按摩模式对揉捏式按摩椅按摩舒适性的影响

以揉捏式按摩椅作为研究对象,采用自由模量幅度估计法.通过对标准化后的按摩舒适度评价值分别进行统计,得到颈肩、背部和腰部区域3个区域共计8个测点最舒适的按摩模式,并通过脑电测试实验从客观角度验证了对由自由模量幅度估计法所得的各按摩区域最佳按摩模式的科学性,探讨了按摩模式对按摩椅按摩舒适性的影响.     

PolyMorph: Increasing the Spelling Efficiency of P300 by Selection Matrix PolyMorphism and Sentence-Based Predictions

One application of the P300 brain electric signal is sentence spelling, which enables subjects who have lost control of their motor pathways to communicate by selecting characters in a matrix containing all alphabet symbols. This technology still suffers from both low communication/high error rates. A P300 speller, named PolyMorph, which jointly introduces the selection matrix polymorphism (reducing the matrix size by removing useless symbols) and sentence-based predictions (which forecast words on the basis of language statistics) is presented. This is accomplished by using a custom dynamic knowledge-base, tailored to the subject lexicon, and updated in real time with the selections of the subject. The effectiveness of the presented speller is measured in vivo and in silico. The results suggest that the use of PolyMorph increases the number of spelt characters per time unit and reduces the error rate.     

一种教学用脑波分析耳机的设计与实现

笔者阐述了一种教学环境中使用的脑波分析耳机的工作原理和设计方法,其核心是对EEG脑电波信号的采集、滤波,运用时频谱和复杂度分析的方法进行综合分析提取精神状态特征,设计神经网络多层分类器对精神状态进行分类和识别,最后完成精神状态的自动检测和识别,以提高学生上课的注意力。     

Advanced daytime polysomnographic preprocessing: A versatile approach for stream-wise estimation

The enhancement of monitoring biosignals plays a crucial role to thrive successfully computer-assisted diagnosis, ergo the deployment of outstanding approaches is an ongoing field of research demand. In the present article, a computational prototype for preprocessing short daytime polysomnographic (sdPSG) recordings based on advanced estimation techniques is introduced. The postulated model is capable of performing data segmentation, baseline correction, whitening, embedding artefacts removal and noise cancellation upon multivariate sdPSG data sets. The methodological framework includes Karhunen–Loève Transformation (KLT), Blind Source Separation with Second Order Statistics (BSS-SOS) and Wavelet Packet Transform (WPT) to attain low-order, time-to-diagnosis efficiency and modular autonomy. The data collected from 10 voluntary subjects were preprocessed by the model, in order to evaluate the withdrawal of noisy and artefactual activity from electroencephalographic (EEG) and electrooculographic (EOG) channels. The performance metrics are distinguished in qualitative (visual inspection) and quantitative manner, such as: Signal-to-Interference Ratio (SIR), Root Mean Square Error (RMSE) and Signal-to-Noise Ratio (SNR). The computational model demonstrated a complete artefact rejection in 80% of the preprocessed epochs, 4 to 8 dB for residual error and 12 to 30 dB in signal-to-noise gain after denoising trial. In comparison to previous approaches, N-way ANOVA tests were conducted to attest the prowess of the system in the improvement of electrophysiological signals to forthcoming processing and classification stages.     

Human emotion recognition and analysis in response to audio music using brain signals

Human emotion recognition using brain signals is an active research topic in the field of affective computing. Music is considered as a powerful tool for arousing emotions in human beings. This study recognized happy, sad, love and anger emotions in response to audio music tracks from electronic, rap, metal, rock and hiphop genres. Participants were asked to listen to audio music tracks of 1 min for each genre in a noise free environment. The main objectives of this study were to determine the effect of different genres of music on human emotions and indicating age group that is more responsive to music. Thirty men and women of three different age groups (15–25 years, 26–35 years and 36–50 years) underwent through the experiment that also included self reported emotional state after listening to each type of music. Features from three different domains i.e., time, frequency and wavelet were extracted from recorded EEG signals, which were further used by the classifier to recognize human emotions. It has been evident from results that MLP gives best accuracy to recognize human emotion in response to audio music tracks using hybrid features of brain signals. It is also observed that rock and rap genres generated happy and sad emotions respectively in subjects under study. The brain signals of age group (26–35 years) gave best emotion recognition accuracy in accordance to the self reported emotions.     

急性等容血液稀释自体血回输对老年骨科手术患者脑电双谱指数与肌松效应的影响

目的：观察急性等容血液稀释（ANH）自体血回输对老年骨科手术患者脑电双谱指数与肌松效应的影响,探究含有麻醉药成分的自体血回输对术后麻醉苏醒质量及安全性的影响。方法：择期骨科手术患者40例,年龄65～75岁,体质量55～80 kg,ASA Ⅰ～Ⅱ级,预计术中出血量≥600 mL。患者随机分为2组（n=20）：A组为进行ANH组,在麻醉诱导平稳后实施ANH,血细胞比容（Hct）目标值28%～30%;B组为对照组,即术中常规补液,不进行ANH。于术毕回输自体血时（T1）、回输10 min（T2）、20 min（T3）、30 min（T4）、40 min（T5）、50 min（T6）、60 min（T7）各时点观测两组患者脑电双频指数（BIS）、四个成串刺激（TOF值）及丙泊酚、顺式阿曲库铵的血浆药物浓度变化;观察并记录术前（T0）及T7时点血气变化,观察患者拔除气管导管的时间以及苏醒质量。结果：A组BIS值在术后T6、T7时点明显低于B组（P<0.05）,A组TOF值在T5、T6、T7时点明显低于B组（P<0.05）;A组与B组术后丙泊酚、顺式阿曲库铵的血药浓度组间比较差异无显著性（P>0.05）;A组在术后T7时点乳酸（Lac）值明显高于B组（P<0.05）;A组拔除气管导管的时间明显长于B组（P<0.05）;A组Aldrete评分明显低于B组（P<0.05）。结论：ANH后术毕自体血回输可增加血浆中麻醉药物浓度,加深患者的麻醉深度,增强肌松效应,使老年患者呼吸功能恢复和拔管时间延迟。     

基于近似熵的磁刺激穴位脑功能网络构建与分析

针灸是基于传统中医的理论,经临床实践已证明其疗效,然而其作用机制仍不清楚。磁刺激穴位为研究针灸理论提供了一种新的方法。基于图论的复杂网络的构建和分析方法可以帮助研究脑功能网络的拓扑结构和理解大脑的工作机制。在该研究中,通过磁刺激内关穴(PC6)采集EEG信号;运用非线性动力学方法(近似熵)和复杂网络理论,基于磁刺激内关穴的脑电信号构建脑功能网络并对脑功能网络进行分析;对比分析了安静和磁刺激两种状态下的脑功能网络的拓扑性质。实验结果表明,基于刺激内关穴构建的脑功能网络,其拓扑结构发生了改变,网络连接增强,信息传输效率提高,并且"小世界"属性增强。     

基于排序熵算法的麻醉深度检测系统设计

为辅助医生在手术过程中对患者麻醉深度状态的判断,设计了一种基于排序熵算法的麻醉深度检测系统。该系统主要包含3个部分,前端脑电预处理电路、排序熵算法数据处理电路和人机交互软件。前端电路主要包括各类放大电路和滤波电路;排序熵算法数据处理是基于stm32单片机完成;人机交互软件主要用于对测试结果进行记录显示等工作,辅助医生对患者麻醉状况的判断。依据以上方案制作了检测系统,并在实验室环境下进行了测试,能完成相关数据检测。     

Resting frontal EEG asymmetry as an endophenotype for depression risk: Sex-specific patterns of frontal brain asymmetry.

Resting frontal electroencephalographic (EEG) asymmetry has been hypothesized as a marker of risk for major depressive disorder (MDD), but the extant literature is based predominately on female samples. Resting frontal asymmetry was assessed on 4 occasions within a 2-week period in 306 individuals aged 18–34 (31% male) with (n = 143) and without (n = 163) lifetime MDD as defined by the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (American Psychiatric Association, 1994). Lifetime MDD was linked to relatively less left frontal activity for both sexes using a current source density (CSD) reference, findings that were not accounted for solely by current MDD status or current depression severity, suggesting that CSD-referenced EEG asymmetry is a possible endophenotype for depression. In contrast, results for average and linked mastoid references were less consistent but demonstrated a link between less left frontal activity and current depression severity in women. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Detection of motor imagery EEG signals employing Naïve Bayes based learning process

The objective of this study is to develop a reliable and robust analysis system that can automatically detect motor imagery (MI) based electroencephalogram (EEG) signals for the development of brain–computer interface (BCI) systems. The detection of MI tasks provides an important basis for designing a communication way between brain and computer in creating devices for people with motor disabilities. This paper presents a synthesis approach based on optimum allocation system and Naive Bayes (NB) algorithm for detecting mental states based on EEG signals. In this study, an optimal allocation (OA) is introduced to discover the most effective representatives with minimal variability from a large number of MI based EEG data and the NB classifier is employed on the extracted features for discriminating the MI signals. The feasibility and effectiveness of the proposed method is demonstrated by analyzing the results and its success on two public benchmark datasets. The results indicate that the proposed approach outperforms the most recently reported five methods and achieves 0.64–20.90% improvement on average accuracy. The performances of this proposed approach implies that it can be reliably used to detect EEG based MI activity and can be a promising avenue for EEG based BCI applications.