Electroencephalogram and EEG

INTRODUCTION: Within the development of brain-computer interface (BCI) systems, it is crucial to consider the impact of brain network dynamics and neural signal transmission mechanisms on electroencephalogram-based motor imagery (MI-EEG) tasks. However, conventional deep learning (DL) methods cannot reflect the topological relationship among electrodes, thereby hindering the effective decoding of brain activity. METHODS: Inspired by the concept of brain neuronal forward-forward (F-F) mechanism, a novel DL framework based on Graph Neural Network combined forward-forward mechanism (F-FGCN) is presented...

BACKGROUND: Traditional therapist-based rehabilitation training for patients with movement impairment is laborious and expensive. In order to reduce the cost and improve the treatment effect of rehabilitation, many methods based on human-computer interaction (HCI) technology have been proposed, such as robot-assisted therapy and functional electrical stimulation (FES). However, due to the lack of active participation of brain, these methods have limited effects on the promotion of damaged nerve remodeling...

OBJECTIVE: Sleep-related hypermotor epilepsy (SHE) is a focal epilepsy syndrome characterized by seizures that predominantly occur during sleep. The pathogenesis of these seizures remains unclear. We previously detected rare variants in GABRG2, which encodes the γ2 subunit of γ-aminobutyric acid type A receptor (GABAA R), in patients with SHE and demonstrated that these variants impaired GABAA R function in vitro. However, the mechanisms by which GABRG2 variants contribute to seizure attacks during sleep remain unclear...

Transfer learning (TL) has demonstrated its efficacy in addressing the cross-subject domain adaptation challenges in affective brain-computer interfaces (aBCI). However, previous TL methods usually use a stationary distance, such as Euclidean distance, to quantify the distribution dissimilarity between two domains, overlooking the inherent links among similar samples, potentially leading to suboptimal feature mapping. In this study, we introduced a novel algorithm called multi-source manifold metric transfer learning (MSMMTL) to enhance the efficacy of conventional TL...

Nick is a 5-year-old boy who began displaying self-stimulating behaviors and decreased social interactions shortly before turning 3 years. At the age of 3.5 years, he was diagnosed with autism spectrum disorder by a local developmental-behavioral pediatrician. His parents recall that the physician described Nick to be "high functioning" and encouraged them to expect that he would attend college and live independently as an adult. Upon receiving the diagnosis, intervention was initiated using an applied behavioral analysis (ABA) approach...

INTRODUCTION: Night-shift workers often face various health issues stemming from circadian rhythm shift and the consequent poor sleep quality. We aimed to study nurses working night shifts, evaluate the electroencephalogram (EEG) pattern of daytime sleep, and explore possible pattern changes due to ambient light exposure (30 lux) compared to dim conditions (<5 lux) during daytime sleep. MOETHODS: The study involved 31 participants who worked night shifts and 24 healthy adults who had never worked night shifts...

BACKGROUND: Syncope is a common condition that increases the risk of injury and reduces the quality of life. Abdominal pain as a precursor to vasovagal syncope (VVS) in adults is rarely reported and is often misdiagnosed.​. METHODS: We present three adult patients with VVS and presyncopal abdominal pain diagnosed by synchronous multimodal detection (transcranial Doppler [TCD] with head-up tilt [HUT]) and discuss the relevant literature. RESULTS: Case 1: A 52-year-old man presented with recurrent decreased consciousness preceded by six months of abdominal pain...

Prompt diagnosis of epilepsy relies on accurate classification of automated electroencephalogram (EEG) signals. Several approaches have been developed to characterize epileptic EEG data; however, none of them have exploited time-frequency data to evaluate the effect of tweaking parameters in pretrained frameworks for EEG data classification. This study compares the performance of several pretrained convolutional neural networks (CNNs) namely, AlexNet, GoogLeNet, MobileNetV2, ResNet-18 and SqueezeNet for the localization of epilepsy EEG data using various time-frequency data representation algorithms...

Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting the quality of life of over 10 million individuals worldwide. Early diagnosis is crucial for timely intervention and better patient outcomes. Electroencephalogram (EEG) signals are commonly used for early PD diagnosis due to their potential in monitoring disease progression. But traditional EEG-based methods lack exploration of brain regions that provide essential information about PD, and their performance falls short for real-time applications...

Developing reliable methodologies to decode brain state information from electroencephalogram (EEG) signals is an open challenge, crucial to implementing EEG-based brain-computer interfaces (BCIs). For example, signal processing methods that identify brain states could allow motor-impaired patients to communicate via non-invasive, EEG-based BCIs. In this work, we focus on the problem of distinguishing between the states of eyes closed (EC) and eyes open (EO), employing quantities based on permutation entropy (PE)...

Sleep onset latency (SOL) is an important factor relating to the sleep quality of a subject. Therefore, accurate prediction of SOL is useful to identify individuals at risk of sleep disorders and to improve sleep quality. In this study, we estimate SOL distribution and falling asleep function using an electroencephalogram (EEG), which can measure the electric field of brain activity. We proposed a Multi Ensemble Distribution model for estimating Sleep Onset Latency (MEDi-SOL), consisting of a temporal encoder and a time distribution decoder...

Contrast-induced encephalopathy (CIE) is a rare complication of imaging using ionidated contrast media. Its pathogenesis remains unknown, and its clinical presentation is variable. We present two cases of CIE following coronary angiography (CAG) that underscore the multitude of clinical manifestations and imaging findings associated with the disorder. In patients 1, CIE manifested during the CAG with agitation and decreased consciousness, followed by left hemiparesis and visual neglect. Native computed tomography (CT) of the head was unremarkable but CT perfusion (CTP) showed extensive hypoperfusion of the right hemisphere with corresponding slow-wave activity in the electroencephalogram (EEG)...

Electroencephalogram (EEG) recorded as response to transcranial magnetic stimulation (TMS) can be highly informative of cortical reactivity and connectivity. Reliable EEG interpretation requires artifact removal as the TMS-evoked EEG can contain high-amplitude artifacts. Several methods have been proposed to uncover clean neuronal EEG responses. In practice, determining which method to select for different types of artifacts is often difficult. Here, we used a unified data cleaning framework based on beamforming to improve the algorithm selection and adaptation to the recorded signals...

Neuronal pentraxin 2 (Nptx2), a member of the synaptic protein family linked to excitatory synaptic formation, is found to be upregulated in epileptic mice, yet its role in epilepsy has been unclear. In vivo, we constructed a mouse model of epilepsy by using kainic acid induction. In vitro experiments, a Mg2+ -free medium was used to induce epileptiform discharges in neurons. The results showed that the Nptx2 was upregulated in epileptic mice. Moreover, Nptx2 knockdown reduced the number of seizures and seizure duration...

With the rapid development of new energy vehicles, the users have an increasing demand for charging piles. It is generally believed that the charging pile is a kind of practical product, and it only needs to realize the charging function. However, as a product, the shape design of the charging pile will directly affect the user experience, thus affecting product sales. Therefore, in the face of increasingly fierce market competition, when designing the shape of charging piles, it is necessary to adopt the traditional evaluation method and human physiological cognitive characteristics to evaluate the shape of charging piles more objectively...

For nearly a century, evidence has accumulated indicating that the lateral hypothalamus (LH) contains neurons essential to sustain wakefulness. While lesion or inactivation of LH neurons produces a profound increase in sleep, stimulation of inhibitory LH neurons promotes wakefulness. To date, the primary wake-promoting cells that have been identified in the LH are the hypocretin/orexin (Hcrt) neurons, yet these neurons have little impact on total sleep or wake duration across the 24-h period. Recently, we and others have identified other LH populations that increase wakefulness...

Objective. The extended infomax algorithm for independent component analysis (ICA) can separate sub- and super-Gaussian signals but converges slowly as it uses stochastic gradient optimization. In this paper, an improved extended infomax algorithm is presented that converges much faster. Approach. Accelerated convergence is achieved by replacing the natural gradient learning rule of extended infomax by a fully-multiplicative orthogonal-group based update scheme of the ICA unmixing matrix, leading to an orthogonal extended infomax algorithm (OgExtInf)...

Electroencephalography (EEG) captures minute electrical signals emanating from the brain. These signals are vulnerable to interference from external noise and dynamic artifacts; hence, accurately recording such signals is challenging. Although dry electrodes are convenient, their signals are of limited quality; consequently, wet electrodes are predominantly used in EEG. Therefore, developing dry electrodes for accurately and stably recording EEG signals is crucial. In this study, we developed flexible dry electrodes using polydimethylsiloxane (PDMS)/carbon-nanotube (CNT) composites with isotropically wrinkled surfaces that effectively combine the advantages of wet and dry electrodes...

INTRODUCTION: Epilepsy is a chronic neurological disorder characterized by abnormal electrical activity in the brain, often leading to recurrent seizures. With 50 million people worldwide affected by epilepsy, there is a pressing need for efficient and accurate methods to detect and diagnose seizures. Electroencephalogram (EEG) signals have emerged as a valuable tool in detecting epilepsy and other neurological disorders. Traditionally, the process of analyzing EEG signals for seizure detection has relied on manual inspection by experts, which is time-consuming, labor-intensive, and susceptible to human error...

Although true subarachnoid hemorrhage (SAH) is an atypical complication owing to suicide by hanging, pseudo-SAH can often develop because of hypoxic encephalopathy. Therefore, differentiating pseudo-SAH from true SAH using brain computed tomography (CT) is often challenging. In Japan, an individual's cause of brain death must be determined to be eligible for organ donation, regardless of whether true SAH is involved or not. Herein, we report a case of SAH confirmed by magnetic resonance imaging (MRI) in a patient with brain death owing to hypoxic encephalopathy following suicide by hanging...