Correlations between the psychological peculiarities of an individual and the efficacy of a single neurofeedback session (by the EEG characteristics)

Modifications of different EEG rhythms induced by a single neurofeedback session (by the EEG characteristics) directed toward an increase in the ratio of the spectral powers (SPs) of the α vs θ oscillations were compared with the psychological characteristics of the tested subjects (the group included 30 persons). A generally accepted neurofeedback technique was used; the intensity of acoustic white noise served as the feedback signal. EEG potentials were recorded from the C3 and C4 leads. Psychological testing was carried out using Eysenck’s (EPQ), Rusalov’s (OST), and (16 PF) questionnaires. The directions of changes in the SPs of EEG frequency components were found to significantly correlate with some individuality-related peculiarities of the tested subjects. The SP of the δ rhythm correlated with the EPQ scale “neuroticism,” OST scale “social plasticity,” and 16 PF factors H (“parmia”), I (“premsia”), and Q₃ (“self-control of behavior”). The SP of the θ component demonstrated correlations with the OST scales “ergisity,” “plasticity,” and “social temp” and with 16 PF factors M (“autia”), Q₄ (“frustration”), and Q1 (“exvia”). The SP of the α rhythm correlated with 16 PF factors Q₃ (“self-control of behavior”), G (“strength of superEgo”), O (“hypothymia”), L (“protension”), and N (“shrewdness”). The SP of the β rhythm correlated with the OST scale “emotionality,” while that of the γ rhythm showed correlations with the 16 PF indices L (“protension”) and M (“autia”). Changes in the ratio of the α vs θ SPs correlated with the EPQ scale “neuroticism.” Thus, our data confirm the statement that a high individual variability of the results of a single (first in the series) neurofeedback session is to a great extent related to peculiarities of the individual psychological pattern of the subject. Neirofiziologiya/Neurophysiology, Vol. 38, No. 3, pp. 239–247, May–June, 2006.     

脑电图信号在疲劳驾驶检测中的应用与挑战

当前快节奏生活已经成为日常生活的常态,对人们的身心健康带来了不少负面影响,特别是在驾驶过程中容易出现疲劳困倦的情况。因此,疲劳驾驶检测技术成为了研究的热点,并取得了显著的进展。本文将介绍基于脑电图（electroencephalogram,EEG）信号的疲劳驾驶检测方法。本文首先概述基于EEG信号的疲劳驾驶检测的总体流程,包括信号采集、预处理、特征提取和分类等步骤。然后,详细介绍EEG信号中与疲劳相关的特征及其在疲劳驾驶检测中的应用。这些特征包括频域特征、时域特征、拓扑特征等,通过分析这些特征可以有效地识别出驾驶员是否处于疲劳状态。接下来,探讨目前已有的疲劳驾驶检测模型的性能评估和评价指标。针对基于EEG信号的疲劳驾驶检测,常用的指标包括准确率、灵敏度、特异度等。本文分析不同模型在这些指标上的表现,并比较它们的优劣。本文还简单介绍了EEG信号分类方法及其应用现状。常见的分类方法包括支持向量机、神经网络、贝叶斯分类器等,这些方法在疲劳驾驶检测中得到了广泛应用。针对目前基于EEG信号的疲劳驾驶检测面临的问题,本文提出了一些解决方法。例如,统一数据标注标准、增加被试数量等。最后,在总结全文内容的基础上讨论了基于EEG信号的疲劳驾驶技术未来的发展方向。在未来,可以进一步提升疲劳驾驶检测技术的准确性和实用性,以更好地应对快节奏社会给驾驶员安全带来的挑战。     

基于beta爆发期的帕金森病患者脑功能网络特征研究

目的 帕金森病 （Parkinson’s disease, PD） 的核心症状是运动功能受损。 大脑基底神经节运动网络中beta频段神经电活动同运动功能密切相关。本文结合头皮脑电、脑功能网络与临床量表评估,探讨beta频段神经电活动爆发期对脑功能网络特征的影响,为临床诊疗提供参考。方法 本文纳入13名PD患者为PD组,13名健康受试者为健康对照组,采集两组受试者的静息态脑电数据,提取脑电数据中的beta爆发期与非爆发期,利用加权相位滞后指数构建相位同步网络,对比两组受试者不同时期、4个频段下的相位同步网络拓扑特征参数的变化,讨论网络特征变化与临床症状之间的相关性。结果 PD患者beta爆发期时4个频段下相位同步网络拓扑特征参数较健康受试者显著升高,beta爆发期时beta频段下相位同步网络的平均聚类系数与UPDRS-III评分呈负相关,非爆发期时low gamma频段下相位同步网络的平均聚类系数与UPDRS评分和UPDRS-III评分、全局效率和平均度与UPDRS-III评分之间呈正相关。结论 PD患者beta爆发期脑功能网络特征显著提高,beta爆发期时beta频段脑功能网络特征与临床量表评分负相关,而非爆发期时low gamma频段脑功能网络特征与临床量表评分正相关,说明PD患者运动功能与beta爆发期有关。本文结果可为PD的诊断提供参考。     

Role of electroencephalogram and oxygen saturation in the induction mechanism of arousal for obstructive sleep apnea-hypopnea syndrome patients

Arousal concomitant with obstructive sleep apnea-hypopnea syndrome (OSAHS) is known to result in sleep fragmentation and excessive daytime sleepiness. The cause of arousal is multifarious, and the mechanism is not yet clear. The aim of this study was to further research the induction mechanism of arousal by investigating the variation of electroencephalogram (EEG) and oxygen saturation (SaO₂). This study enrolled 20 subjects with a clinical diagnosis of OSAHS who underwent overnight polysomnography. Respiratory events and arousals were scored, and individuals with insufficient samples (<30) were excluded. Thus, 13 subjects mostly with severe OSAHS were analyzed in this study. The wavelet coefficients, spectral power of EEG (C4-M1 and C3-M2) before arousal or airway reopening, and the maximum desaturations of SaO₂ during respiratory events were analyzed. For most subjects, EEG (in stages N1 and N2) during respiratory events with arousals exhibited significantly lower values of wavelet coefficients and spectral power (p < 0.05). The maximum desaturations of SaO₂ during respiratory events with arousals are larger than those without among individual. In binary logistic regression analysis, the P values of EEG features and SaO₂ desaturation were both less than 0.001. Our results demonstrate that in light NREM stage, less activity in EEG during respiratory events and larger SaO₂ drop both independently were related to the occurrence of arousal. These significant differences come from major subjects based on the statistical analysis, and help supplement the induction mechanism of arousal.