Gelastic seizure (GS) is a type of epilepsy characterized primarily by inappropriate bursts of laughter, with or without other epileptic events. Based on the timing of symptoms, the presence of emotional changes, and disturbances of consciousness, GS is classified into simple and complex types. The generation of laughter involves two major neural pathways: the emotional pathway and the volitional pathway. The neural network involved in GS includes structures such as the frontal lobe, insula, cingulate gyrus, temporal lobe, and brainstem.The most common cause of GS is a hypothalamic hamartoma, and stereotactic electroencephalography can record discharges from the lesion itself. Surgical removal of the hypothalamic hamartoma can result in immediate cessation of GS in the majority of patients, while some may experience partial improvement with persistent epileptic-like discharges detectable on scalp electroencephalography (EEG). Early surgical intervention may improve prognosis.In cases of non-hypothalamic origin of GS with no apparent imaging abnormalities, focal discharges are often observed on EEG and these cases respond well to antiepileptic drugs. Conversely, patients with structural abnormalities suggested by imaging studies tend to have multifocal discharges and a poorer response to medication. In a small subset of medically refractory non-hypothalamic GS, surgical intervention can effectively control symptoms.This article provides a comprehensive review of the etiology, neural networks involved, EEG characteristics, and treatment options for GS, with the goal of improving understanding of this relatively rare type of epileptic seizure.
In recent years, epileptic seizure detection based on electroencephalogram (EEG) has attracted the widespread attention of the academic. However, it is difficult to collect data from epileptic seizure, and it is easy to cause over fitting phenomenon under the condition of few training data. In order to solve this problem, this paper took the CHB-MIT epilepsy EEG dataset from Boston Children's Hospital as the research object, and applied wavelet transform for data augmentation by setting different wavelet transform scale factors. In addition, by combining deep learning, ensemble learning, transfer learning and other methods, an epilepsy detection method with high accuracy for specific epilepsy patients was proposed under the condition of insufficient learning samples. In test, the wavelet transform scale factors 2, 4 and 8 were set for experimental comparison and verification. When the wavelet scale factor was 8, the average accuracy, average sensitivity and average specificity was 95.47%, 93.89% and 96.48%, respectively. Through comparative experiments with recent relevant literatures, the advantages of the proposed method were verified. Our results might provide reference for the clinical application of epilepsy detection.
ObjectiveTo investigate the feasibility of electroencephalography (EEG) power spectrum analysis monitoring noninvasive intracranial pressure (ICP). MethodsBetween September 2008 and May 2009, the EEG signals were recorded in 62 patients (70 cases/times) with central nervous system (CNS). By using self-designed software, EEG power spectrum analysis was conducted and pressure index (PI) was calculated automatically. ICP was measured by lumbar puncture (LP). ResultsThe mean ICP was (239.74±116.25) mm H2O (70-500 mm H2O, 1 mm H2O=0.009 8 kPa), and 52.9% of patients had increased ICP. The mean PI was 0.29±0.20 (0.02-0.85). The Spearman rank test showed that there was a significant negative correlation between PI and ICP (rs=-0.849, P<0.01). The data from the patients with diffuse lesions of CNS and focal lesions were analyzed separately; the results showed there were significant negative correlations between PI and ICP in both groups (rs=-0.815, -0.912; P<0.01). ConclusionThe PI obtained from EEG analysis is correlated with ICP. Analysis of specific parameters from EEG power spectrum might reflect the ICP. Further research should be carried out.
Brain age prediction, as a significant approach for assessing brain health and early diagnosing neurodegenerative diseases, has garnered widespread attention in recent years. Electroencephalogram (EEG), an non-invasive, convenient, and cost-effective neurophysiological signal, offers unique advantages for brain age prediction due to its high temporal resolution and strong correlation with brain functional states. Despite substantial progress in enhancing prediction accuracy and generalizability, challenges remain in data quality and model interpretability. This review comprehensively examined the advancements in EEG-based brain age prediction, detailing key aspects of data preprocessing, feature extraction, model construction, and result evaluation. It also summarized the current applications of machine learning and deep learning methods in this field, analyzed existing issues, and explored future directions to promote the widespread application of EEG-based brain age prediction in both clinical and research settings.
There are two modes to display panoramic movies in virtual reality (VR) environment: non-stereoscopic mode (2D) and stereoscopic mode (3D). It has not been fully studied whether there are differences in the activation effect between these two continuous display modes on emotional arousal and what characteristics of the related neural activity are. In this paper, we designed a cognitive psychology experiment in order to compare the effects of VR-2D and VR-3D on emotional arousal by analyzing synchronously collected scalp electroencephalogram signals. We used support vector machine (SVM) to verify the neurophysiological differences between the two modes in VR environment. The results showed that compared with VR-2D films, VR-3D films evoked significantly higher electroencephalogram (EEG) power (mainly reflected in α and β activities). The significantly improved β wave power in VR-3D mode showed that 3D vision brought more intense cortical activity, which might lead to higher arousal. At the same time, the more intense α activity in the occipital region of the brain also suggested that VR-3D films might cause higher visual fatigue. By the means of neurocinematics, this paper demonstrates that EEG activity can well reflect the effects of different vision modes on the characteristics of the viewers’ neural activities. The current study provides theoretical support not only for the future exploration of the image language under the VR perspective, but for future VR film shooting methods and human emotion research.
Objective To understand the status quo of medical staffs engaged in epilepsy and EEG in Shanxi Province, analyze the existing problems, and summarize the improvement and development direction of epilepsy and EEG in Shanxi Province. Methods A questionnaire survey was conducted among medical staff of epilepsy and electroencephalogram specialty in public hospitals at or above county level in whole province and municipalities. Results ① Generally speaking, there are 17 males and 473 females in this study, with an average age of 38.7 years, the youngest was 23 years-old and the oldest was 70 years-old; ② The regional distribution has a tendency of decrease from Taiyuan in Shanxi Province to the remote areas of southeast, northwest and northwest China, and the epilepsy treatment in some poverty-stricken areas have not even been carried out; ③ The shortest time of working is 3 months and the longest is more than 40 years. The proportion of junior collage students, undergraduates, masters and doctors is 24%, 50%, 25% and 1% respectivel. The professional titles of primary, medium-level, vice-senior and senior are 24%, 39%, 26% and 11% respectively. Conclusion The number of medical workers engaged in EEG specialty in Shanxi Province is insufficient, the regional development is not balanced, and the number of junior and medium-level professional titles is large. We can formulate a mobile policy to encourage experienced medical personnel to communicate with weak areas, so as to improve the overall level of epilepsy and EEG professional development in Shanxi Province.
Attention level evaluation refers to the evaluation of people's attention level through observation or experimental testing, and its research results have great application value in education and teaching, intelligent driving, medical health and other fields. With its objective reliability and security, electroencephalogram signals have become one of the most important technical means to analyze and express attention level. At present, there is little review literature that comprehensively summarize the application of electroencephalogram signals in the field of attention evaluation. To this end, this paper first summarizes the research progress on attention evaluation; then the important methods for electroencephalogram attention evaluation are analyzed, including data preprocessing, feature extraction and selection, attention evaluation methods, etc.; finally, the shortcomings of the current development in the field of electroencephalogram attention evaluation are discussed, and the future development trend is prospected, to provide research references for researchers in related fields.
ObjectiveTo analyze and summarize the clinical and video EEG (VEEG) characteristics of tuberous sclerosis (TSC) with epilepsy.MethodsClinical data of 30 children with TSC who met the revised diagnostic criteria of TSC in 2012 from Jan. 2016 to May 2019 in Zhengzhou Children’s Hospital were collected, including 29 children with epileptic seizures. The characteristics of skin lesions, imaging, seizures and long-term VEEG were analyzed retrospectively.ResultsThe mean age was (2.88 ± 2.64), 12 males and 18 females, 1 case of lumbar acid as the first symptom, 29 cases with epilepsy as the first symptom, the incidence of epilepsy is high, and the onset age is less than 1 year old; TSC can cause different degrees of cognitive impact; depigmentation or milk coffee spots are the most common skin changes in young children; TSC with infantile spasm has a high incidence; children younger than 10 years old may have lesions of other organs except nervous system lesions. However, the incidence of other organ lesions was relatively low. Most of TSC children with epilepsy were accompanied by abnormal EEG discharge.ConclusionThe clinical characteristics of TSC with epileptic seizures are various, and early diagnosis is of great significance.
The deep learning-based automatic detection of epilepsy electroencephalogram (EEG), which can avoid the artificial influence, has attracted much attention, and its effectiveness mainly depends on the deep neural network model. In this paper, an attention-based multi-scale residual network (AMSRN) was proposed in consideration of the multiscale, spatio-temporal characteristics of epilepsy EEG and the information flow among channels, and it was combined with multiscale principal component analysis (MSPCA) to realize the automatic epilepsy detection. Firstly, MSPCA was used for noise reduction and feature enhancement of original epilepsy EEG. Then, we designed the structure and parameters of AMSRN. Among them, the attention module (AM), multiscale convolutional module (MCM), spatio-temporal feature extraction module (STFEM) and classification module (CM) were applied successively to signal reexpression with attention weighted mechanism as well as extraction, fusion and classification for multiscale and spatio-temporal features. Based on the Children’s Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) public dataset, the AMSRN model achieved good results in sensitivity (98.56%), F1 score (98.35%), accuracy (98.41%) and precision (98.43%). The results show that AMSRN can make good use of brain network information flow caused by seizures to enhance the difference among channels, and effectively capture the multiscale and spatio-temporal features of EEG to improve the performance of epilepsy detection.
Individuals with motor dysfunction caused by damage to the central nervous system are unable to transmit voluntary movement commands to their muscles, resulting in a reduced ability to control their limbs. However, traditional rehabilitation methods have problems such as long treatment cycles and high labor costs. Functional electrical stimulation (FES) based on brain-computer interface (BCI) connects the patient’s intentions with muscle contraction, and helps to promote the reconstruction of nerve function by recognizing nerve signals and stimulating the moving muscle group with electrical impulses to produce muscle convulsions or limb movements. It is an effective treatment for sequelae of neurological diseases such as stroke and spinal cord injury. This article reviewed the current research status of BCI-based FES from three aspects: BCI paradigms, FES parameters and rehabilitation efficacy, and looked forward to the future development trend of this technology, in order to improve the understanding of BCI-based FES.