Vigilance represents an ability to sustain prolonged attention and plays a crucial role in ensuring the reliability and optimal performance of various tasks. In this report, we describe a MultiModal Vigilance (MMV) dataset comprising seven physiological signals acquired during two Brain-Computer Interface (BCI) tasks. The BCI tasks encompass a rapid serial visual presentation (RSVP)-based target image retrieval task and a steady-state visual evoked potential (SSVEP)-based cursor-control task. The MMV dataset includes four sessions of seven physiological signals for 18 subjects, which encompasses electroencephalogram(EEG), electrooculogram (EOG), electrocardiogram (ECG), photoplethysmogram (PPG), electrodermal activity (EDA), electromyogram (EMG), and eye movement. The MMV dataset provides data from four stages: 1) raw data, 2) pre-processed data, 3) trial data, and 4) feature data that can be directly used for vigilance estimation. We believe this dataset will achieve flexible reuse and meet the various needs of researchers. And this dataset will greatly contribute to advancing research on physiological signal-based vigilance research and estimation.

With the rapid advancement of computer vision, machine learning, and consumer electronics, eye tracking has emerged as a topic of increasing interest in recent years. It plays a key role across diverse domains including human-computer interaction, virtual reality, and clinical and healthcare applications. Near-eye tracking (NET) has recently been developed to possess encouraging features such as wearability, affordability, and interactivity. These features have drawn considerable attention in the health domain, as NET provides accessible solutions for long-term and continuous health monitoring and a comfortable and interactive user interface. Herein, this work offers an inaugural concise review of NET for health, encompassing approximately 70 related articles published over the past two decades and supplemented by an in-depth examination of 30 literatures from the preceding five years. This paper provides a concise analysis of health-related NET technologies from aspects of technical specifications, data processing workflows, and the practical advantages and limitations. In addition, the specific applications of NET are introduced and compared, revealing that NET is fairly influencing our lives and providing significant convenience in daily routines. Lastly, we summarize the current outcomes of NET and highlight the limitations.

For patients with disorders of consciousness (DoC), accurate assessment of residual consciousness levels and cognitive abilities is critical for developing appropriate rehabilitation interventions. In this study, we investigated the potential of electrooculography (EOG) in assessing language processing abilities and consciousness levels. Patients\' EOG data and related electrophysiological data were analysed before and after explicit language learning. The results showed distinct differences in vocabulary learning patterns among patients with varying levels of consciousness. While minimally conscious patients showed significant neural tracking of artificial words and notable learning effects similar to those observed in healthy controls, whereas patients with unresponsive wakefulness syndrome did not show such effects. Correlation analysis further indicated that EOG detected vocabulary learning effects with comparable validity to electroencephalography, reinforcing the credibility of EOG indicator as a diagnostic tool. Critically, EOG also revealed significant correlations between individual patients\' linguistic learning performance and their Oromotor/verbal function as assessed through behavioural scales. In conclusion, this study explored the differences in language processing abilities among patients with varying consciousness levels. By demonstrating the utility of EOG in evaluating consciousness and detecting vocabulary learning effects, as well as its potential to guide personalised rehabilitation, our findings indicate that EOG indicators show promise as a rapid, accurate and effective additional tool for diagnosing and managing patients with DoC.

OBJECTIVE: To provide a genotype and phenotype characterization of the BEST1 mutation in Chinese patients with autosomal recessive bestrophinopathy (ARB) through multimodal imaging and next-generation sequencing (NGS).
METHODS: Seventeen patients from 17 unrelated families of Chinese origin with ARB were included in a retrospective cohort study. Phenotypic characteristics, including anterior segment features, were assessed by multimodal imaging. Multigene panel testing, involving 586 ophthalmic disease-associated genes, and Sanger sequencing were performed to identify disease-causing variants.
RESULTS: Among 17 ARB patients, the mean follow-up was 15.65 months and average onset age was 30.53 years (range: 9-68). Best corrected visual acuity ranged from light perception to 0.8. EOG recordings showed a typically decreased Arden ratio in 12 patients, and a normal or slightly decreased Arden ratio in two patients. Anterior features included shallow anterior chambers (16/17), ciliary pronation (16/17), iris bombe (13/17), iridoschisis (2/17), iris plateau (1/17), narrow angles (16/17) and reduced axial lengths (16/17). Sixteen patients had multiple bilateral small, round, yellow vitelliform deposits distributed throughout the posterior pole, surrounding the optic disc. Initial diagnoses included angle-closure glaucoma (four patients), Best disease (three patients), and central serous chorioretinopathy secondary to choroidal neovascularization (CNV) (one patient), with the remainder diagnosed with ARB. Fourteen patients underwent preventive laser peripheral iridotomy, four of whom also received combined trabeculectomy and iridotomy in both eyes for uncontrolled intraocular pressure. One patient received intravitreal conbercept for CNV. Overall, 15 distinct disease-causing variants of BEST1 were identified, with 14 (82.35%) patients having missense mutations. Common mutations included p. Arg255-256 and p. Ala195Val (both 23.68%), with the most frequent sites in exons 7 and 5.
CONCLUSIONS: This study provides a comprehensive characterization of anterior segment and genetic features in ARB, with a wide array of morphological abnormalities. Findings are relevant for refining clinical practices and genetic counseling and advancing pathogenesis research.

OBJECTIVE: To compare the accuracy and generalizability of an automated deep neural network and the Philip Sleepware G3™ Somnolyzer system (Somnolyzer) for sleep stage scoring using American Academy of Sleep Medicine (AASM) guidelines.
METHODS: Sleep recordings from 104 participants were analyzed by a convolutional neural network (CNN), the Somnolyzer and skillful technicians. Evaluation metrics were derived for different combinations of sleep stages. A further comparison between the Somnolyzer and the CNN model using a single-channel signal as input was also performed. Sleep recordings from 263 participants with a lower prevalence of OSA served as a cross-validation dataset to validate the generalizability of the CNN model.
RESULTS: The overall agreement between automated and manual scoring for sleep staging in 104 participants outperformed that of the Somnolyzer according to various metrics (accuracy: 81.81 % vs. 77.07 %; F1: 76.36 % vs. 73.80 %; Cohen\'s kappa: 0.7403 vs. 0.6848). The results showed that the left electrooculography (EOG) single-channel model had minor advantages over the Somnolyzer. In terms of consistency with manual sleep staging, the CNN model demonstrated superior performance in identifying more pronounced sleep transitions, particularly in the N2 stage and sleep latency metrics. Conversely, the Somnolyzer showed enhanced proficiency in the analysis of REM stages, notably in measuring REM latency. The accuracy in the cross-validation set of 263 participants was also above 80 %.
CONCLUSIONS: The CNN-based automated deep neural network outperformed the Somnolyzer and is sufficiently accurate for sleep study analyses using the AASM classification criteria.

Objective. Physiological signals based emotion recognition is a prominent research domain in the field of human-computer interaction. Previous studies predominantly focused on unimodal data, giving limited attention to the interplay among multiple modalities. Within the scope of multimodal emotion recognition, integrating the information from diverse modalities and leveraging the complementary information are the two essential issues to obtain the robust representations.Approach. Thus, we propose a intermediate fusion strategy for combining low-rank tensor fusion with the cross-modal attention to enhance the fusion of electroencephalogram, electrooculogram, electromyography, and galvanic skin response. Firstly, handcrafted features from distinct modalities are individually fed to corresponding feature extractors to obtain latent features. Subsequently, low-rank tensor is fused to integrate the information by the modality interaction representation. Finally, a cross-modal attention module is employed to explore the potential relationships between the distinct latent features and modality interaction representation, and recalibrate the weights of different modalities. And the resultant representation is adopted for emotion recognition.Main results. Furthermore, to validate the effectiveness of the proposed method, we execute subject-independent experiments within the DEAP dataset. The proposed method has achieved the accuracies of 73.82% and 74.55% for valence and arousal classification.Significance. The results of extensive experiments verify the outstanding performance of the proposed method.

Sleep staging plays an important role in the diagnosis and treatment of clinical sleep disorders. The sleep staging standard defines every 30 seconds as a sleep period, which may mean that there exist similar brain activity patterns during the same sleep period. Thus, in this work, we propose a novel time-related synchronization analysis framework named time-related multimodal sleep scoring model (TRMSC) to explore the potential time-related patterns of sleeping. In the proposed TRMSC, the time-related synchronization analysis is first conducted on the single channel electrophysiological signal, i.e., Electroencephalogram (EEG) and Electrooculogram (EOG), to explore the time-related patterns, and the spectral activation features are also extracted by spectrum analysis to obtain the multimodal features. With the extracted multimodal features, the feature fusion and selection strategy is utilized to obtain the optimal feature set and achieve robust sleep staging. To verify the effectiveness of the proposed TRMSC, sleep staging experiments were conducted on the Sleep-EDF dataset, and the experimental results indicate that the proposed TRMSC has achieved better performance than other existing strategies, which proves that the time-related synchronization features can make up for the shortcomings of traditional spectrum-based strategies and achieve a higher classification accuracy. The proposed TRMSC model may be helpful for portable sleep analyzers and provide a new analytical method for clinical sleeping research.

Organic Electrochemical Transistors (OECTs) are integral in detecting human bioelectric signals, attributing their significance to distinct electrochemical properties, the utilization of soft materials, compact dimensions, and pronounced biocompatibility. This review traverses the technological evolution of OECT, highlighting its profound impact on non-invasive detection methodologies within the biomedicalfield. Four sensor types rooted in OECT technology were introduced: Electrocardiogram (ECG), Electroencephalogram (EEG), Electromyography (EMG), and Electrooculography (EOG), which hold promise for integration into wearable detection systems. The fundamental detection principles, material compositions, and functional attributes of these sensors are examined. Additionally, the performance metrics and delineates viable optimization strategies for assorted physiological electrical detection sensors are discussed. The overarching goal of this review is to foster deeper insights into the generation, propagation, and modulation of electrophysiological signals, thereby advancing the application and development of OECT in medical sciences.

Sleep staging is a vital aspect of sleep assessment, serving as a critical tool for evaluating the quality of sleep and identifying sleep disorders. Manual sleep staging is a laborious process, while automatic sleep staging is seldom utilized in clinical practice due to issues related to the inadequate accuracy and interpretability of classification results in automatic sleep staging models. In this work, a hybrid intelligent model is presented for automatic sleep staging, which integrates data intelligence and knowledge intelligence, to attain a balance between accuracy, interpretability, and generalizability in the sleep stage classification. Specifically, it is built on any combination of typical electroencephalography (EEG) and electrooculography (EOG) channels, including a temporal fully convolutional network based on the U-Net architecture and a multi-task feature mapping structure. The experimental results show that, compared to current interpretable automatic sleep staging models, our model achieves a Macro-F1 score of 0.804 on the ISRUC dataset and 0.780 on the Sleep-EDFx dataset. Moreover, we use knowledge intelligence to address issues of excessive jumps and unreasonable sleep stage transitions in the coarse sleep graphs obtained by the model. We also explore the different ways knowledge intelligence affects coarse sleep graphs by combining different sleep graph correction methods. Our research can offer convenient support for sleep physicians, indicating its significant potential in improving the efficiency of clinical sleep staging.

Brain-Computer Interfaces (BCIs) have shown great potential in providing communication and control for individuals with severe motor disabilities. However, traditional BCIs that rely on electroencephalography (EEG) signals suffer from low information transfer rates and high variability across users. Recently, eye movement signals have emerged as a promising alternative due to their high accuracy and robustness. Eye movement signals are the electrical or mechanical signals generated by the movements and behaviors of the eyes, serving to denote the diverse forms of eye movements, such as fixations, smooth pursuit, and other oculomotor activities like blinking. This article presents a review of recent studies on the development of BCI typing systems that incorporate eye movement signals. We first discuss the basic principles of BCI and the recent advancements in text entry. Then, we provide a comprehensive summary of the latest advancements in BCI typing systems that leverage eye movement signals. This includes an in-depth analysis of hybrid BCIs that are built upon the integration of electrooculography (EOG) and eye tracking technology, aiming to enhance the performance and functionality of the system. Moreover, we highlight the advantages and limitations of different approaches, as well as potential future directions. Overall, eye movement signals hold great potential for enhancing the usability and accessibility of BCI typing systems, and further research in this area could lead to more effective communication and control for individuals with motor disabilities.