The driver in road hypnosis has not only some external characteristics, but also some internal characteristics. External features have obvious manifestations and can be directly observed. Internal features do not have obvious manifestations and cannot be directly observed. They need to be measured with specific instruments. Electroencephalography (EEG), as an internal feature of drivers, is the golden parameter for drivers\' life identification. EEG is of great significance for the identification of road hypnosis. An identification method for road hypnosis based on human EEG data is proposed in this paper. EEG data on drivers in road hypnosis can be collected through vehicle driving experiments and virtual driving experiments. The collected data are preprocessed with the PSD (power spectral density) method, and EEG characteristics are extracted. The neural networks EEGNet, RNN, and LSTM are used to train the road hypnosis identification model. It is shown from the results that the model based on EEGNet has the best performance in terms of identification for road hypnosis, with an accuracy of 93.01%. The effectiveness and accuracy of the identification for road hypnosis are improved in this study. The essential characteristics for road hypnosis are also revealed. This is of great significance for improving the safety level of intelligent vehicles and reducing the number of traffic accidents caused by road hypnosis.

With the development of data mining technology, the analysis of event-related potential (ERP) data has evolved from statistical analysis of time-domain features to data-driven techniques based on supervised and unsupervised learning. However, there are still many challenges in understanding the relationship between ERP components and the representation of familiar and unfamiliar faces. To address this, this paper proposes a model based on Dynamic Multi-Scale Convolution for group recognition of familiar and unfamiliar faces. This approach uses generated weight masks for cross-subject familiar/unfamiliar face recognition using a multi-scale model. The model employs a variable-length filter generator to dynamically determine the optimal filter length for time-series samples, thereby capturing features at different time scales. Comparative experiments are conducted to evaluate the model\'s performance against SOTA models. The results demonstrate that our model achieves impressive outcomes, with a balanced accuracy rate of 93.20% and an F1 score of 88.54%, outperforming the methods used for comparison. The ERP data extracted from different time regions in the model can also provide data-driven technical support for research based on the representation of different ERP components.

Ischemic stroke is a type of brain dysfunction caused by pathological changes in the blood vessels of the brain which leads to brain tissue ischemia and hypoxia and ultimately results in cell necrosis. Without timely and effective treatment in the early time window, ischemic stroke can lead to long-term disability and even death. Therefore, rapid detection is crucial in patients with ischemic stroke. In this study, we developed a deep learning model based on fusion features extracted from electroencephalography (EEG) signals for the fast detection of ischemic stroke. Specifically, we recruited 20 ischemic stroke patients who underwent EEG examination during the acute phase of stroke and collected EEG signals from 19 adults with no history of stroke as a control group. Afterwards, we constructed correlation-weighted Phase Lag Index (cwPLI), a novel feature, to explore the synchronization information and functional connectivity between EEG channels. Moreover, the spatio-temporal information from functional connectivity and the nonlinear information from complexity were fused by combining the cwPLI matrix and Sample Entropy (SaEn) together to further improve the discriminative ability of the model. Finally, the novel MSE-VGG network was employed as a classifier to distinguish ischemic stroke from non-ischemic stroke data. Five-fold cross-validation experiments demonstrated that the proposed model possesses excellent performance, with accuracy, sensitivity, and specificity reaching 90.17%, 89.86%, and 90.44%, respectively. Experiments on time consumption verified that the proposed method is superior to other state-of-the-art examinations. This study contributes to the advancement of the rapid detection of ischemic stroke, shedding light on the untapped potential of EEG and demonstrating the efficacy of deep learning in ischemic stroke identification.

Previous studies on crosslinguistic influence (CLI) on third language (L3) morphosyntactic acquisition have provided support for competing theories about the source(s) of CLI. The present study aimed to test if both L1 and L2 can be the source of CLI, and whether they influence L3 learning in similar or different ways. In particular, we aimed to add to our knowledge of the neural correlates of CLI by conducting an exploratory EEG study to investigate how L1 and L2 CLI affect L3 neural processing. Predictions based on the D/P model, which posited different memory systems sustaining L1 and L2, were tested. The findings confirmed both L1-sourced and L2-sourced facilitation on L3 morphosyntactic acquisition. Specifically, we suggest that L1-similarity showed a consolidating effect on L3 implicit knowledge and neurocognitive internalization, whereas L2-similarity contributed to enhanced L3 metalinguistic knowledge. This preliminary study is the first to investigate the neurocognitive mechanisms underlying CLI in L3 learning by natural language learners.

Competition is common in life, and intimate relationships are essential. Understanding how intimate relationships impact an individual\'s competitive process is crucial. This study explored the impact of competitor gender on female competition using electroencephalography analysis. The results revealed that females exhibited a smaller median of the absolute value of reaction time difference (DRT) between their partners and their competitors when their partners were absent compared to when their partners were present. Additionally, females showed greater average amplitudes of N2 posterior contralateral component (N2pc) and Late Positive Potential (LPP), increased activation of the alpha frequency band, and enhanced theta frequency band functional connectivity between the central parietal lobe and occipital lobe. Furthermore, when competing with individuals of the same gender as opposed to individuals of the opposite gender, females exhibited greater average amplitudes of percentage of wins and N2pc. A significant negative correlation was noted between the DRT and the average wave amplitudes of N2pc and LPP. These findings suggest that females are more engaged in competitive tasks when partners are not present and have improved decision-making when competing with same-gender individuals. This study provides evidence for the influence of lovers on female competition, helping females adapt to social competition and promoting healthy relationships.

Carbamazepine, a commonly prescribed antiepileptic drug, is known to induce hiccups in a subset of epileptic patients. Although relatively uncommon, can have significant clinical implications. This comprehensive review delves into the clinical and electroencephalographic correlates of carbamazepine-associated hiccups, aiming to enhance understanding and management of this neurological side effect. The authors\' review synthesizes qualitative epidemiological data, revealing that carbamazepine-induced hiccups occur in a subset of patients receiving the medication, with reported incidence rates ranging from 2.5 to 40%. Despite its relatively low prevalence, hiccups pose substantial challenges for patients and healthcare providers. Complications associated with carbamazepine-induced hiccups include disruption of sleep, impaired social functioning, and decreased quality of life, underscoring the clinical significance of this side effect. Effective management strategies can be implemented through a multidisciplinary approach, including collaboration among neurologists, pharmacists, and other healthcare professionals. These may include dose adjustments, medication discontinuation, and adjunctive therapies such as diaphragmatic breathing exercises or acupuncture. Additionally, close monitoring for adverse effects and timely intervention are essential to mitigate the impact of hiccups on patient well-being. Essentially, carbamazepine-induced hiccups represent a clinically relevant phenomenon that warrants attention in the management of epilepsy. By recognizing the clinical manifestations, understanding the underlying pathophysiology, and implementing evidence-based management strategies, healthcare providers can optimize patient care and improve outcomes in this patient population.

In the past, research on the cognitive neural mechanism of second language (L2) learners\' processing time information has focused on Indo-European languages. It has also focused on the temporal category expressed by morphological changes. However, there has been a lack of research on L2 learners\' various time coding means, especially for Mandarin, which lacks morphological changes. Using event-related potentials (ERPs), we investigated the cognitive neural mechanism of L2 learners with native Indonesian background in processing two time coding means (time adverbs and aspect markers) in Chinese. Indonesian has time adverb encoding time information similar to that of Chinese, but there are no aspect markers similar to Chinese in Indonesian. We measured ERPs time locked to the time adverb \" (cengjing)\" and the aspect marker \"verb + (verb + guo)\" in two different conditions, i.e., a control condition (the correct sentence) and a temporal information violation. The experimental results showed that the native speaker group induced the biphasic N400-P600 effect under the condition of time adverb violation, and induced P600 under the condition of the aspect marker \"verb + (verb + guo)\" violation. Indonesian L2 learners of Chinese only elicited P600 for the violation of time adverbs, and there was no statistically significant N400 similar to that of Chinese native speakers. In the case of aspect marker violation, we observed no significant ERPs component for the Indonesian L2 learners of Chinese. Both groups of subjects induced elicited a widely distributed and sustained negativity on the post-critical words after \"verb + (verb + guo)\" and \"(cengjing)\". This showed that the neural mechanism of Indonesian L2 learners of Chinese processing Chinese time coding differs from that of Chinese native speakers.

Understanding the response of the injured brain to different transcranial direct current stimulation (tDCS) montages may help explain the variable tDCS treatment results on poststroke motor gains. Cortical connectivity has been found to reflect poststroke motor gains and cortical plasticity, but the changes in connectivity following tDCS remain unknown. We aimed to investigate the relationship between tDCS-induced changes in cortical connectivity and poststroke motor gains. In this study, participants were assigned to receive four tDCS montages (anodal, cathodal, bilateral, and sham) over the primary motor cortex (M1) according to a single-blind, randomized, crossover design. Electroencephalography (EEG) and Jebsen-Taylor hand function test (JTT) were performed before and after the intervention. Motor cortical connectivity was measured using beta-band coherence with the ipsilesional and contralesional M1 as seed regions. Motor gain was evaluated based on the JTT completion time. We examined the relationship between baseline connectivity and clinical characteristics and that between changes in connectivity and motor gains after different tDCS montages. Baseline functional connectivity, motor impairment, and poststroke duration were correlated. High ipsilesional M1-frontal-temporal connectivity was correlated with a good baseline motor status, and increased connectivity was accompanied by good functional improvement following anodal tDCS treatment. Low contralesional M1-frontal-central connectivity was correlated with a good baseline motor status, and decreased connectivity was accompanied by good functional improvement following cathodal tDCS treatment. In conclusion, EEG-based motor cortical connectivity was correlated with stroke characteristics, including motor impairment and poststroke duration, and motor gains induced by anodal and cathodal tDCS.

Joint attention is an indispensable tool for daily communication. Abnormalities in joint attention may be a key reason underlying social impairment in schizophrenia spectrum disorders. In this study, we aimed to explore the attentional orientation mechanism related to schizotypal traits in a social situation. Here, we employed a Posner cueing paradigm with social attentional cues. Subjects needed to detect the location of a target that is cued by gaze and head orientation. The power in the theta frequency band was used to examine the attentional process in the schizophrenia spectrum. There were four main findings. First, a significant association was found between schizotypal traits and attention orientation in response to invalid gaze cues. Second, individuals with schizotypal traits exhibited significant activation of neural oscillations and synchrony in the theta band, which correlated with their schizotypal tendencies. Third, neural oscillations and synchrony demonstrated a synergistic effect during social tasks, particularly when processing gaze cues. Finally, the relationship between schizotypal traits and attention orientation was mediated by neural oscillations and synchrony in the theta frequency band. These findings deepen our understanding of the impact of theta activity in schizotypal traits on joint attention and offer new insights for future intervention strategies.

UNASSIGNED: This study aims to improve brain age estimation by developing a novel deep learning model utilizing overnight electroencephalography (EEG) data.
UNASSIGNED: We address limitations in current brain age prediction methods by proposing a model trained and evaluated on multiple cohort data, covering a broad age range. The model employs a one-dimensional Swin Transformer to efficiently extract complex patterns from sleep EEG signals and a convolutional neural network with attentional mechanisms to summarize sleep structural features. A multi-flow learning-based framework attentively merges these two features, employing sleep structural information to direct and augment the EEG features. A post-prediction model is designed to integrate the age-related features throughout the night. Furthermore, we propose a DecadeCE loss function to address the problem of an uneven age distribution.
UNASSIGNED: We utilized 18,767 polysomnograms (PSGs) from 13,616 subjects to develop and evaluate the proposed model. The model achieves a mean absolute error (MAE) of 4.19 and a correlation of 0.97 on the mixed-cohort test set, and an MAE of 6.18 years and a correlation of 0.78 on an independent test set. Our brain age estimation work reduced the error by more than 1 year compared to other studies that also used EEG, achieving the level of neuroimaging. The estimated brain age index demonstrated longitudinal sensitivity and exhibited a significant increase of 1.27 years in individuals with psychiatric or neurological disorders relative to healthy individuals.
UNASSIGNED: The multi-flow deep learning model proposed in this study, based on overnight EEG, represents a more accurate approach for estimating brain age. The utilization of overnight sleep EEG for the prediction of brain age is both cost-effective and adept at capturing dynamic changes. These findings demonstrate the potential of EEG in predicting brain age, presenting a noninvasive and accessible method for assessing brain aging.