Consumer behavior on the Internet is influenced by factors that can affect consumers' perceptions and attention to products. Understanding these processes at the neurobiological level can help to understand consumers' implicit responses to marketing stimuli. The objective of this study is to use electroencephalography (EEG) to investigate the differential effects of selected online purchase decision factors that are becoming increasingly important in online shopping.

This study investigates the impact of social factors on public acceptance of brain-computer interface (BCI) technology within China's general population. As BCI emerges as a pivotal advancement in artificial intelligence and a cornerstone of Industry 5.0, understanding its societal reception is crucial. Utilizing data from the Psychological and Behavioral Study of Chinese Residents ( = 1,923), this research examines the roles of learning ability, age, health, social support, and socioeconomic status in BCI acceptance, alongside considerations of gender and the level of monthly household income. Multiple regression analysis via STATA-MP18 reveals that while health, socioeconomic status, social support, and learning ability significantly positively correlate with acceptance, and age presents an inverse relationship, gender and household income do not demonstrate a significant effect. Notably, the prominence of learning ability and social support as principal factors suggests targeted avenues for increasing BCI technology adoption. These findings refine the current understanding of technology acceptance and offer actionable insights for BCI policy and practical applications.

Neuroimaging has expanded our understanding of pediatric brain disorders in which white matter organization and connectivity are crucial to functioning. Paralleling the known pathobiology of many neurodevelopmental disorders, traumatic brain injury (TBI) in childhood can alter trajectories of brain development. Specifically, diffusion tensor imaging (DTI) studies in TBI have demonstrated white matter (WM) abnormalities that suggest microstructural disruptions that may underlie atypical neurodevelopment. The neurocognitive correlates of these previous findings will be explored in this study.

Understanding speech in noisy environments is challenging even for individuals with normal hearing, and it poses a significant challenge for those with hearing impairments or listening difficulties. There are limitations associated with the current methods of evaluating speech comprehension in such environments, especially in individuals with peripheral hearing impairments. According to the predictive coding model, speech comprehension is an active inference process that integrates sensory information through the interaction of bottom-up and top-down processing. Therefore, in this study, we aimed to examine the role of prediction in speech comprehension using an electrophysiological marker of anticipation: stimulus-preceding negativity (SPN).

This study aimed to characterize the differences in neural function among patients with different functional abilities 2 years after anterior cruciate ligament reconstruction (ACLR).

Resting-state functional magnetic resonance imaging (RS-fMRI) has been extensively utilized for noninvasive investigation of human brain activity. While studies employing simultaneous recordings of fMRI and electrophysiology have established a connection between the low-frequency fluctuation (< 0.1 Hz) observed in RS-fMRI and the local field potential (LFP), it remains unclear whether the RS-fMRI signal exhibits frequency-dependent modulation, which is a well-documented phenomenon in LFP. The present study concurrently recorded resting-state functional magnetic resonance imaging (RS-fMRI) and local field potentials (LFP) in the striatum of 8 rats before and after a pharmacological manipulation. We observed a highly similar frequency-dependent pattern of amplitude changes in both RS-fMRI and LFP following the manipulation, specifically an increase in high-frequency band amplitudes accompanied by a decrease in low-frequency band amplitudes. These findings provide direct evidence that the enhanced high-frequency fluctuations and reduced low-frequency fluctuations observed in RS-fMRI may reflect heightened neuronal activity.

Vision serves as a critical channel for athletes to acquire information during competitions and constitutes a vital component of their competitive ability. Through scientifically designed sports visual training, specific visual skills can be enhanced, thereby assisting athletes in achieving optimal performance in competitive settings. This study aim to explore the visuomotor abilities and shooting performance of skeet shooters through Sports Vision Training (SVT).

In our complex world, the auditory system plays a crucial role in perceiving and processing our environment. Humans are able to segment and stream concurrent auditory objects, allowing them to focus on specific sounds, such as speech, and suppress irrelevant auditory objects. The attentional enhancement or suppression of sound processing is evident in neural data through a phenomenon called neural speech tracking. Previous studies have identified correlates of neural speech tracking in electroencephalography (EEG) data, but EEG measures are susceptible to motion artefacts, and the association between neural data and auditory objects is vulnerable to distraction.

Acupuncture on the acupuncture point GV20 has been considered effective for managing psychoneurological disorders since ancient times, and GV20 is now regularly used to treat stress-related diseases (SRDs), including psychoneurological disorders. Although reports indicating that acupuncture stimulation of GV20 alone regulates cerebral blood flow (CBF) in brain regions associated with SRDs have been scattered, from the perspective of the need for earlier action, the effects on subjective stress as self-reported in the stress state of healthy individuals and CBF changes in related prefrontal cortex (PFC) subregions, which is based as underlying mechanism, remain unclear. Therefore, there is a need to explore effective methods for analyzing such effects.

Despite using beta oscillations within the subthalamic nucleus as a biomarker of akinesia or rigidity in Parkinson's disease, a specific biomarker for freezing of gait (FOG) remains unclear. Recently, scalp phase-amplitude coupling (PAC) measured through scalp electroencephalography (EEG) has emerged as a promising tool for analyzing brain function. In this study, we examined whether PAC could be a biomarker for FOG.

Applying convolutional neural networks to a large number of EEG signal samples is computationally expensive because the computational complexity is linearly proportional to the number of dimensions of the EEG signal. We propose a new Gated Recurrent Unit (GRU) network model based on reinforcement learning, which considers the implementation of attention mechanisms in Electroencephalogram (EEG) signal processing scenarios as a reinforcement learning problem.

Transverse myelitis (TM)-associated dysesthesia is diverse and frequently resistant to treatment. This study explored the comprehensive effects of a novel transcutaneous electrical nerve stimulation (TENS) approach tailored to an individual's specific dysesthesia profile in a patient with TM.

Non-invasive brain stimulation (NIBS) technologies, such as repetitive transcranial magnetic stimulation (rTMS), offer significant therapeutic potential for a growing number of neuropsychiatric conditions. Concurrent with the expansion of this field is the swift evolution of rTMS methodologies, including approaches to optimize stimulation site planning. Traditional targeting methods, foundational to early successes in the field and still widely employed today, include using scalp-based heuristics or integrating structural MRI co-registration to align the transcranial magnetic stimulation (TMS) coil with anatomical landmarks. Recent evidence, however, supports refining and personalizing stimulation sites based on the target's structural and/or functional connectivity profile. These connectomic approaches harness the network-wide neuromodulatory effects of rTMS to reach deeper brain structures while also enabling a greater degree of personalization by accounting for heterogenous network topology. In this study, we acquired baseline multimodal magnetic resonance (MRI) at two time points to evaluate the reliability and reproducibility of distinct connectome-based strategies for stimulation site planning. Specifically, we compared the intra-individual difference between the optimal stimulation sites generated at each time point for (1) functional connectivity (FC) guided targets derived from resting-state functional MRI and (2) structural connectivity (SC) guided targets derived from diffusion tensor imaging. Our findings suggest superior reproducibility of SC-guided targets. We emphasize the necessity for further research to validate these findings across diverse patient populations, thereby advancing the personalization of rTMS treatments.

In hyperscanning studies, participants perform a joint task while their brain activation is simultaneously recorded. Evidence of inter-brain coupling is examined, in these studies, as a predictor of behavioral change. While the field of hyperscanning has made significant strides in unraveling the associations between inter-brain coupling and changes in social interactions, drawing causal conclusions between brain and behavior remains challenging. This difficulty arises from factors like the inherently different timescales of behavioral responses and measured cerebral activity, as well as the predominant focus of existing methods on associations rather than causality. Specifically, a question remains as to whether inter-brain coupling between specific brain regions leads to changes in behavioral synchrony, or vice-versa. We propose two novel approaches to addressing this question. The first method involves using dyadic neurofeedback, wherein instances of inter-brain coupling are directly reinforced. Such a system could examine if continuous changes of inter-brain coupling are the result of deliberate mutual attempts to synchronize. The second method employs statistical approaches, including Granger causality and Structural Equation Modeling (SEM). Granger causality assesses the predictive influence of one time series on another, enabling the identification of directional neural interactions that drive behavior. SEM allows for detailed modeling of both direct and indirect effects of inter-brain coupling on behavior. We provide an example of data analysis with the SEM approach, discuss the advantages and limitations of each approach and posit that applying these approaches could provide significant insights into how inter-brain coupling supports crucial processes that occur in social interactions.

In research on second language (L2) processing, the processing of reference has been highlighted as a domain of particular difficulty, but the source of the difficulty is not well understood. The present study examines whether differences in the pronominal systems of the first language (L1) and L2 impact processing. We take a novel approach, testing a group of intermediate-advanced L2 learners in both their L1 (Mandarin Chinese) and L2 (English), allowing us to directly examine whether L2 learners show similar or different patterns when processing the L1 and L2. We also test a group of L1 English speakers. The study focused on two topicality-related factors, subjecthood and pronominalization, that have been shown to increase the prominence of an entity in the discourse, making it more likely that an entity in subject position (subjecthood) or an entity that has been referred to with a pronoun (pronominalization) will be considered as an antecedent for a subsequent pronoun. We developed a picture verification task with visual-world eye-tracking in both English and Chinese. This task provides a measure of both pronoun interpretation and online processing. Results showed subtle differences in how subjecthood and pronominalization are weighted in English and Chinese as L1s: pronominalization played a stronger role in L1 Chinese than in L1 English both in the interpretation measure and in the eye-movement data. The results for the L2 English learners showed an interesting pattern in which their results were more similar to the L1 English results on the measure of pronoun interpretation, but were more similar to the L1 Chinese results in the eye-movement data. These results show successful use of discourse cues in L2 pronoun interpretation but differences between L1 and L2 speakers during processing. It is proposed that decreased sensitivity to morphosyntactic information that is not present in the L1 (case on pronouns) leads to differences in L2 referential processing, in line with proposals that L2 learners face challenges with integrating different kinds of linguistic information online, particularly morphosyntactic information.

Impairment in perception and sensory reweighting could predispose older adults to falls. This exploratory study aimed to investigate the differences in sensory reweighting and self-motion perception for postural control under single-sensory and multisensory perturbations between older Tai Chi (TC) practitioners and healthy active older controls.

Social ties play a crucial role in determining the health and wellbeing of individuals. However, it remains unclear whether the capacity to process social information distinguishes well-connected individuals from their less-connected peers. This study explored how an individual's social network structure influences the dynamic processing of group norms, utilizing event-related potentials (ERPs).

While the ethical significance of caregivers in neurological research has increasingly been recognized, the role of caregivers in brain-computer interface (BCI) research has received relatively less attention.

Electroencephalogram (EEG) is widely used in emotion recognition due to its precision and reliability. However, the nonstationarity of EEG signals causes significant differences between individuals or sessions, making it challenging to construct a robust model. Recently, domain adaptation (DA) methods have shown excellent results in cross-subject EEG emotion recognition by aligning marginal distributions. Nevertheless, these methods do not consider emotion category labels, which can lead to label confusion during alignment. Our study aims to alleviate this problem by promoting conditional distribution alignment during domain adaptation to improve cross-subject and cross-session emotion recognition performance.

Understanding brain-behavior relationships is the core goal of cognitive neuroscience. However, these relationships-especially those related to complex cognitive and psychopathological behaviors-have recently been shown to suffer from very small effect sizes (0.1 or less), requiring potentially thousands of participants to yield robust findings. Here, we focus on a much more optimistic case utilizing task-based fMRI and a multi-echo acquisition with trial-level brain-behavior associations measured within participant. In a visual object identification task for which the behavioral measure is response time (RT), we show that while trial-level associations between BOLD and RT can similarly suffer from weak effect sizes, converting these associations to their corresponding group-level effects can yield robust peak effect sizes (Cohen's  = 1.0 or larger). Multi-echo denoising (Multi-Echo ICA or ME-ICA) yields larger effects than optimally combined multi-echo with no denoising, which is in turn an improvement over standard single-echo acquisition. While estimating these brain-behavior relationships benefits from the inclusion of a large number of trials per participant, even a modest number of trials (20-30 or more) yields robust group-level effect sizes, with replicable effects obtainable with relatively standard sample sizes ( = 20-30 participants per sample).

Although the application of brain-computer interface (BCI) technology in rehabilitation has been extensively studied, a systematic and comprehensive bibliometric analysis of this area remains lacking. Thus, this study aims to analyze the research progress of BCI technology in rehabilitation through bibliometric methods.

Chronic olfactory disorders are some of the most frequent post-COVID-19 presentations. Olfactory training (OT) is currently the most popular method used for treating post-viral olfactory dysfunction (PVOD). We evaluated the effect of olfactory training on the chronic olfactory disorders of patients infected with COVID-19.

Temporal interference (TI) stimulation, an innovative non-invasive brain stimulation approach, has the potential to activate neurons in deep brain regions. However, the dynamic mechanisms underlying its neuromodulatory effects are not fully understood. This study aims to investigate the effects of TI stimulation on dynamic functional connectivity (dFC) in the motor cortex.

The purpose of this study was to investigate whether the anticipation of a mechanical perturbation applied to the arm during a reach-to-grasp movement elicits anticipatory adjustments in the reach and grasp components. Additionally, we aimed to evaluate whether anticipatory adjustments in the upper limb might be global or specific to the direction of the perturbation.