Objective To investigate the electroencephalographic （EEG） manifestations of children with mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes （MELAS）， to explore the EEG characteristics of such patients and their correlation with neuroimaging findings， and to provide a reference for the early clinical diagnosis of MELAS. Methods A retrospective analysis was performed for the EEG and neuroimaging data of 11 children who were admitted to Department of Pediatric Neurology， The First Hospital of Jilin University， from January 2016 to December 2024 and were diagnosed with MELAS by genetic testing. Results There were varying degrees of EEG background abnormalities in the children with MELAS. Interictal epileptiform discharges predominantly involved the posterior head region. Seizures mainly manifested as focal seizures， frequently originating from the posterior head regions. Serial EEG and neuroimaging examinations showed changes in lesion sites over time， with consistent localization of lesions in more than half of the cases based on the two examinations， predominantly involving the posterior head regions. Conclusion In clinical practice， most children with MELAS have the initial manifestation of seizures， and the possibility of MELAS should be considered in case of the characteristic changes in EEG and imaging examinations， so as to provide help for early identification and diagnosis.
Electroencephalography

This article aims to improve the understanding of Lance-Adams syndrome （LAS） in clinical practice. By analyzing the clinical data of a patient diagnosed with LAS in 2021 and conducting a literature review， this article elaborates on the clinical symptoms， pathogenesis， diagnostic methods， and treatment regimens of LAS. LAS is a rare central nervous system disorder characterized by action myoclonus after cerebral hypoxia. The etiology of LAS is mainly associated with cerebral hypoxia， and cardiopulmonary resuscitation and asphyxia are the most common predisposing factors for LAS. Characteristic manifestations on electroencephalography have an important value in diagnosis. Clinicians should enhance their ability to identify LAS through typical clinical symptoms and electroencephalography in the early stage. This article also points out that there are still many challenges in the treatment of LAS， which requires further research and exploration. This article has an important reference value in improving the understanding， diagnosis， and treatment of LAS.
Electroencephalography

Electroencephalography (EEG) and magnetic resonance imaging (MRI), as neuroimaging technologies, provided objective and visualized technical tools for analyzing the brain effect mechanisms of acupuncture and moxibustion from the perspectives of brain structure, function, metabolism, and hemodynamics. The advancement of artificial intelligence (AI) algorithms can compensate for issues such as the large and scattered nature of neuroimaging data, inconsistent quality, and high heterogeneity of image information. The integration of AI with neuroimaging can facilitate individualized, intelligent, and precise prediction of acupuncture and moxibustion effects, enable intelligent classification of differential acupuncture responses, and identify brain activation patterns. This paper focuses on EEG and MRI, analyzing how machine learning and deep learning optimize multimodal neuroimaging data and their applications in the study of acupuncture and moxibustion brain effects mechanisms. Furthermore, it highlights current research gaps and limitations to provide insights for future studies on acupuncture brain effects mechanisms.
Humans ; Acupuncture Therapy ; Brain/physiology* ; Moxibustion ; Neuroimaging/methods* ; Artificial Intelligence ; Magnetic Resonance Imaging ; Electroencephalography

BACKGROUND: Visual input significantly influences cerebral activity related to locomotor navigation, although the underlying mechanism remains unclear. This study aimed to analyze the effects of chronic visual impairment and its rehabilitation on sensorimotor integration during level walking in patients with age-related cataract. METHODS: This prospective case series enrolled 14 female patients (68.4 ± 4.7 years) with age-related cataract, scheduled for consecutive cataract surgeries at the Department of Ophthalmology in Peking University Third Hospital from June 2019 to June 2020. Electroencephalogram (EEG) signals during level walking were recorded using a portable EEG system before and 4 weeks after visual restoration. Walking speed was assessed using the Footscan system. Spectral power of the theta and alpha bands was analyzed with repeated-measures analysis of variance, with Assignment (rest and walking), Phase (preoperative and postoperative), and Electrode sites (F3, Fz, F4, O1, and O2) as within-subject factors. RESULTS: Compared to the visual impairment state, theta band power significantly decreased after visual restoration (13.16 ± 1.58 μV 2vs. 23.65 ± 3.48 μV 2 , P = 0.018). Theta activity was notably reduced during walking (17.24 ± 2.43 μV 2vs. 37.86 ± 6.62 μV 2 , P = 0.017), while theta power at rest was not significantly different between the two phases (9.44 ± 1.24 μV 2vs. 9.08 ± 1.74 μV 2 , P = 0.864). Changes in walking speed were correlated with alterations in theta power at electrode sites of O1 ( r = -0.574, P = 0.032) and O2 ( r = -0.648, P = 0.012). Alpha band power remained stable during walking and was unaffected by visual status. CONCLUSIONS Chronic visual impairment from age-related cataract triggers enhanced cerebral activation of sensorimotor integration to compensate for visual decline during locomotion. This cerebral over-activation is effectively alleviated by visual restoration.
Humans ; Female ; Walking/physiology* ; Aged ; Electroencephalography/methods* ; Prospective Studies ; Middle Aged ; Cataract/physiopathology* ; Vision Disorders/physiopathology*

The hippocampus, a major component of the limbic system, is the most important region related to emotion regulation and memory processing. Cognitive impairment and depressive symptoms observed in Alzheimer's disease (AD) patients may be attributed to hippocampal damage caused by amyloid β-protein (Aβ). Our previous studies have demonstrated that a steroid sulfatase inhibitor DU-14 can enhance hippocampal synaptic plasticity and spatial memory abilities in a chronic AD murine model by counteracting the toxic effects of Aβ. However, limited experimental evidence exists regarding the efficacy of steroid sulfatase inhibitor on depressive symptoms in AD animal models. In this study, we investigated the effects of DU-14 on depressive symptoms and theta-band neuronal oscillations in rats with intrahippocampal injection of Aβ_1-42 using various behavioral tests such as sucrose preference test, tail suspension test, forced swimming test, and in vivo hippocampal local field potential (LFP) recording. The results demonstrated that, in comparison to the control group: (1) rats in the Aβ group exhibited a decrease in sucrose preference, indicating a loss of interest in pleasurable activities; (2) rats in the Aβ group displayed aggravated depressive-like behavior characterized by prolonged immobility time during tail suspension and forced swimming tests; (3) Aβ disrupted the induction of theta rhythm via tail pinch stimulation, and resulted in a significant reduction in peak power of theta rhythm. In contrast to the Aβ group, pretreatment with DU-14 resulted in: (1) a significant improvement in Aβ-induced anhedonia, as evidenced by increased sucrose preference; (2) significant alleviation of Aβ-induced despair and depressive-like behaviors, reflected by reduced immobility time during tail suspension and forced swimming tests; (3) successful mitigation of Aβ-mediated inhibition on bilateral hippocampal theta rhythm. These findings indicate that steroid sulfatase inhibitor DU-14 can counteract neurotoxicity induced by Aβ, and prevent Aβ-induced depressive-like behavior and suppression of theta rhythm.
Animals ; Amyloid beta-Peptides/toxicity* ; Rats ; Depression/physiopathology* ; Theta Rhythm/drug effects* ; Hippocampus/physiopathology* ; Male ; Rats, Sprague-Dawley ; Alzheimer Disease/physiopathology* ; Steryl-Sulfatase/antagonists & inhibitors* ; Peptide Fragments ; Behavior, Animal/drug effects*

This paper proposes a motor imagery recognition algorithm based on feature fusion and transfer adaptive boosting (TrAdaboost) to address the issue of low accuracy in motor imagery (MI) recognition across subjects, thereby increasing the reliability of MI-based brain-computer interfaces (BCI) for cross-individual use. Using the autoregressive model, power spectral density and discrete wavelet transform, time-frequency domain features of MI can be obtained, while the filter bank common spatial pattern is used to extract spatial domain features, and multi-scale dispersion entropy is employed to extract nonlinear features. The IV-2a dataset from the 4 ^th International BCI Competition was used for the binary classification task, with the pattern recognition model constructed by combining the improved TrAdaboost integrated learning algorithm with support vector machine (SVM), k nearest neighbor (KNN), and mind evolutionary algorithm-based back propagation (MEA-BP) neural network. The results show that the SVM-based TrAdaboost integrated learning algorithm has the best performance when 30% of the target domain instance data is migrated, with an average classification accuracy of 86.17%, a Kappa value of 0.723 3, and an AUC value of 0.849 8. These results suggest that the algorithm can be used to recognize MI signals across individuals, providing a new way to improve the generalization capability of BCI recognition models.
Brain-Computer Interfaces ; Humans ; Support Vector Machine ; Algorithms ; Neural Networks, Computer ; Imagination/physiology* ; Pattern Recognition, Automated/methods* ; Electroencephalography ; Wavelet Analysis

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.
Humans ; Emotions/physiology* ; Electroencephalography ; Support Vector Machine ; Electromyography ; Signal Processing, Computer-Assisted ; Galvanic Skin Response/physiology* ; Machine Learning ; Male

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.
Humans ; Electroencephalography/methods* ; Emotions/physiology* ; Eye Movements/physiology* ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Algorithms

Depression, a mental health disorder, has emerged as one of the significant challenges in the global public health domain. Investigating the pathogenesis of depression and accurately assessing the symptomatic changes are fundamental to formulating effective clinical diagnosis and treatment strategies. Utilizing non-invasive brain imaging technologies such as functional magnetic resonance imaging and scalp electroencephalography, existing studies have confirmed that the onset of depression is closely associated with abnormal neural activities and altered functional connectivity in multiple brain regions. Magnetoencephalography, unaffected by tissue conductivity and skull thickness, boasts high spatial resolution and signal-to-noise ratio, offering unique advantages and significant value in revealing the abnormal brain mechanisms and neural characteristics of depression. This review, starting from the rhythmic characteristics, nonlinear dynamic features, and connectivity characteristics of magnetoencephalography in depression patients, revisits the research progress on magnetoencephalography features related to depression, discusses current issues and future development trends, and provides insights for the study of pathophysiological mechanisms, as well as for clinical diagnosis and treatment of depression.
Humans ; Magnetoencephalography/methods* ; Brain/physiopathology* ; Depression/diagnosis* ; Electroencephalography ; Magnetic Resonance Imaging

Motor imagery (MI) is a mental process that can be recognized by electroencephalography (EEG) without actual movement. It has significant research value and application potential in the field of brain-computer interface (BCI) technology. To address the challenges posed by the non-stationary nature and low signal-to-noise ratio of MI-EEG signals, this study proposed a Riemannian spatial filtering and domain adaptation (RSFDA) method for improving the accuracy and efficiency of cross-session MI-BCI classification tasks. The approach addressed the issue of inconsistent data distribution between source and target domains through a multi-module collaborative framework, which enhanced the generalization capability of cross-session MI-EEG classification models. Comparative experiments were conducted on three public datasets to evaluate RSFDA against eight existing methods in terms of classification accuracy and computational efficiency. The experimental results demonstrated that RSFDA achieved an average classification accuracy of 79.37%, outperforming the state-of-the-art deep learning method Tensor-CSPNet (76.46%) by 2.91% ( P < 0.01). Furthermore, the proposed method showed significantly lower computational costs, requiring only approximately 3 minutes of average training time compared to Tensor-CSPNet's 25 minutes, representing a reduction of 22 minutes. These findings indicate that the RSFDA method demonstrates superior performance in cross-session MI-EEG classification tasks by effectively balancing accuracy and efficiency. However, its applicability in complex transfer learning scenarios remains to be further investigated.
Electroencephalography/methods* ; Brain-Computer Interfaces ; Humans ; Imagination/physiology* ; Signal Processing, Computer-Assisted ; Movement/physiology* ; Signal-To-Noise Ratio ; Deep Learning ; Algorithms