Search Results (479)

Stroke is a significant global health concern characterized by the abrupt disruption of cerebral blood flow, leading to neurological impairment. Accurate and timely diagnosis—enabled by imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI)—is essential for differentiating stroke types and initiating interventions like thrombolysis, thrombectomy, or surgical management. In parallel, recent advancements in wearable technology, particularly smart clothing, offer new opportunities for stroke prevention, real-time monitoring, and rehabilitation. These garments integrate various sensors, including electrocardiogram (ECG) electrodes, electroencephalography (EEG) caps, electromyography (EMG) sensors, and motion or pressure sensors, to continuously track physiological and functional parameters. For example, ECG shirts monitor cardiac rhythm to detect atrial fibrillation, smart socks assess gait asymmetry for early mobility decline, and EEG caps provide data on neurocognitive recovery during rehabilitation. These technologies support personalized care across the stroke continuum, from early risk detection and acute event monitoring to long-term recovery. Integration with AI-driven analytics further enhances diagnostic accuracy and therapy optimization. This narrative review explores the application of smart clothing in conjunction with traditional imaging to improve stroke management and patient outcomes through a more proactive, connected, and patient-centered approach. Full article

Emotion analysis based on electroencephalogram (EEG) sensors is pivotal for human–machine interaction yet faces key challenges in spatio-temporal feature fusion and cross-band and brain-region integration from multi-channel sensor-derived signals. This paper proposes MB-MSTFNet, a novel framework for EEG emotion recognition. The model constructs a 3D tensor to encode band–space–time correlations of sensor data, explicitly modeling frequency-domain dynamics and spatial distributions of EEG sensors across brain regions. A multi-scale CNN-Inception module extracts hierarchical spatial features via diverse convolutional kernels and pooling operations, capturing localized sensor activations and global brain network interactions. Bi-directional GRUs (BiGRUs) model temporal dependencies in sensor time-series, adept at capturing long-range dynamic patterns. Multi-head self-attention highlights critical time windows and brain regions by assigning adaptive weights to relevant sensor channels, suppressing noise from non-contributory electrodes. Experiments on the DEAP dataset, containing multi-channel EEG sensor recordings, show that MB-MSTFNet achieves 96.80 ± 0.92% valence accuracy, 98.02 ± 0.76% arousal accuracy for binary classification tasks, and 92.85 ± 1.45% accuracy for four-class classification. Ablation studies validate that feature fusion, bidirectional temporal modeling, and multi-scale mechanisms significantly enhance performance by improving feature complementarity. This sensor-driven framework advances affective computing by integrating spatio-temporal dynamics and multi-band interactions of EEG sensor signals, enabling efficient real-time emotion recognition. Full article

Background/Objectives: Fear detection through EEG signals has gained increasing attention due to its applications in affective computing, mental health monitoring, and intelligent safety systems. This systematic review aimed to identify the most effective methods, algorithms, and configurations reported in the literature for detecting fear from EEG signals using artificial intelligence (AI). Methods: Following the PRISMA 2020 methodology, a structured search was conducted using the string (“fear detection” AND “artificial intelligence” OR “machine learning” AND NOT “fnirs OR mri OR ct OR pet OR image”). After applying inclusion and exclusion criteria, 11 relevant studies were selected. Results: The review examined key methodological aspects such as algorithms (e.g., SVM, CNN, Decision Trees), EEG devices (Emotiv, Biosemi), experimental paradigms (videos, interactive games), dominant brainwave bands (beta, gamma, alpha), and electrode placement. Non-linear models, particularly when combined with immersive stimulation, achieved the highest classification accuracy (up to 92%). Beta and gamma frequencies were consistently associated with fear states, while frontotemporal electrode positioning and proprietary datasets further enhanced model performance. Conclusions: EEG-based fear detection using AI demonstrates high potential and rapid growth, offering significant interdisciplinary applications in healthcare, safety systems, and affective computing. Full article

In recent decades, flexible electronics have witnessed remarkable advancements in multiple fields, encompassing wearable electronics, human–machine interfaces (HMI), clinical diagnosis, and treatment, etc. Nevertheless, conventional rigid electronic devices are fundamentally constrained by their inherent non-stretchability and poor conformability, limitations that substantially impede their practical applications. In contrast, conductive hydrogels (CHs) for electronic skin (E-skin) and healthcare monitoring have attracted substantial interest owing to outstanding features, including adjustable mechanical properties, intrinsic flexibility, stretchability, transparency, and diverse functional and structural designs. Considerable efforts focus on developing CHs incorporating various conductive materials to enable multifunctional wearable sensors and flexible electrodes, such as metals, carbon, ionic liquids (ILs), MXene, etc. This review presents a comprehensive summary of the recent advancements in CHs, focusing on their classifications and practical applications. Firstly, CHs are categorized into five groups based on the nature of the conductive materials employed. These categories include polymer-based, carbon-based, metal-based, MXene-based, and ionic CHs. Secondly, the promising applications of CHs for electrophysiological signals and healthcare monitoring are discussed in detail, including electroencephalogram (EEG), electrocardiogram (ECG), electromyogram (EMG), respiratory monitoring, and motion monitoring. Finally, this review concludes with a comprehensive summary of current research progress and prospects regarding CHs in the fields of electronic skin and health monitoring applications. Full article

Background: Electroencephalography (EEG) systems based on textile electrodes are increasingly being developed to address the need for more wearable sensor systems for brain function monitoring. Blink-related oscillations (BROs) are a new measure of brain function that corresponds to brainwave responses occurring after spontaneous blinking, and indexes neural processes as the brain evaluates new visual information appearing after eye re-opening. Prior studies have reported BRO utility as both a clinical and non-clinical biomarker of cognition, but no study has demonstrated BRO measurement using textile-based EEG devices that facilitate user comfort for real-world applications. Methods: We investigated BRO measurement using a four-channel EEG system with textile electrodes by extracting BRO responses using existing, publicly available EEG data (n = 9). We compared BRO effects derived from textile-based electrodes with those from standard dry Ag/Ag-Cl electrodes collected at the same locations (i.e., Fp1, Fp2, F7, F8) and using the same EEG amplifier. Results: Results showed that BRO effects measured using textile electrodes exhibited similar features in both time and frequency domains compared to dry Ag/Ag-Cl electrodes. Data from both technologies also showed similar performance in artifact removal and signal capture. Conclusions: These findings provide the first demonstration of successful BRO signal capture using four-channel EEG with textile electrodes, providing compelling evidence toward the development of a comfortable and user-friendly EEG technology that uses the simple activity of blinking for objective brain function assessment in a variety of settings. Full article

Background: Biomechanical and neuromuscular differences between falls and recoveries have been well-studied; however, the cortical correlations remain unclear. Using mobile brain imaging via electroencephalography (EEG), we examined differences in sensorimotor beta frequencies between falls and recoveries during an unpredicted slip in walking. Methods: We recruited 22 young adults (15 female; 18–35 years) who experienced a slip (65 cm) during walking. Raw EEG signals were band-pass filtered, and independent component analysis was performed to remove non-neural sources, eventually three participants were excluded due to excessive artifacts. Peak beta power was extracted from three time-bins: 400 milliseconds pre-, 0–150 milliseconds post and 150–300 milliseconds post-perturbation from the midline (Cz) electrode. A 2 × 3 Analysis of Covariance assessed the interaction between time-bins and group on beta power, followed by Independent and Paired t-tests for between and within-group post hoc comparisons. Results: All participants (n = 19) experienced a balance loss, seven experienced a fall. There was a time × group interaction on beta power (p < 0.05). With no group differences pre-perturbation, participants who experienced a fall exhibited higher beta power during 0–150 milliseconds post-perturbation than those who recovered (p < 0.001). However, there were no group differences in beta power during 150–300 milliseconds post-perturbation. Conclusions: Young adults exhibiting a greater increase in beta power during the early post-perturbation period experienced a fall, suggesting a higher cortical error detection due to a larger mismatch in the expected and ongoing postural state and greater cortical dependence for sensorimotor processing. Our study results provide an overview of the possible cortical governance to modulate slip-fall/recovery outcomes. Full article

Background/Objectives: Accurately predicting the arrival position of a moving target is essential in sports and daily life. While predictive saccades are known to enhance performance, the neural mechanisms underlying the timing of these strategies remain unclear. This study investigated how the timing of saccadic strategies—executed early versus late—affects cortical activity patterns, as measured by electroencephalography (EEG). Methods: Sixteen participants performed a task requiring them to predict the arrival position and timing of a parabolically moving target that became occluded midway through its trajectory. Based on eye movement behavior, participants were classified into an Early Saccade Strategy Group (SSG) or a Late SSG. EEG signals were analyzed in the low beta band (13–15 Hz) using the Hilbert transform. Group differences in eye movements and EEG activity were statistically assessed. Results: No significant group differences were observed in final position or response timing errors. However, time-series analysis showed that the Early SSG achieved earlier and more accurate eye positioning. EEG results revealed greater low beta activity in the Early SSG at electrode sites FC6 and P8, corresponding to the frontal eye field (FEF) and middle temporal (MT) visual area, respectively. Conclusions: Early execution of predictive saccades was associated with enhanced cortical activity in visuomotor and motion-sensitive regions. These findings suggest that early engagement of saccadic strategies supports more efficient visuospatial processing, with potential applications in dynamic physical tasks and digitally mediated performance domains such as eSports. Full article

Introduction: Substance use disorders (SUDs) pose a significant public health challenge, with current treatments often exhibiting limited effectiveness and high relapse rates. Transcranial direct current stimulation (tDCS), a noninvasive neuromodulation technique that delivers low-intensity direct current via scalp electrodes, has shown promise in various psychiatric and neurological conditions. In SUDs, tDCS may help to modulate key neurocircuits involved in craving, executive control, and reward processing, potentially mitigating compulsive drug use. However, the precise neurobiological mechanisms by which tDCS exerts its therapeutic effects in SUDs remain only partly understood. This review addresses that gap by synthesizing evidence from clinical studies that used neuroimaging (fMRI, fNIRS, EEG) and blood-based biomarkers to elucidate tDCS’s mechanisms in treating SUDs. Methods: A targeted literature search identified articles published between 2008 and 2024 investigating tDCS interventions in alcohol, nicotine, opioid, and stimulant use disorders, focusing specifically on physiological and neurobiological assessments rather than purely behavioral outcomes. Studies were included if they employed either neuroimaging (fMRI, fNIRS, EEG) or blood tests (neurotrophic and neuroinflammatory markers) to investigate changes induced by single- or multi-session tDCS. Two reviewers screened titles/abstracts, conducted full-text assessments, and extracted key data on participant characteristics, tDCS protocols, neurobiological measures, and clinical outcomes. Results: Twenty-seven studies met the inclusion criteria. Across fMRI studies, tDCS—especially targeting the dorsolateral prefrontal cortex—consistently modulated large-scale network activity and connectivity in the default mode, salience, and executive control networks. Many of these changes correlated with subjective craving, attentional bias, or extended time to relapse. EEG-based investigations found that tDCS can alter event-related potentials (e.g., P3, N2, LPP) linked to inhibitory control and salience processing, often preceding or accompanying changes in craving. One fNIRS study revealed enhanced connectivity in prefrontal regions under active tDCS. At the same time, two blood-based investigations reported the partial normalization of neurotrophic (BDNF) and proinflammatory markers (TNF-α, IL-6) in participants receiving tDCS. Multi-session protocols were more apt to drive clinically meaningful neuroplastic changes than single-session interventions. Conclusions: Although significant questions remain regarding optimal stimulation parameters, sample heterogeneity, and the translation of acute neural shifts into lasting behavioral benefits, this research confirms that tDCS can induce detectable neurobiological effects in SUD populations. By reshaping activity across prefrontal and reward-related circuits, modulating electrophysiological indices, and altering relevant biomarkers, tDCS holds promise as a viable, mechanism-based adjunctive therapy for SUDs. Rigorous, large-scale studies with longer follow-up durations and attention to individual differences will be essential to establish how best to harness these neuromodulatory effects for durable clinical outcomes. Full article

This paper presents a multi-participant driving simulation framework designed to support traffic experiments involving the simultaneous collection of vehicle telemetry and cognitive data. The system integrates motion-enabled driving cockpits, high-fidelity steering and pedal systems, immersive visual displays (monitor or virtual reality), and the Assetto Corsa simulation engine. To capture cognitive states, dry-electrode EEG headsets are used alongside a custom-built software tool that synchronizes EEG signals with vehicle telemetry across multiple drivers. The primary contribution of this work is the development of a modular, scalable, and customizable experimental platform with robust data synchronization, enabling the coordinated collection of neural and telemetry data in multi-driver scenarios. The synchronization software developed through this study is freely available to the research community. This architecture supports the study of human–human interactions by linking driver actions with corresponding neural activity across a range of driving contexts. It provides researchers with a powerful tool to investigate perception, decision-making, and coordination in dynamic, multi-participant traffic environments. Full article

Currently, the electroencephalogram (EEG) cap is limited to a finite number of sizes based on head circumference, lacking the mechanical flexibility to accommodate the full range of skull dimensions. This reliance on head circumference data alone often results in a poor fit between the EEG cap and the user’s head shape. To address these limitations, we have developed a four-dimensional (4D) adjustable EEG cap. This cap features an adjustable mechanism that covers the entire cranial area in four dimensions, allowing it to fit the head shapes of nearly all adults. The system is compatible with 64 channels or lower electrode counts. We conducted a study with numerous volunteers to compare the performance characteristics of the 4D caps with the commercial (COML) caps in terms of contact pressure, preparation time, wearing impedance, and performance in brain–computer interface (BCI) applications. The 4D cap demonstrated the ability to adapt to various head shapes more quickly, reduce impedance during testing, and enhance measurement accuracy, signal-to-noise ratio (SNR), and comfort. These improvements suggest its potential for broader application in both laboratory settings and daily life. Full article

The mental fatigue of crane operators can pose a serious threat to construction safety. To enhance the safety of crane operations on construction sites, this study proposes a rotary switch semi-dry electrode for detecting the mental fatigue of crane operators. This rotary switch semi-dry electrode overcomes the problems of the large impedance value of traditional dry electrodes, the cumbersome wet electrode operation, and the uncontrollable outflow of conductive liquid from traditional semi-dry electrodes. By designing a rotary switch structure inside the electrode, it allows the electrode to be turned on and used in motion, which greatly improves the efficiency of using the conductive fluid and prolongs the electrode’s use time. A conductive sponge was used at the electrode’s contact end with the skin, improving comfort and making it suitable for long-term wear. In addition, in this study, the multifractal detrend fluctuation analysis (MF-DFA) method was used to detect the mental fatigue state of crane operators. The results indicate that the MF-DFA is more responsive to the tiredness traits of individuals than conventional fatigue detection methods. The proposed rotary switch semi-dry electrode can quickly and accurately detect the mental fatigue of crane operators, provide support for timely warning or intervention, and effectively reduce the risk of accidents at construction sites, enhancing construction safety and efficiency. Full article

The electroencephalogram (EEG) is one of the most popular neurophysiological methods in neuroscience. Scalp EEG measurements are obtained using various numbers of channels for both clinical and research applications. This pilot study explores the effect of EEG channel count on motor imagery classification using source analysis in brain–computer interface (BCI) applications. Different channel configurations are employed to evaluate classification performance. This study focuses on mu band signals, which are sensitive to motor imagery-related EEG changes. Common spatial patterns are utilized as a spatiotemporal filter to extract signal components relevant to the right hand and right foot extremities. Classification accuracies are obtained using configurations with 19, 30, 61, and 118 electrodes to determine the optimal number of electrodes in motor imagery studies. Experiments are conducted on the BCI Competition III Dataset Iva. The 19-channel configuration yields lower classification accuracy when compared to the others. The results from 118 channels are better than those from 19 channels but not as good as those from 30 and 61 channels. The best results are achieved when 61 channels are utilized. The average accuracy values are 83.63% with 19 channels, increasing to 84.70% with 30 channels, 84.73% with 61 channels, and decreasing to 83.95% when 118 channels are used. Full article

The way in which EEG signals reflect mental tasks that vary in duration and intensity is a key topic in the investigation of neural processes concerning neuroscience in general and BCI technologies in particular. More recent research has reinforced historical studies that highlighted theta band activity in relation to cognitive performance. In our study, we propose a comparative analysis of experiments with cognitive load imposed by arithmetic calculations performed mentally. The analysis of EEG signals captured with 64 electrodes is performed on low theta components extracted by narrowband filtering. As main signal discriminators, we introduced an original measure inspired by the integral of the curve of a function—specifically the signal function over the period corresponding to the filter band. Another measure of the signal considered as a discriminator is energy. In this research, it was used just for model comparison. A cognitive load detection algorithm based on these signal metrics was developed and tested on original experimental data. The results present EEG activity during mental tasks and show the behavioral pattern across 64 channels. The most precise and specific EEG channels for discriminating cognitive tasks induced by arithmetic tests are also identified. Full article

This study investigated the effects of lemon verbena extract (LVE) on sleep regulation using both a pentobarbital-induced sleep model and an EEG-based sleep assessment model in mice. To elucidate its potential mechanisms, mice were randomly assigned to five groups: control, positive control (diazepam, 2 mg/kg b.w.), and three LVE-treated groups receiving 40, 80, or 160 mg/kg b.w. via oral administration. In the pentobarbital-induced sleep model, mice underwent a two-week oral administration of LVE, followed by intraperitoneal pentobarbital injections. The results demonstrated that LVE significantly shortened sleep latency and prolonged sleep duration compared to the control group. Notably, adenosine A1 receptor expression, both at the mRNA and protein levels, was markedly upregulated in the brains of LVE-treated mice. Furthermore, LVE’s administration led to a significant increase in the mRNA expression of gamma-aminobutyric acid type A (GABA_A) receptor subunits (α2 and β2) in brain tissue. In the electroencephalography (EEG)/electromyogram (EMG)-based sleep model, mice underwent surgical implantation of EEG and EMG electrodes, followed by one week of LVE administration. Quantitative EEG analysis revealed that LVE treatment reduced wakefulness while significantly enhancing REM and NREM sleep’s duration, indicating its potential sleep-promoting effects. These findings suggest that LVE may serve as a promising natural sleep aid, improving both the quality and duration of sleep through the modulation of adenosine and GABAergic signaling pathways. Full article

The study of non-phase modulation of different frequencies in the human electroencephalography (EEG) is revealing new mechanisms involved in information processing. In particular, it has been described that the alpha band, through a desynchronization of its non-phase component, could represent a mechanism for sensory gain in visual stimulus processing. One key question to address is whether this activity can be modulated (increased) by the recall of a previously memorized stimulus. The objective of this study is to answer this question by recording EEG activity with 58 electrodes and applying time-frequency analysis techniques (Temporal Spectral Evolution and the Hilbert Transform) in a sample of 27 human participants during a word recall task. The results of the study showed an increase in alpha phase modulation for recalled words compared to not recalled words, which included modulation of the P1 component. Additionally, alpha non-phase modulation also increased for recalled words, suggesting that the enhanced P1 component response could, in fact, be an indirect result of the attenuation of background neural noise, as proposed by the sensory gain hypothesis. Full article