Objective To investigate the differences in emotional processing characteristics and electroencephalography （EEG） power values in patients with Parkinson disease （PD）. Methods A total of 24 PD patients were enrolled as subjects， and 30 healthy individuals were enrolled as control group. With the use of the EPIE experimental paradigm， SAM questionnaire was used to determine the scores of emotional valence and arousal， and EEG was used for real-time monitoring of cortical EEG signals. The two groups were compared in terms of the differences in valence/arousal and EEG power values under different emotions and their correlation. Results The PD group had significantly higher BAI and BDI scores than the control group[BAI（16.92±3.83）vs（11.62±3.65），t=4.521，P<0.05；BDI（22.69±2.30）vs（14.17±4.06），t=7.981，P<0.05]. In the negative mood， there were significant differences in valence/arousal between the two groups （t=4.505，-7.705，bothP<0.05）. There were significant differences between the two groups in power values at Fp1，Fp2，F7，F3，F4，T3，T4， and T5（t=-4.12，-12.43，5.76，-2.90，-4.72，-5.34，-5.81，-2.65，all P<0.05）. In the negative mood， for the control group， valence score was correlated with Fp1 （r=-0.837， P<0.01）， Fp2 （r=-0.920， P<0.01），F4（r=-0.604，P=0.008），P3（r=-0.658，P=0.003），and P4（r=-0.546，P=0.019）， and arousal score was correlated with Fp1（r=0.887， P<0.01）， Fp2 （r=0.958， P=0.003），F4（r=0.683，P=0.003），P3 （r=0.721， P=0.003），and P4 （r=0.610，P=0.007）； for the PD group， valence score was correlated with Fp2（r=-0.490，P=0.015） and F7（r=-0.564，P=0.004）， and arousal score was correlated with Fp2 （r=0.440， P=0.031） and F7（r=0.853，P<0.01）. Conclusion Patients with PD have negative emotional processing abnormalities associated with right PFC and left lateral FL.
Electroencephalography

Valacyclovir-associated neurotoxicity (VAN) is a recognized adverse effect in elderly patients with renal impairment but remains underdiagnosed due to its nonspecific presentation and overlap with acute neurologic emergencies. We report a 78-year-old Filipino female with end-stage renal disease on maintenance hemodialysis who developed acute disorientation, agitation, vivid visual hallucinations and generalized weakness shortly after initiation of valacyclovir for herpes zoster. Given the abrupt onset of neuropsychiatric symptoms, viral encephalitis was initially considered. Magnetic resonance imaging of the brain showed no evidence of acute infarction or encephalitis, while electroencephalography demonstrated diffuse generalized slowing consistent with an encephalopathic process. Review of the medication history revealed valacyclovir dosing that exceeded recommendations for patients with end-stage renal disease. Valacyclovir was discontinued and emergent hemodialysis was initiated resulting in marked improvement in sensorium after the second session and complete resolution of symptoms after the third. This case shows VAN as an important diagnostic mimic of acute encephalopathy in elderly patients with renal failure and emphasizes the critical role of early medication review in preventing unnecessary investigations and enabling prompt, reversible management.

Sepsis-associated encephalopathy (SAE) is a common complication of sepsis, referring to a diffuse brain dysfunction caused by sepsis in the absence of direct central nervous system (CNS) infection. SAE occurs in up to 70% of patients with sepsis. Globally, the annual incidence of sepsis ranges from 30.0 to 48.9 million cases, resulting in approximately 11 million deaths per year, which accounts for 20% of all global mortalities. SAE is identified as an independent risk factor contributing to the increased mortality rate among these patients. Early diagnosis of SAE and related cerebral protection interventions hold significant clinical importance. Currently, the main indicators of brain function for sepsis patients include Glasgow coma score (GCS), confusion assessment method for the intensive care unit (CAM-ICU), electroencephalogram (EEG), brain CT or magnetic resonance imaging (MRI) and other related imaging changes, which have the problems of low sensitivity, poor specificity, and non-objective evaluation of the results of the diagnosis of SAE. This article focuses on the latest progress in the pathogenesis of SAE and systematically reviews potential biomarkers related to the onset of SAE from multiple aspects, including inflammatory markers, endothelial and neuronal injury markers, and metabolic markers. This will provide new insights for the clinical diagnosis and treatment of SAE.
Humans ; Sepsis-Associated Encephalopathy/therapy* ; Biomarkers ; Sepsis/complications* ; Magnetic Resonance Imaging ; Electroencephalography ; Brain Diseases/etiology*

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

Ultrasound has emerged as a non-invasive neural modulation technique. Its mechanisms of action in the brain involve mechanical, cavitation, and thermal effects, which modulate neural activity by activating mechanosensitive ion channels, enhancing cell permeability, and improving blood circulation. The ultrasound-piezo-electric systems, based on the coupling between ultrasound and piezoelectric materials, can generate wireless electrical stimulation to promote neural repair, significantly improving therapeutic outcomes for neurodegenerative diseases and showing potential as a replacement for traditional invasive deep brain stimulation techniques. The ultrasound-responsive piezoelectric drug delivery system combines mechano-electrical conversion capability of piezoelectric materials with the non-invasive penetration advantage of ultrasound. This system achieves synergistic therapeutic effects for neurodegenerative diseases through on-demand drug release and wireless electrical stimulation in deep brain regions. It can effectively overcome the blood-brain barrier limitation, enabling precisely targeted drug delivery to specific brain regions. Simultaneously, it generates electrical stimulation in deep brain areas to exert synergistic neuroreparative effects. Together, these capabilities provide a more precise, efficient, and safe solution for treating neurodegenerative diseases. This review summarizes the neural regulatory mechanisms, technical advantages, and research progress of the ultrasound-responsive piezoelectric drug delivery systems for neurodegenerative disease therapy, aiming to offer novel insights for the field.
Humans ; Neurodegenerative Diseases/drug therapy* ; Drug Delivery Systems/methods* ; Blood-Brain Barrier ; Ultrasonic Waves ; Brain ; Ultrasonic Therapy ; Deep Brain Stimulation/methods*

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