Search Results (364)

Objectives: We examined whether sensory inhibition was altered in patients with autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) as deficits in the neural inhibition are considered to be involved in both disorders. Methods: By using auditory change-related brain potentials as the test response, paired pulse suppression (PPS) and prepulse inhibition (PPI) were compared among healthy controls (n = 57), patients with ASD (n = 22), and ADHD (n = 8). The test change-related response was elicited by an abrupt sound pressure increase in a continuous sound. In the PPS experiment, two 15 dB change stimuli were given 600 ms apart to elicit two change-related responses. In the PPI experiment, the test stimulus of a 10 dB increase and prepulse of a 2 dB increase were given with an interval of 50 ms. Evoked potentials were recorded from Cz referenced to the linked mastoids. Results: PPS differed significantly among the three groups, with a significantly lower value for ADHD than controls. PPI was significantly lower for ASD and ADHD than normal controls. Conclusions: Since these two measures are thought to represent changes in circuit excitability due to preceding stimuli via GABAergic transmission, the present results support the idea that dysfunction of the GABAergic system contributes to the etiology of these disorders. The present results showed that the pattern of dysfunction differed between the two disorders and suggest that measurements of inhibitory function may be able to differentiate between the two disorders. Full article

Medical physicists are transforming physiological measurements and electrophysiological applications by addressing challenges like motion artifacts and regulatory compliance through advanced signal processing, artificial intelligence (AI), and statistical rigor. Their innovations in wearable electrophysiology achieve 8–12 dB signal-to-noise ratio (SNR) improvements in EEG, 60% motion artifact reduction, and 94.2% accurate AI-driven arrhythmia detection at 12 μW power. In precision neurology, machine learning (ML) with evoked potentials (EPs) predicts spinal cord injury (SCI) recovery and multiple sclerosis (MS) progression with 79.2% accuracy based on retrospective data from 560 SCI/MS patients. By integrating multimodal data (EPs, MRI), developing quantum sensors, and employing federated learning, these can enhance diagnostic precision and prognostic accuracy. Clinical applications span epilepsy, stroke, cardiac monitoring, and chronic pain management, reducing diagnostic errors by 28% and optimizing treatments like deep brain stimulation (DBS). In this paper, we review the current state of wearable devices and provide some insight into possible future directions. Embedding medical physicists into standardization efforts is critical to overcoming barriers like quantum sensor power consumption, advancing personalized, evidence-based healthcare. Full article

As brain–computer interface (BCI) technology continues to advance, research on human brain function has gradually transitioned from theoretical investigation to practical engineering applications. To support EEG signal acquisition in a variety of real-world scenarios, BCI electrode systems must demonstrate a balanced combination of electrical performance, wearing comfort, and portability. Dry electrodes have emerged as a promising alternative for EEG acquisition due to their ability to operate without conductive gel or complex skin preparation. This paper reviews the latest progress in dry electrode EEG systems, summarizing key achievements in hardware design with a focus on structural innovation and material development. It also examines application advances in several representative BCI domains, including emotion recognition, fatigue and drowsiness detection, motor imagery, and steady-state visual evoked potentials, while analyzing system-level performance. Finally, the paper critically assesses existing challenges and identifies critical future research priorities. Key recommendations include developing a standardized evaluation framework to bolster research reliability, enhancing generalization performance, and fostering coordinated hardware-algorithm optimization. These steps are crucial for advancing the practical implementation of these technologies across diverse scenarios. With this survey, we aim to offer a comprehensive reference and roadmap for researchers engaged in the development and implementation of next-generation dry electrode EEG-based BCI systems. Full article

Steady-State Visual Evoked Potentials (SSVEPs) have emerged as an efficient means of interaction in brain–computer interfaces (BCIs), achieving bioinspired efficient language output for individuals with aphasia. Addressing the underutilization of frequency information of SSVEPs and redundant computation by existing transformer-based deep learning methods, this paper analyzes signals from both the time and frequency domains, proposing a stacked encoder–decoder (SED) network architecture based on an xLSTM model and spatial attention mechanism, termed SED-xLSTM, which firstly applies xLSTM to the SSVEP speller field. This model takes the low-channel spectrogram as input and employs the filter bank technique to make full use of harmonic information. By leveraging a gating mechanism, SED-xLSTM effectively extracts and fuses high-dimensional spatial-channel semantic features from SSVEP signals. Experimental results on three public datasets demonstrate the superior performance of SED-xLSTM in terms of classification accuracy and information transfer rate, particularly outperforming existing methods under cross-validation across various temporal scales. Full article

In this paper, we study the classification problem of short-time-window steady-state visual evoked potentials (SSVEPs) and propose a novel deep convolutional network named EEGResNet based on the idea of residual connection to further improve the classification performance. Since the frequency-domain features extracted from short-time-window signals are difficult to distinguish, the EEGResNet starts from the filter bank (FB)-based feature extraction module in the time domain. The FB designed in this paper is composed of four sixth-order Butterworth filters with different bandpass ranges, and the four bandwidths are 19–50 Hz, 14–38 Hz, 9–26 Hz, and 3–14 Hz, respectively. Then, the extracted four feature tensors with the same shape are directly aggregated together. Furthermore, the aggregated features are further learned by a six-layer convolutional neural network with residual connections. Finally, the network output is generated through an adaptive fully connected layer. To prove the effectiveness and superiority of our designed EEGResNet, necessary experiments and comparisons are conducted over two large public datasets. To further verify the application potential of the trained network, a virtual simulation of brain computer interface (BCI) based quadrotor control is presented through V-REP. Full article

Steady-state visual evoked potential (SSVEP) paradigms are widely used in brain–computer interface (BCI) systems due to their reliability and fast response. However, traditional static stimuli may reduce user comfort and engagement during prolonged use. This study proposes a dynamic stimulation paradigm combining periodic motion trajectories with speed control. Using four frequencies (6, 8.57, 10, 12 Hz) and three waveform patterns (sinusoidal, square, sawtooth), speed was modulated at 1/5, 1/10, and 1/20 of each frequency’s base rate. An offline experiment with 17 subjects showed that the low-speed sinusoidal and sawtooth trajectories matched the static accuracy (85.84% and 83.82%) while reducing cognitive workload by 22%. An online experiment with 12 subjects participating in a fruit-slicing game confirmed its practicality, achieving recognition accuracies above 82% and a System Usability Scale score of 75.96. These results indicate that coordinated trajectory and speed modulation preserves SSVEP signal quality and enhances user experience, offering a promising approach for fatigue-resistant, user-friendly BCI application. Full article

In this study, we conducted two steady-state visual evoked potential (SSVEP) studies to develop a practical brain–computer interface (BCI) system for communication and control applications. The first study introduces a novel visual stimulus paradigm that combines checkerboard patterns with flickering circles configured in single-, double-, and triple-layer forms. We tested three flickering frequency conditions: a single fundamental frequency, a combination of the fundamental frequency and its harmonics, and a combination of two fundamental frequencies. The second study utilizes personalized visual stimuli to enhance SSVEP responses. SSVEP detection was performed using power spectral density (PSD) analysis by employing Welch’s method and relative PSD to extract SSVEP features. Commands classification was carried out using a proposed decision rule–based algorithm. The results were compared with those of a conventional checkerboard pattern with flickering squares. The experimental findings indicate that single-layer flickering circle patterns exhibit comparable or improved performance when compared with the conventional stimuli, particularly when customized for individual users. Conversely, the multilayer patterns tended to increase visual fatigue. Furthermore, individualized stimuli achieved a classification accuracy of 90.2% in real-time SSVEP-based BCI systems for six-command generation tasks. The personalized visual stimuli can enhance user experience and system performance, thereby supporting the development of a practical SSVEP-based BCI system. Full article

Background/Objectives: Pediatric acquired demyelinating syndromes (ADSs) encompass a heterogeneous group of disorders, including multiple sclerosis (MS), MOG antibody-associated disease (MOGAD), and neuromyelitis optica spectrum disorder (NMOSD), with distinct clinical trajectories and prognoses. While analyzed collectively at baseline to reflect real-world diagnostic uncertainty, outcome predictors were also examined according to final diagnosis. Identifying early predictors is crucial for optimizing long-term outcomes. Methods: We retrospectively analyzed 30 pediatric patients (mean onset age: 11.3 years) with ADSs. Clinical, radiological, CSF, antibody, and neurophysiological data were collected and analyzed alongside treatment strategies. Outcomes—EDSS scores, neuroradiological changes, and clinical status—were evaluated over a 3-year period. Results: Final diagnoses included MOGAD (36.6%), MS (33.3%), NMOSD (6.6%), ADEM (10%), and other ADSs (13.3%). At onset, ≥3 brain lesions were present in 76.7% of patients. Disease-modifying therapies (DMTs) were used in 37% and acute immunotherapy in 90%. EDSS progression was significantly associated with DMT use at multiple timepoints, with additional predictors including MRI lesion type, CSF findings, antibody status, and evoked potentials. At 3 years, neurocognitive function predicted clinical outcome. Conclusions: Early immunotherapy and baseline instrumental findings are key predictors of outcome in pediatric ADSs. MOGAD showed a more favorable course, while MS and NMOSD were associated with greater long-term disability. A comprehensive, early diagnostic approach is essential for improving prognosis. Full article

Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) has emerged as a transformative tool for probing cortical dynamics with millisecond precision. This review examines the state-dependent nature of TMS-EEG, a critical yet underexplored dimension influencing measurement reliability and clinical applicability. By integrating TMS’s neuromodulatory capacity with EEG’s temporal resolution, this synergy enables real-time analysis of brain network dynamics under varying neural states. We delineate foundational mechanisms of TMS-evoked potentials (TEPs), discuss challenges posed by temporal and inter-individual variability, and evaluate advanced paradigms such as closed-loop and task-embedded TMS-EEG. The former leverages real-time EEG feedback to synchronize stimulation with oscillatory phases, while the latter aligns TMS pulses with task-specific cognitive phases to map transient network activations. Current limitations—including hardware constraints, signal artifacts, and inconsistent preprocessing pipelines—are critically analyzed. Future directions emphasize adaptive algorithms for neural state prediction, phase-specific stimulation protocols, and standardized methodologies to enhance reproducibility. By bridging mechanistic insights with personalized neuromodulation strategies, state-dependent TMS-EEG holds promise for advancing both basic neuroscience and precision medicine, particularly in psychiatric and neurological disorders characterized by dynamic neural dysregulation. Full article

Background/Objectives: We showed that a tailored strengthening intervention based on the size of motor evoked potentials (MEPs) elicited by transcranial magnetic stimulation (TMS) in the affected hemisphere resulted in an improved affected arm function, regardless of stroke severity. Also, adding anodal transcranial direct stimulation (atDCS) during training did not alter the results as participants receiving real or sham stimulation showed similar gains. The goal of this study was to report on the changes in basic measures of corticomotor excitability in response to the intervention and to determine whether these changes were influenced by tDCS and correlated with those measured in arm function. Methods: The TMS measures consisted of the resting motor threshold (rMT), MEP amplitude at rest, and the silent period (SP) duration. Clinical outcomes included the Box and Block test (BBT) and grip strength (GS). Results: Post-intervention, regardless of atDCS (p > 0.62), no significant change in corticomotor excitability was noted (p > 0.15), as well as no association between the changes in TMS measures and arm function gains (p > 0.06). Conclusions: As observed for clinical measures, atDCS did not influence corticomotor excitability. The absence of an increase in the excitability of the affected hemisphere and important associations between changes in corticomotor excitability and clinical gains suggest that factors other than brain plasticity could mediate gains in arm function. Further investigations are required regarding the role of tDCS in stroke rehabilitation. Full article

The P300 event-related potential, evoked by attending to specific sensory stimuli, is utilized in non-invasive brain–computer interface (BCI) systems and is considered the only interface through which individuals with complete paralysis can operate devices based on their intention. Conventionally, visual stimuli used to elicit P300 have been presented using displays; however, placing a display directly in front of the user obstructs the field of view and prevents the user from perceiving their surrounding environment. Moreover, every time the user changes posture, the display must be repositioned accordingly, increasing the burden on caregivers. To address these issues, we propose a novel system that employs wirelessly controllable LED visual stimulus presentation devices distributed throughout the surrounding environment, rather than relying on traditional displays. The primary challenge in the proposed system is the communication delay associated with wireless control, which introduces errors in the timing of stimulus presentation—an essential factor for accurate P300 analysis. Therefore, it is necessary to evaluate how such delays affect P300 detection accuracy. The second challenge lies in the variability of visual stimulus strength due to differences in viewing distance caused by the spatial distribution of stimulus devices. This also requires the validation of its impact on P300 detection. In Experiment 1, we evaluated system performance in terms of wireless communication delay and confirmed an average delay of 352.1 ± 30.9 ms. In Experiment 2, we conducted P300 elicitation experiments using the wireless visual stimulus presentation device under conditions that allowed the precise measurement of stimulus presentation timing. We compared P300 waveforms across three conditions: (1) using the exact measured stimulus timing, (2) using the stimulus timing with a fixed compensation of 350 ms for the wireless delay, and (3) using the stimulus timing with both the 350 ms fixed delay compensation and an additional pseudo-random error value generated based on a normal distribution. The results demonstrated the effectiveness of the proposed delay compensation method in preserving P300 waveform integrity. In Experiment 3, a system performance verification test was conducted on 21 participants using a wireless visual presentation device. As a result, statistically significant differences (p < 0.01) in amplitude between target and non-target stimuli, along with medium or greater effect sizes (Cohen’s d: 0.49–0.61), were observed under all conditions with an averaging count of 10 or more. Notably, the P300 detection accuracy reached 85% with 40 averaging trials and 100% with 100 trials. These findings demonstrate that the system can function as a P300 speller and be utilized as an interface equivalent to conventional display-based methods. Full article

This study investigated performance characteristics and channel selection strategies for single-flicker steady-state visual evoked potential (SSVEP) brain–computer interfaces (BCIs) using minimal recording channels. SSVEP clustering patterns from seven subjects, who focused on four static targets while being exposed to a central 15 Hz stimulus, were analyzed. Using a single-channel approach, signal energy patterns were examined, and principal component analysis (PCA) was performed, which explained over 90% of the data variance. The Calinski–Harabasz Index quantified state separability, identifying channels and comparisons with maximum clustering efficiency. The results demonstrate the feasibility of implementing single-flicker SSVEP BCIs with reduced recording channels, contributing to more practical and efficient BCI systems. Full article

The search for novel compounds with anticonvulsant properties remains a key focus in neuropharmacology. Recently, the diazepine-benzimidazole derivative, DAB-19, has emerged as a promising candidate due to its demonstrated anxiolytic and analgesic effects. In this study, we investigate the mechanisms underlying DAB-19’s activity, focusing on its impact on glutamatergic transmission, a key target in the pathophysiology of various central nervous system disorders. Intriguingly, while DAB-19 suppressed evoked glutamatergic transmission in rat brain slices, it simultaneously enhanced spontaneous neurotransmission. Further experiments on glutamatergic neuromuscular synapses in fly larvae revealed two distinct mechanisms: calcium-dependent potentiation of glutamate release and inhibition of spike propagation via blockade of voltage-gated sodium channels. The latter effect was directly confirmed in rat brain neurons. Given its action on sodium channels, we tested DAB-19 in the pentylenetetrazole model, where it delayed seizure onset but did not prevent seizures. These findings position DAB-19 as a multifaceted compound with significant therapeutic potential. Full article

Background: Spinal manual therapy (SMT) is widely used in the management of musculoskeletal pain. In addition to mechanical effects, SMT may induce neurophysiological changes at both central and autonomic levels. However, the extent and consistency of these short-term effects remain unclear. Objective: To systematically review the short-term effects of SMT on pain perception, central nervous system (CNS) activity, and autonomic nervous system (ANS) responses in adults with musculoskeletal pain or in healthy controls. Methods: A systematic review was conducted. Three databases (PubMed, ScienceDirect, Embase) were searched up to October 2023, with a final update in March 2025. Randomized controlled trials involving SMT and assessing outcomes related to pain, CNS, or ANS function were included. The methodological quality was assessed using the PEDro scale. The results were synthesized narratively and categorized by outcome domain. Four summary tables were created to present the study characteristics, main findings, methodological quality, and risk of bias. Results: Eleven trials were included. SMT produced variable effects on pain perception, with more consistent results observed when the treatment was applied frequently and followed standardized protocols. The CNS-related outcomes (e.g., fMRI connectivity, motor-evoked potentials) suggested short-term modulation of brain and spinal excitability in some studies. The ANS responses were heterogeneous, ranging from parasympathetic activation to sympathetic stimulation, depending on the intervention and population. The methodological quality was moderate to high in most studies, although the small sample sizes and limited blinding increased the risk of bias. The effect sizes were not consistently reported. Conclusions: SMT may induce short-term neuromodulatory effects on pain, CNS, and ANS activity. These effects appear to be context-dependent and require precise, repeated, and purposeful application. Full article

Background/Objectives: As the regulatory center for basal ganglia, the substantia nigra is involved in the pathophysiology of dopaminergic dysregulation in Parkinson’s disease (PD). Increasing neuronal excitability of dopaminergic neurons by different therapeutic methods could reverse the locomotor disturbances of PD. The purpose of this study was the comparative assessment of effects induced by excitatory output from the motor cortex to the substantia nigra (SN) and to investigate the pattern of neuronal responses in an experimental rat model of rotenone-induced (intracerebral infusion) neurodegeneration and treated with bacterial melanin (BM). Methods: Thirty-three rats were divided into three groups: control or intact animals (n = 12), animals with the rotenone-induced model of PD (n = 10), and animals with the PD model and treated with BM in 48 h following the infusion (n = 11). Registration of neuronal activity from SN neurons was conducted at four weeks following the rotenone administration. High-frequency stimulation of brain cortical area M1 was performed and the background and evoked activity patterns of 622 neurons were recorded. The difference between the groups was analyzed using one-way ANOVA followed by Tukey’s test. Results: A statistically significant difference was observed between the similar proportions of post-stimulus effects registered in different groups, showing the predominance of excitatory responses in the neurons of the melanin-treated group. A comparison of the firing pattern between the SNc and SNr neurons did not reveal significant differences. Conclusions: BM treatment has the potential to enhance motor recovery after neurodegeneration in the SN. Deep brain stimulation via the cortico-nigral pathway, with the application of BM, enhances electrical activity in dopaminergic neurons of the substantia nigra and could be a potential therapeutic model for PD. Full article