, the EEG band ratio such as heart rate and vigilance index shows significant (p < 0.05) decrease after the examination as compared to before examination. It was found that there was a significant (p < 0.05) rise in the arousal index, Cognitive performance attentional resource index (CPARI), and neural activity after the examination as compared to before examination.ConclusionThe experimental results found that the memory and concentration were high before examination, which concludes that adolescence group examination stress was high before examination period as compared to after examination. In the case of gender group comparison, theta energy band for male students was found high compared to female students in before examination state such that it concludes that male students were highly stressed (before examination) than female students. Overall, our results suggest that after examination male students with lower heart rate index than female students which implies the male students control their stress levels as compared to females in the same stress situation.     

Validity of a novel method to measure vertical oscillation during running using a depth camera

Recent advancements in low-cost depth cameras may provide a clinically accessible alternative to conventional three-dimensional (3D) multi-camera motion capture systems for gait analysis. However, there remains a lack of information on the validity of clinically relevant running gait parameters such as vertical oscillation (VO). The purpose of this study was to assess the validity of measures of VO during running gait using raw depth data, in comparison to a 3D multi-camera motion capture system. Sixteen healthy adults ran on a treadmill at a standard speed of 2.7 m/s. The VO of their running gait was simultaneously collected from raw depth data (Microsoft Kinect v2) and 3D marker data (Vicon multi-camera motion capture system). The agreement between the VO measures obtained from the two systems was assessed using a Bland-Altman plot with 95% limits of agreement (LOA), a Pearson’s correlation coefficient (r), and a Lin’s concordance correlation coefficient (r_c). The depth data from the Kinect v2 demonstrated excellent results across all measures of validity (r = 0.97; r_c = 0.97; 95% LOA = −8.0 mm – 8.7 mm), with an average absolute error and percent error of 3.7 (2.1) mm and 4.0 (2.0)%, respectively. The findings of this study have demonstrated the ability of a low cost depth camera and a novel tracking method to accurately measure VO in running gait.     

Dynamics of EEG potentials at the beginning of a series of EEG-feedback sessions

We studied changes in the amplitudes of event-related EEG potentials (ERPs) and power spectra of background EEG in the course of a series of EEG-feedback sessions directed toward an increase in the ratio of powers of the α vs θ rhythms. The examined group included 70 volunteers divided into an experimental group (n = 37) and a control group (n = 33). The intensity of acoustic white noise overlapping the musical background served as a feedback signal; it became lower with increase in the above ratio, while in the control group it remained constant. The EEG potentials were recorded from C3 and C4 leads. The ERPs were recorded within a paradigm of measuring time intervals. Within a series of EEG-feedback sessions, the α/θ ratio decreased somewhat both in the control and experimental groups, but in subjects of the latter group this decrease was less significant, and the mean intragroup index became significantly greater than the respective value in the control group after the end of the third session. The EEG-feedback sessions also resulted in significant increases in the amplitudes of early components of the readiness potential in both hemispheres and in the amplitude of the contingent negative variation in the right hemisphere. We conclude that, in most healthy subjects, at least three sessions of α/θ training are necessary to form an effective series providing considerable changes in the pattern of EEG potentials. Neirofiziologiya/Neurophysiology, Vol. 39, No. 1, pp. 88–98, January–February, 2007.     

Automated detection of schizophrenia using optimal wavelet-based [... formula ...] norm features extracted from single-channel EEG

Schizophrenia (SZ) is a mental disorder, which affects the ability of human thinking, memory, and way of living. Manual screening of SZ patients is tedious, laborious and prone to human errors. Hence, we developed a computer-aided diagnosis (CAD) system to diagnose SZ patients accurately using single-channel electroencephalogram (EEG) signals. The EEG signals are nonlinear and non-stationary. Hence, we have used wavelet-based features to capture the hidden non-stationary nature present in the signal. First, the EEG signals are subjected to the the wavelet decomposition through six iterations, which yields seven sub-bands. The l1 norm is computed for each sub-band. The extracted norm features are disseminated to various classification algorithms. We have obtained the highest accuracy of 99.21% and 97.2% using K-nearest neighbor classifiers with ten-fold and leave-one-subject-out cross-validations. The developed single-channel EEG wavelet-based CAD model can help the clinicians to confirm the outcome of their manual screening and obtain an accurate diagnosis.     

High-density EEG Recordings of the Freely Moving Mice using Polyimide-based Microelectrode

Electroencephalogram (EEG) indicates the averaged electrical activity of the neuronal populations on a large-scale level. It is widely utilized as a noninvasive brain monitoring tool in cognitive neuroscience as well as a diagnostic tool for epilepsy and sleep disorders in neurology. However, the underlying mechanism of EEG rhythm generation is still under the veil. Recently introduced polyimide-based microelectrode (PBM-array) for high resolution mouse EEG¹ is one of the trials to answer the neurophysiological questions on EEG signals based on a rich genetic resource that the mouse model contains for the analysis of complex EEG generation process. This application of nanofabricated PBM-array to mouse skull is an efficient tool for collecting large-scale brain activity of transgenic mice and accommodates to identify the neural correlates to certain EEG rhythms in conjunction with behavior. However its ultra-thin thickness and bifurcated structure cause a trouble in handling and implantation of PBM-array. In the presented video, the preparation and surgery steps for the implantation of PBM-array on a mouse skull are described step by step. Handling and surgery tips to help researchers succeed in implantation are also provided.     

EEG Spectral Analysis of Relaxation Techniques

The acute central nervous system effects of relaxation techniques (RT) have not been systematically studied. We conducted a controlled, randomized study of the central nervous system effects of RT using spectral analysis of EEG activity. Thirty-six subjects were randomized to either RT or a music comparison condition. After listening to an RT audiotape or music audiotapes daily for 6 weeks, the acute central nervous system effects of RT and music were measured using power spectral analysis of alpha and theta EEG activity in all cortical regions. RT produced significantly greater increases in theta activity in multiple cortical regions compared to the music condition. These findings are consistent with widespread reductions in cortical arousal during RT. They extend previous findings and suggest that theta, and not alpha, EEG may be the most reliable marker of the central nervous system effects of RT. These findings demonstrate that RT produce greater reductions in central nervous system activity than a credible comparison condition. The findings suggest that RT represent a hypoactive central nervous system state that may be similar to Stage 1 sleep and that RT may exert their therapeutic effects, in part, through cerebral energy conservation/restoration.     

电针对实验性癫痫发作的影响：脑电的功率谱分析

以电惊厥和青霉素致痫作为实验性癲痫的动物模型。采用脑电的计算机功率谱分析技术,研究了电针作用于发作过程中脑电各频段功率百分比的变化。在安静的大鼠,脑电以δ和θ频段为主,其功率主峰在δ频段。青霉素致痫和电惊厥使δ频段功率百分比下降,α和β频段功率百分比增加,主功率频段右移,总功率亦大大增强。本实验采用的电针对背景脑电活动没有明显影响。而电针加电惊厥或青霉素致痫,δ频段功率百分比复又增加,α和β频段功率百分比则下降,主功率频段又回到δ频段,总功率也显著减少。压缩功率谱阵图直观地显示了这种变化。结果提示,电针可使大鼠脑电出现同步化趋势,可能是加强了脑的抑制过程,从而抑制了癲痫发作的。     

功能磁共振和脑电神经成像技术与大脑刺激相结合的研究进展

随着对神经机制问题阐述水平的迅速提高,所应用的神经成像技术、方法及各种工具的复杂程度也在不断提高．一方面是神经成像技术本身的不断发展,另一方面则是大脑直接刺激与神经成像技术同步记录方法的发展．经颅磁刺激-功能磁共振成像同步技术(TMS-fMRI)和经颅磁刺激-脑电技术(TMS-EEG)能为研究大脑网络的功能和有效连通性提供技术手段,该技术在多种认知领域的发展和应用,为神经科学、认知心理学、神经信息学等学科的研究者对人脑的研究开启了多条通道,更加有利于深入地理解人类大脑的工作机制．     

咪达唑仑用于上肢创伤病人麻醉前给药的遗忘作用与脑电变化

目的:评价咪达唑仑用于上肢创伤病人行高位硬膜外阻滞的遗忘作用和相应的脑电变化。方法:选择40例单纯上肢创伤病人行高位硬膜外阻滞随机双盲分为四组:咪达唑仑0.15mg/kg和0.10mg/kg,安定0.20mg/kg,生理盐水2ml,麻醉前30分钟肌注,观察用药前后脑电改变,镇静分级对麻醉操作的遗忘率和程度以及术后心理状况。结果:用咪达唑仑后,镇静程度与遗忘效果有显著变化。0.10mg/kg咪达唑仑的遗忘率为70%,其中90%为不全遗忘;0.15mg/kg咪达唑仑可达到100%完全遗忘;咪达唑仑的脑电功率谱变化为δ和β相对功率明显增加,而θ和α相对功率明显下降。结论:肌注咪达唑仑完全可以消除病人对高位硬膜外麻醉穿刺操作过程的不良回忆,并与剂量相关。