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.
ObjectiveThe purpose of the research is to study the distribution and early warning of electroencephalogram (EEG) in acute mountain sickness (AMS). MethodsA total of 280 healthy young men were recruited from September 2016 to October 2016. The basic data were collected by the centralized flow method, the general situation of the division of the investigators after the training, the Lewis Lake score, the computer self-rating anxiety scale and depression scale, and the collection of EEG. Follow up in three months. Results94 of the patients with AMS, morbidity is 33%, 21 (22.34%) of the patients are moderate to severe, 73 (77.66%) are mild, morbidity is 26.67%. The abnormal detection rate of electrogram was 7.9% (22/280), which were mild EEG, normal EEG abnormal rate was 8.6% (16/186), abnormal detection rate of mild AMS was 4.1% (3/73), and the abnormal detection rate was 14.3% (3/21) in the medium / heavy AMS. The latter was significantly different from the previous (P < 0.05). Three months follow-up of this group of patients with 0 case of high altitude disease. Conclusions The EEG in AMS is mainly a rhythm irregular, unstable, poor amplitude modulation; or two hemisphere volatility difference of more than 50% or slightly increased activity. The result is statistically significant, suggesting that EEG distributions has possible early warning of AMS.
Due to the high complexity and subject variability of motor imagery electroencephalogram, its decoding is limited by the inadequate accuracy of traditional recognition models. To resolve this problem, a recognition model for motor imagery electroencephalogram based on flicker noise spectrum (FNS) and weighted filter bank common spatial pattern (wFBCSP) was proposed. First, the FNS method was used to analyze the motor imagery electroencephalogram. Using the second derivative moment as structure function, the ensued precursor time series were generated by using a sliding window strategy, so that hidden dynamic information of transition phase could be captured. Then, based on the characteristic of signal frequency band, the feature of the transition phase precursor time series and reaction phase series were extracted by wFBCSP, generating features representing relevant transition and reaction phase. To make the selected features adapt to subject variability and realize better generalization, algorithm of minimum redundancy maximum relevance was further used to select features. Finally, support vector machine as the classifier was used for the classification. In the motor imagery electroencephalogram recognition, the method proposed in this study yielded an average accuracy of 86.34%, which is higher than the comparison methods. Thus, our proposed method provides a new idea for decoding motor imagery electroencephalogram.
ObjectiveTo make the model of Wistar suckling rats Focal cortical dysplasia (FCD) by liquid nitrogen freezing brain cortex and verify it. Analysed the electroencephalogram (EEG) and magnetic resonance imaging (MRI) features of the FCD model, in order to provide theoretical and experimental basis for human FCD diagnosis and treatment. MethodsTake the first day of Wistar suckling rats as experimental object, liquid nitrogen freezing Wistar suckling rats brain cortex.Make examination of EEG and MRI for Wistar suckling rats. The Brain tissue slice of Wistar suckling rats model dyed by HE and check with light microscope examination. ResultsIn experiment group, the sample epileptic discharge rate of EEG was about 41.6% on average, and showed visible spike wave, spine slow wave frequency distribution. Experimental Wistar suckling rats MRI showed positive performance for long T1 and long T2 signal, brain tissue slices HE staining showed brain cortex layer structure and columnar structure disorder, exist abnormal neurons and the balloon sample cells. ConclusionThe method of liquid nitrogen freezing Wistar suckling rats cortex can established FCDⅢd animal models successfully, and showed specific EEG and MRI, which has important value for diagnosis and treatment of human FCD.
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.
Emotion classification and recognition is a crucial area in emotional computing. Physiological signals, such as electroencephalogram (EEG), provide an accurate reflection of emotions and are difficult to disguise. However, emotion recognition still faces challenges in single-modal signal feature extraction and multi-modal signal integration. This study collected EEG, electromyogram (EMG), and electrodermal activity (EDA) signals from participants under three emotional states: happiness, sadness, and fear. A feature-weighted fusion method was applied for integrating the signals, and both support vector machine (SVM) and extreme learning machine (ELM) were used for classification. The results showed that the classification accuracy was highest when the fusion weights were set to EEG 0.7, EMG 0.15, and EDA 0.15, achieving accuracy rates of 80.19% and 82.48% for SVM and ELM, respectively. These rates represented an improvement of 5.81% and 2.95% compared to using EEG alone. This study offers methodological support for emotion classification and recognition using multi-modal physiological signals.
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.
ObjectiveTo investigate the clinical features and changes of EEG in children with late onset epilepsy spasm. MethodsThe clinical data, treatment, follow-up and outcome of 13 patients with late-onset epilepsy spasms were analyzed retrospectively from June 2010 to August 2015 in Bo ai Hospital of Zhong Shan City.Affiliated Southern Medical University ResultsThirteen cases of children were enrolled in the group, including 9 males and 4 females, the onset of age were 1 year 3 months to 5 years 7 months, duration of treatment were 1 year 5 months to 4 years 8months.Seven cases of children had clear cause in 13 patients: 2 cases of viral encephalitis, 3 cases of HIE, 1 case of neonatal sepsis, ARDS, and 1 case of methylmalonic acid hyperchomocysteinemia.Six cases did not clear the cause.Spasm is still the main type of Seizures.Seven cases had seizures with partial origin.the most onset time were awake period and wake up for the time, and coexisted with other types of seizures.EEG in Epileptic seizures period was a broad range of high amplitude slow wave, slow bursts, complex or non-composite low amplitude fast wave, sometimes with the burst after the voltage attenuation of a few seconds, string or isolation occurs.Synchronous bilateral deltoid EMG monitoring showed bilateral or unilateral synchronous EMG 1 ~ 2s Bilateral or unilateral synchronous EMG outbreak1-2s.Intermittent EEG showed multifocal and extensive epileptic discharge, still sharp (spine) slow wave continuous release based.Treatment: All children underwent ACTH or methylprednisolone immunoregulation treatment, 3 cases underwent ketone diet therapy.At the same time choice valproic acid, topiramate, clonazepam, lamotrigine, levarabesilan and other anti-broad-spectrum antiepileptic drugs, according to the history.all children were taken in combination with the way.Prognosis: 13 patients'seizures reduced or controled after the end of the ACTH or methylprednisolone immunotherapy course.followed-up 3 to 12 months, the clinical attack control were failed 3 cases had relatively good prognosis, treated with Ketogenic diet (Lasted for 1 year 3 mothes~2 years 5 mothes), one case of attack control, mental improvement significantly, Another 2 cases, the numbers of episodes were reduced and the level of intelligence were significantly improved. ConclusionPerinatal factors and acquired brain injury are the most common cause of pathogenesis.Spasm as a major form of attack, and other forms of coexistence.EEG is not typical of high degree of performance.Simultaneous EMG monitoring shows bilateral or unilateral synchronous EMG outbreaks.The treatment of various antiepileptic drugs were ineffective.The vast majority of patients developed refractory epilepsy.Ketogenic diet treatment may be a relatively good choice.
Emotion is a crucial physiological attribute in humans, and emotion recognition technology can significantly assist individuals in self-awareness. Addressing the challenge of significant differences in electroencephalogram (EEG) signals among different subjects, we introduce a novel mechanism in the traditional whale optimization algorithm (WOA) to expedite the optimization and convergence of the algorithm. Furthermore, the improved whale optimization algorithm (IWOA) was applied to search for the optimal training solution in the extreme learning machine (ELM) model, encompassing the best feature set, training parameters, and EEG channels. By testing 24 common EEG emotion features, we concluded that optimal EEG emotion features exhibited a certain level of specificity while also demonstrating some commonality among subjects. The proposed method achieved an average recognition accuracy of 92.19% in EEG emotion recognition, significantly reducing the manual tuning workload and offering higher accuracy with shorter training times compared to the control method. It outperformed existing methods, providing a superior performance and introducing a novel perspective for decoding EEG signals, thereby contributing to the field of emotion research from EEG signal.
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.