Epilepsy is a prevalent neurological disorder characterized by recurrent, transient episodes of central nervous system dysfunction resulting from abnormal neuronal discharges in the brain. Diagnosis of epilepsy integrates clinical manifestations, electroencephalogram (EEG) findings, and imaging studies. Clinical presentations are diverse and variable, with abnormal EEG serving as a critical diagnostic indicator; however, some patients exhibit normal EEG results. Moreover, there are still many patients who were underdiagnosed because of atypical epilepsy symptoms. With advancements in EEG and multimodal imaging technologies, diagnostic strategies based on biorhythm theory have emerged. This paper reviewed the diagnostic approaches for epilepsy grounded in biorhythm theory, in order to provide more effective support for the clinical management of epilepsy.
Magnetic resonance imaging (MRI)-based electroencephalography (EEG) forward modeling method has become prevalent in the field of EEG. However, due to the inability to obtain clear images of an infant’s fontanel through MRI, the fontanelle information is often lacking in the EEG forward model, which affects accuracy of modeling in infants. To address this issue, we propose a novel method to achieve fontanel compensation for infant EEG forward modeling method. First, we employed imaging segmentation and meshing to the head MRIs, creating a fontanel-free model. Second, a projection-based surface reconstruction method was proposed, which utilized priori information on fontanel morphology and the fontanel-free head model to reconstruct the two-dimensional measured fontanel into a three-dimensional fontanel model to achieve fontanel-compensation modeling. Finally, we calculated a fontanel compensation-based EEG forward model for infants based on this model. Simulation results, based on a real head model, demonstrated that the compensation of fontanel had a potential to improve EEG forward modeling accuracy, particularly for the sources beneath the fontanel (relative difference measure larger than 0.05). Additional experimental results revealed that the uncertainty of the infant’s skull conductivity had the widest impact range on the neural sources, and the absence of fontanel had the strongest impact on the neural sources below the fontanel. Overall, the proposed fontanel-compensated method showcases the potential to improve the modeling accuracy of EEG forward problem without relying on computed tomography (CT) acquisition, which is more in line with the requirements of practical application scenarios.
ObjectiveTo explore the clinical manifestation, diagnosis, treatment and prognosis of infantile spasm complicated with craniostenosis.MethodsA case of infantile spasm complicated with craniostenosis in the Department of Neurology of Qilu Children's Hospital in December 2017 was reviewed with the literature. The clinical manifestations, diagnosis, treatment and prognosis of infantile spasm with craniostenosis were analyzed.ResultsThe proband infantile spasms and craniostenosis was diagnosed by clinical, imaging examination and VEEG. Epileptic attack was prevented and craniostenosis was corrected by hormone shock therapy (corticotrophin was administered for 14 days, followed by topiramate)and surgical treatment (cranial cap reconstruction was performed), and good clinical prognosis was obtained.ConclusionThis case was the first reported case of craniostenosis with infantile spasm in China, and compared with the foreign treatment method, better treatment method and the operation opportunity were obtained. Which has a significant effect on the clinical treatment of infantile spasm complicated with transcranial disease.
The non-invasive brain-computer interface (BCI) has gradually become a hot spot of current research, and it has been applied in many fields such as mental disorder detection and physiological monitoring. However, the electroencephalography (EEG) signals required by the non-invasive BCI can be easily contaminated by electrooculographic (EOG) artifacts, which seriously affects the analysis of EEG signals. Therefore, this paper proposed an improved independent component analysis method combined with a frequency filter, which automatically recognizes artifact components based on the correlation coefficient and kurtosis dual threshold. In this method, the frequency difference between EOG and EEG was used to remove the EOG information in the artifact component through frequency filter, so as to retain more EEG information. The experimental results on the public datasets and our laboratory data showed that the method in this paper could effectively improve the effect of EOG artifact removal and improve the loss of EEG information, which is helpful for the promotion of non-invasive BCI.
Rapid serial visual presentation (RSVP) is a type of psychological visual stimulation experimental paradigm that requires participants to identify target stimuli presented continuously in a stream of stimuli composed of numbers, letters, words, images, and so on at the same spatial location, allowing them to discern a large amount of information in a short period of time. The RSVP-based brain-computer interface (BCI) can not only be widely used in scenarios such as assistive interaction and information reading, but also has the advantages of stability and high efficiency, which has become one of the common techniques for human-machine intelligence fusion. In recent years, brain-controlled spellers, image recognition and mind games are the most popular fields of RSVP-BCI research. Therefore, aiming to provide reference and new ideas for RSVP-BCI related research, this paper reviewed the paradigm design and system performance optimization of RSVP-BCI in these three fields. It also looks ahead to its potential applications in cutting-edge fields such as entertainment, clinical medicine, and special military operations.
ObjectiveTo investigate the value of interictal scalp high-frequency oscillations (HFOs) in localizing the epileptogenic zone (EZ) in children with infantile epileptic spasms syndrome (IESS). Methods A retrospective analysis was conducted on surgical IESS patients treated at the Epilepsy Center of Shenzhen Children’s Hospital from August 2018 to November 2021. Preoperative EEG and clinical data were collected. Based on Engel classification at 2-year follow-up, patients were divided into a seizure-free group (Engel Ia) and a non–seizure-free group (non–Engel Ia). An automated detection system was used to identify ripple events (80–250 Hz), and the H-index (ripples per channel per minute) was calculated in the resection zone (RZ) and non-resection zone (non-RZ). Result 35 children were included (19 seizure-free, 16 non–seizure-free). The H-index in the RZ was significantly higher than in the non-RZ in the seizure-free group [(58.16±43.95) vs. (24.07±22.85), P<0.05]. Using the RZ of the seizure-free group as the gold standard, the area under the ROC curve (AUC) of the H-index for predicting the EZ was 0.83 [95%CI (0.70, 0.96) , P<0.001], with an optimal diagnostic threshold of 21.50 (sensitivity 73.68%, specificity 84.21%). Conclusion The results confirm that the interictal scalp HFO index may serve as a potential biomarker for localizing the EZ in IESS children with structural etiologies.
ObjectiveTo investigate the clinical electrophysiological characteristics of Cyclin-dependent kinase-like 5 gene induced developmental epileptic encephalopathy (CDKL5-DEE). MethodsThe clinical data and series of video EEGs of children with CDKL5-associated developmental epileptic encephalopathy (CDKL5-DEE) who were admitted to the Children’s Medical Center of Peking University First Hospital from June 2016 to May 2024 were retrospectively analyzed. Results A total of 16 patients with CDKL5-DEE were enrolled, including 13 females and 3 males. All patients had de novo variants of CDKL5 gene, including 6 cases of missense variants, 5 cases of frameshift variants, 4 cases of nonsense variants, and 1 case of large fragment deletion. The age of onset was 8 days (d) after birth ~1 year (y) and 10 months (m), and the median age was (85.94±95.76) days. Types of seizures at onset: 4 cases of tonic seizures [age of onset 10~52 days, median age (25.5±15.84) days]; There were 5 cases of focal seizures [age of onset 8 d~8 m, median age (77.76±85.97) d]. There were 4 cases of epileptic spasmodic seizures [age of onset 3 m~1 y 10 m, median age (6.25±3.49) m]; There were 2 cases of bilateral tonic-clonic seizures [age of onset 30~40 days, median age (35.00±5.00) days]; focal concurrent epileptic spasm seizures 1 case (age of onset 2 m). A total of 59 VEEG sessions were performed in the pediatric EEG room of Peking University First Hospital for 4 hours. All the results were abnormal, including 26 normal background, 25 slow rhythm difference with background, and 8 no background. The interictal was 16 posterior or focal discharges, 19 multifocal discharges, 17 generalized or accompanied by focal/multifocal discharges, and 7 hypsarrhythmia; The ictal was 33 epileptic seizures, 6 myoclonic seizures, 5 focal seizures, 2 tonic-clonic seizures, 2 atypical absence seizures, 2 tonic seizures, 1 myoclonic sequential focal seizure, 1 focal sequential epileptic spasm, and 1 hypermotor-tonic-spasms. The background of patients within 6 months of age was normal, and the background abnormality increased significantly with age. generalized discharges are evident after 2 years of age between seizures. Conclusion CDKL5-DEE seizures have an early onset and are refractory to medications. Epileptic spasms are the most common type of seizure in every patient and long-lasting, with generalized seizures increasing markedly with age. EEG is characterized by a normal background within 6 months. With the increase of age, the background and interictal discharges have a tendency to deteriorate.
ObjectiveNumerous foreign researches focused on the changes of EEG during the developmental periods from the newborn to late adulthood. However, the EEG changes of healthy Chinese people is still rare. Therefore, we examined the EEG of 2 357 healthy Chinese people.MethodsIn 1982, guided by Prof. Feng, we analysed the waking EEG of 2 357 healthy people, from 2 to above 60 years old, including open eyes induction test and hyperventilation.ResultsAt age 2 ~ 4, the posterior basic rhythms has reached 8 ~ 9 Hz, but the rhythms were unregular pattern. After age 7, the rhythms were 9 Hz, α index was more than 60%, the amplitude was higher than other ages. At age 12 ~ 14, the main rhythms was 10 Hz, the same as adulthood, α index was 70% ~ 80%. After this age, the amplitude of α rhythm deceased gradually. Above 60 years old, the main rhythm was 9 Hz, α index <60%, the amplitude was lower than adulthood. At age 14 ~ 16, the θ index in frontal and temporal regions was 6%, the same as the adulthood. At age 18 ~ 20, β index was 20%.ConclusionsIn the article, we analyzed the waking EEG of 2 357 healthy Chinese people in Beijing area. Although this multi-center study was accomplished at 1980s, the data is still of great value to the clinical EEG today.
Objective To investigate the network reorganization and dynamic brain activity in visuospatial neglect (VSN) patients using resting-state electroencephalography (rEEG), and to develop classification models to facilitate its identification. Methods In this retrospective study, stroke patients admitted to the Department of Rehabilitation, Xuanwu Hospital, Capital Medical University between August 2022 and December 2024 were included and divided into VSN (n=22) and non-VSN (n=21) groups based on paper-and-pencil assessments. A healthy control group (n=20) was also recruited. Microstate segmentation and graph-theoretical analysis were applied to rEEG data to extract microstate parameters and topological network features. Four machine learning models (logistic regression, na?ve Bayes, k-nearest neighbors, and decision tree) were built for classification. Results Compared with the non-VSN group, the VSN group showed significantly increased mean duration and time coverage in microstate C, and significantly decreased coverage and occurrence in microstate D. Graph-theoretical analysis revealed higher average clustering coefficients in the VSN group. Degree centrality in the frontal-central regions (C1, CZ) was significantly lower, while that in the parietal-occipital regions (P5, P3, PO7, PO5) was significantly higher than in the non-VSN group. Among the classification models, logistic regression and na?ve Bayes models performed best, with the mean duration of microstate C contributing most to classification performance. Conclusions Patients with VSN exhibit distinct alterations in electroencephalography microstate dynamics and functional network topology. Microstate parameters play a crucial role in distinguishing VSN from non-VSN stroke cases, and combining these features with machine learning offers a promising approach for early identification and personalized intervention of VSN.
Existing emotion recognition research is typically limited to static laboratory settings and has not fully handle the changes in emotional states in dynamic scenarios. To address this problem, this paper proposes a method for dynamic continuous emotion recognition based on electroencephalography (EEG) and eye movement signals. Firstly, an experimental paradigm was designed to cover six dynamic emotion transition scenarios including happy to calm, calm to happy, sad to calm, calm to sad, nervous to calm, and calm to nervous. EEG and eye movement data were collected simultaneously from 20 subjects to fill the gap in current multimodal dynamic continuous emotion datasets. In the valence-arousal two-dimensional space, emotion ratings for stimulus videos were performed every five seconds on a scale of 1 to 9, and dynamic continuous emotion labels were normalized. Subsequently, frequency band features were extracted from the preprocessed EEG and eye movement data. A cascade feature fusion approach was used to effectively combine EEG and eye movement features, generating an information-rich multimodal feature vector. This feature vector was input into four regression models including support vector regression with radial basis function kernel, decision tree, random forest, and K-nearest neighbors, to develop the dynamic continuous emotion recognition model. The results showed that the proposed method achieved the lowest mean square error for valence and arousal across the six dynamic continuous emotions. This approach can accurately recognize various emotion transitions in dynamic situations, offering higher accuracy and robustness compared to using either EEG or eye movement signals alone, making it well-suited for practical applications.
