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

In recent years, cardiovascular disease has become a common disease. With the development of machine learning and big data technologies, the processing ability of electrocardiogram (ECG) signals has been greatly enhanced through new computer technologies, enabling the auxiliary diagnosis technology for cardiovascular disease (CVD) to achieve new improvements. This article discusses the application of machine learning in ECG processing, especially in the auxiliary diagnosis of diseases. Firstly, the conventional signal preprocessing methods are introduced, and then the EEG signal processing methods based on feature extraction and fuzzy classification are explored. Secondly, the application of auxiliary diagnosis in CVD is further summarized. Finally, the advantages and disadvantages of the two methods are analyzed, and based on this, a design of an auxiliary diagnostic system compatible with the two methods is proposed, providing a new perspective for similar applied researches in the future.
Machine Learning ; Cardiovascular Diseases/diagnosis* ; Humans ; Electrocardiography ; Signal Processing, Computer-Assisted ; Diagnosis, Computer-Assisted ; Fuzzy Logic ; Electroencephalography

Brain-computer interface (BCI) technology is an innovative and cutting-edge medical advancement that enables direct interaction between the brain and external devices, facilitating the reconstruction of daily functions for patients or serving as a method for neuro-regulation therapy. Although this technology offers a broad range of clinical applications, there are problems as potential risks, individual variations, and the need for long-term monitoring of its effects during utilization. Consequently, the comprehensive evaluation of its safety and effectiveness poses a considerable challenge for regulatory agencies. This study provides a concise introduction to the development history and various types of BCI technology, followed by a summary of the regulatory situation for different types of BCI medical devices in the United States. Furthermore, the regulatory requirements imposed by the US FDA on this product category are analyzed. Finally, the article concludes by presenting a summary and future perspective on the current development of BCI technology, with the aim of offering beneficial insights and guidance for the regulation of BCI medical devices.
Brain-Computer Interfaces ; United States ; United States Food and Drug Administration ; Humans ; Electroencephalography

In order to improve the effect of transcranial electrical stimulation treatment and realize personalized treatment for patients with varying severity levels, this paper designed an integrated four-channel EEG recording multimodal transcranial electrical stimulation system. This system can conduct real-time monitoring on EEG and related characteristic analysis before stimulation, in stimulation, and after stimulation. This enables physicians and researchers to resolve real-time brain states, evaluate transcranial electrical stimulation effect, and then artificially adjust the stimulation parameters. After relevant testing and verification, the system can select four stimulation modes: TACS, TDCS, TPCS and TRNS, which can output the constant stimulation current of 0.03 mA accuracy in the range of ±2 mA and the stimulation frequency of low frequency of 0~4 kHz (precision of 0.01 Hz) and high frequency 50~100 kHz, which can obtain more accurate EEG signals under stimulation interference, demonstrating a good market application prospect.
Electroencephalography/methods* ; Transcranial Direct Current Stimulation/instrumentation* ; Humans ; Equipment Design

Neural oscillatory changes play a critical role in pain and analgesia research. Previous studies on pain-related neural oscillations have primarily utilized electroencephalogram (EEG) power spectral analysis, revealing a strong correlation between alpha ( α) power and subjective pain perception. However, alpha power may be influenced by the baseline of the power spectrum, making it difficult to accurately capture the true changes in alpha oscillations. This study employed power spectral analysis and further applied a power spectral parameterization method, which decomposed the power spectrum into periodic and aperiodic components, to compare EEG α power in 50 primiparous women who underwent severe pain during the first stage of labor before and after epidural analgesia. The results indicated no significant differences in α power between pre- and post-analgesia conditions. However, following power spectral parameterization, the aperiodic component of the EEG significantly decreased after analgesia, whereas the periodic component of α power showed a significant increase. This study not only validates the effectiveness and validity of the power spectral parameterization method in analgesia research but also uncovers the differential regulatory mechanism by which analgesia modulates the periodic and aperiodic components of α oscillations.
Humans ; Electroencephalography/methods* ; Female ; Adult ; Alpha Rhythm ; Pregnancy ; Young Adult ; Analgesia, Epidural

During long-duration manned space missions, the complex and extreme space environment exerts significant impacts on astronauts' physiological, psychological, and cognitive functions, thereby posing direct risks to mission safety and operational efficiency. As a key bridge between the brain and external devices, brain-computer interface (BCI) technology enables precise acquisition and interpretation of neural signals, offering a novel paradigm for human-machine collaboration in manned spaceflight. Non-invasive BCI technology shows broad application prospects across astronaut selection, mission training, in-orbit task execution, and post-mission rehabilitation. During mission preparation, multimodal signal assessment and neurofeedback training based on BCI can effectively enhance cognitive performance and psychological resilience. During mission execution, BCI can provide real-time monitoring of physiological and psychological states and enable intention-based device control, thereby improving operational efficiency and safety. In the post-mission rehabilitation phase, non-invasive BCI combined with neuromodulation may improve emotional and cognitive functions, support motor and cognitive recovery, and contribute to long-term health management. However, the application of BCI in space still faces challenges, including insufficient signal robustness, limited system adaptability, and suboptimal data processing efficiency. Looking forward, integrating multimodal physiological sensors with deep learning algorithms to achieve accurate monitoring and individualized intervention, and combining BCI with virtual reality and robotics to develop intelligent human-machine collaboration models, will provide more efficient support for space missions.
Brain-Computer Interfaces ; Humans ; Space Flight ; Astronauts/psychology* ; Neurofeedback ; Cognition ; Electroencephalography ; Man-Machine Systems

Abuse of amphetamine-based stimulants is a primary public health concern. Recent studies have underscored a troubling escalation in the inappropriate use of prescription amphetamine-based stimulants. However, the neurophysiological mechanisms underlying the impact of acute methamphetamine exposure (AME) on sleep homeostasis remain to be explored. This study employed non-human primates and electroencephalogram (EEG) sleep staging to evaluate the influence of AME on neural oscillations. The primary focus was on alterations in spindles, delta oscillations, and slow oscillations (SOs) and their interactions as conduits through which AME influences sleep stability. AME predominantly diminishes sleep-spindle waves in the non-rapid eye movement 2 (NREM2) stage, and impacts SOs and delta waves differentially. Furthermore, the competitive relationships between SO/delta waves nesting with sleep spindles were selectively strengthened by methamphetamine. Complexity analysis also revealed that the SO-nested spindles had lost their ability to maintain sleep depth and stability. In summary, this finding could be one of the intrinsic electrophysiological mechanisms by which AME disrupted sleep homeostasis.
Animals ; Methamphetamine ; Electroencephalography ; Male ; Sleep/drug effects* ; Central Nervous System Stimulants ; Delta Rhythm/drug effects* ; Sleep Stages/drug effects*