The multi-fractal de-trended fluctuation analysis was used to estimate the mental stress in the present study. In order to obtain the optimal fractal order of the multi-fractal de-trended fluctuation analysis, we analyzed the relationship between singular index and Hurst index with order. We recorded the electroencephalogram (EEG) of 14 students, compared the relationship between singular index, Hurst index and quality index, ensured the optimal order being [—5, 5] and achieved the estimation of mental stress with the β wave in the EEGs. The result indicated that Hurst index and quality index of the EEGs under mental stress were greater than those of EEGs in the relaxing state. The Hurst index was gradually decreasing with the order increasing and was finally approaching a constant, while the quality index was amplified and variation of amplitude of the singular index was more obvious. We also compared the amplitude and the width of singular spectrum of the EEGs under the two conditions, and results indicated that the characteristics of multi-fractal spectrum of the EEGs under different conditions were different, namely the width of singular spectrum of the EEGs under mental stress was greater than that under relax condition.
ObjectiveTo explore the application effect of standardized management on video-electroencephalogram (VEEG) monitoring.MethodsIn January 2018, a multidisciplinary standardized management team composed with doctors, technicians, and nurses was established. The standardized management plan for VEEG monitoring from outpatient, pre-hospital appointment, hospitalization and post-discharge follow-up was developed; the special quilt for epilepsy patients was designed and customized, braided for the patient instead of shaving head, standardized the work flow of the staff, standardized the health education of the patients and their families, and standardized the quality control of the implementation process. The standardized managemen effect carried out from January to December 2018 (after standardized managemen) was compared with the management effect from January to December 2017 (before standardized managemen).ResultsAfter standardized management, the average waiting time of patients decreased from (2.08±1.13) hours to (0.53±0.21) hours, and the average hospitalization days decreased from (6.63±2.54) days to (6.14±2.17) days. The pass rate of patient preparation increased from 63.14% to 90.09%. The capture rate of seizure onset increased from 73.37% to 97.08%. The accuracy of the record increased from 33.12% to 94.10%, the doctor’s satisfaction increased from 76.34±29.53 to 97.99±9.27, and the patient’s satisfaction increased from 90.04±18.97 to 99.03±6.51. The difference was statistically significant (P<0.05).ConclusionStandardization management is conducive to ensuring the homogeneity of clinical medical care, reducing the average waiting time and the average hospitalization days, improving the capture rate and accuracy of seizures, ensuring the quality of medical care and improving patient’s satisfaction.
Electroconvulsive therapy (ECT) is an interventional technique capable of highly effective neuromodulation in major depressive disorder (MDD), but its antidepressant mechanism remains unclear. By recording the resting-state electroencephalogram (RS-EEG) of 19 MDD patients before and after ECT, we analyzed the modulation effect of ECT on the resting-state brain functional network of MDD patients from multiple perspectives: estimating spontaneous EEG activity power spectral density (PSD) using Welch algorithm; constructing brain functional network based on imaginary part coherence (iCoh) and calculate functional connectivity; using minimum spanning tree theory to explore the topological characteristics of brain functional network. The results show that PSD, functional connectivity, and topology in multiple frequency bands were significantly changed after ECT in MDD patients. The results of this study reveal that ECT changes the brain activity of MDD patients, which provides an important reference in the clinical treatment and mechanism analysis of MDD.
High-density channels are often used to acquire electroencephalogram (EEG) spatial information in different cortical regions of the brain in brain-computer interface (BCI) systems. However, applying excessive channels is inconvenient for signal acquisition, and it may bring artifacts. To avoid these defects, the common spatial pattern (CSP) algorithm was used for channel selection and a selection criteria based on norm-2 is proposed in this paper. The channels with the highest M scores were selected for the purpose of using fewer channels to acquire similar rate with high density channels. The DatasetⅢa from BCI competition 2005 were used for comparing the classification accuracies of three motor imagery between whole channels and the selected channels with the present proposed method. The experimental results showed that the classification accuracies of three subjects using the 20 channels selected with the present method were all higher than the classification accuracies using all 60 channels, which convinced that our method could be more effective and useful.
Vigilance is defined as the ability to maintain attention for prolonged periods of time. In order to explore the variation of brain vigilance in work process, we designed addition and subtraction experiment with numbers of three digits to induce the vigilance to change, combined it with psychomotor vigilance task (PVT) to measure this process of electroencephalogram (EEG), extracted and analyzed permutation entropy (PE) of 11 cases of subjects' EEG and made a brief comparison with nonlinear parameter sample entropy (SE). The experimental results showed that:PE could well reflect the dynamic changes of EEG when vigilance decreases, and has advantages of fast arithmetic speed, high noise immunity, and low requirements for EEG length. This can be used as a measure of the brain vigilance indicators.
Mental fatigue is an important factor of human health and safety. It is important to achieve dynamic mental fatigue detection by using electroencephalogram (EEG) signals for fatigue prevention and job performance improvement. We in our study induced subjects' mental fatigue with 30 h sleep deprivation (SD) in the experiment. We extracted EEG features, including relative power, power ratio, center of gravity frequency (CGF), and basic relative power ratio. Then we built mental fatigue prediction model by using regression analysis. And we conducted lead optimization for prediction model. Result showed that R2 of prediction model could reach to 0.932. After lead optimization, 4 leads were used to build prediction model, in which R2 could reach to 0.811. It can meet the daily application accuracy of mental fatigue prediction.
Common spatial pattern (CSP) is a very popular method for spatial filtering to extract the features from electroencephalogram (EEG) signals, but it may cause serious over-fitting issue. In this paper, after the extraction and recognition of feature, we present a new way in which the recognition results are fused to overcome the over-fitting and improve recognition accuracy. And then a new framework for EEG recognition is proposed by using CSP to extract features from EEG signals, using linear discriminant analysis (LDA) classifiers to identify the user's mental state from such features, and using Choquet fuzzy integral to fuse classifiers results. Brain-computer interface (BCI) competition 2005 data setsⅣa was used to validate the framework. The results demonstrated that it effectively improved recognition and to some extent overcome the over-fitting problem of CSP. It showed the effectiveness of this framework for dealing with EEG.
Rapid and accurate identification and effective non-drug intervention are the worldwide challenges in the field of depression. Electroencephalogram (EEG) signals contain rich quantitative markers of depression, but whole-brain EEG signals acquisition process is too complicated to be applied on a large-scale population. Based on the wearable frontal lobe EEG monitoring device developed by the authors’ laboratory, this study discussed the application of wearable EEG signal in depression recognition and intervention. The technical principle of wearable EEG signals monitoring device and the commonly used wearable EEG devices were introduced. Key technologies for wearable EEG signals-based depression recognition and the existing technical limitations were reviewed and discussed. Finally, a closed-loop brain-computer music interface system for personalized depression intervention was proposed, and the technical challenges were further discussed. This review paper may contribute to the transformation of relevant theories and technologies from basic research to application, and further advance the process of depression screening and personalized intervention.
Video-electroencephalogram (VEEG) monitoring is a valuable tool for diagnosing recurrent partial epilepsy, classification of intractable epilepsy, and evaluation of epilepsy surgery. The role of video EEG in identifying and determining the type of epilepsy and determining the location of seizures has been widely demonstrated, but there is There is a lack of uniform standards for adverse events and management methods during monitoring. In order to improve the quality of long-range video EEG monitoring and reduce the possible impact on patients during monitoring, it is necessary to summarize the possible adverse reactions during monitoring.
Electroencephalogram (EEG) has been an important tool for scientists to study epilepsy and evaluate the treatment of epilepsy for half a century, since epilepsy seizures are caused by the diffusion of excessive discharge of brain neurons. This paper reviews the clinical application of scalp EEG in the treatment of intractable epilepsy with vagus nerve stimulation (VNS) in the past 30 years. It mainly introduces the prediction of the therapeutic effect of VNS on intractable epilepsy based on EEG characteristics and the effect of VNS on EEG of patients with intractable epilepsy, and expounds some therapeutic mechanisms of VNS. For predicting the efficacy of VNS based on EEG characteristics, EEG characteristics such as epileptiform discharge, polarity of slow cortical potential changes, changes of EEG symmetry level and changes of EEG power spectrum are described. In view of the influence of VNS treatment on patients’ EEG characteristics, the change of epileptiform discharge, power spectrum, synchrony, brain network and amplitude of event-related potential P300 are described. Although no representative EEG markers have been identified for clinical promotion, this review paves the way for prospective studies of larger patient populations in the future to better apply EEG to the clinical treatment of VNS, and provides ideas for predicting VNS efficacy, assessing VNS efficacy, and understanding VNS treatment mechanisms, with broad medical and scientific implications.
