A cross-scenario and cross-subject domain adaptation method for driving fatigue detection.

Objective.The scarcity of electroencephalogram (EEG) data, coupled with individual and scenario variations, leads to considerable challenges in real-world EEG-based driver fatigue detection. We propose a domain adaptation method that utilizes EEG data collected from a laboratory to supplement real-world EEG data and constructs a cross-scenario and cross-subject driver fatigue detection model for real-world scenarios.Approach.First, we collect EEG data from subjects participating in a driving experiment conducted in both laboratory and real-world scenarios. To address the issue of data scarcity, we build a real-world fatigued driving detection model by integrating the real-world data with the laboratory data. Then, we propose a method named cross-scenario and cross-subject domain adaptation (CS2DA), which aims to eliminate the domain shift problem caused by individual variances and scenario differences. Adversarial learning is adopted to extract the common features observed across different subjects within the same scenario. The multikernel maximum mean discrepancy (MK-MMD) method is applied to further minimize scenario differences. Additionally, we propose a conditional MK-MMD constraint to better utilize label information. Finally, we use seven rules to fuse the predicted labels.Main results.We evaluate the CS2DA method through extensive experiments conducted on the two EEG datasets created in this work: the SEED-VLA and the SEED-VRW datasets. Different domain adaptation methods are used to construct a real-world fatigued driving detection model using data from laboratory and real-world scenarios, as well as a combination of both. Our findings show that the proposed CS2DA method outperforms the existing traditional and adversarial learning-based domain adaptation approaches. We also find that combining data from both laboratory and real-world scenarios improves the performance of the model.Significance.This study contributes two EEG-based fatigue driving datasets and demonstrates that the proposed CS2DA method can effectively enhance the performance of a real-world fatigued driving detection model.

Investigating Emotion EEG Patterns for Depression Detection with Attentive Simple Graph Convolutional Network.

Depression severely limits daily functioning, diminishes quality of life and possibly leads to self-harm and suicide. Noninvasive electroencephalography (EEG) has been shown effective as biomarkers for objective depression diagnose and treatment response prediction, and dry EEG electrodes further extend its availability for clinical use. Even though many efforts have been made to identify depression biomarkers, searching reliable EEG biomarkers for depression detection remains challenging. This work presents a systematic investigation of capabilities of emotion EEG patterns for depression detection using a dry EEG electrode system. We design an emotion elicitation paradigm with happy, neutral and sad emotions and collect EEG signals during film watching from 33 depressed patients and 40 healthy controls. The mean activation levels at frontal and temporal sites in the alpha, beta and gamma bands of the depressed group are different to those of the healthy group, indicating the impacts of depressive symptoms on the emotion experiences. To leverage the topology information among EEG channels for emotion recognition and depression detection, an Attentive Simple Graph Convolutional network is built. The deep depression-health classifier achieves a sensitivity of 81.93% and a specificity of 91.69% on the happy emotions, suggesting the promising use of the emotion neural patterns for distinguishing the depressed patients from the healthy controls.

Objective Depression Detection Using EEG and Eye Movement Signals Induced by Oil Paintings.

Depression is a mental disorder characterized by persistent sadness and loss of interest, which has become one of the leading causes of disability worldwide. There are currently no objective diagnostic standards for depression in clinical practice. Previous studies have shown that depression causes both brain abnormalities and behavioral disorders. In this study, both electroencephalography (EEG) and eye movement signals were used to objectively detect depression. By presenting 40 carefully selected oil paintings-20 positive and 20 negative-as stimuli, we were able to successfully evoke emotions in 48 depressed patients (DPs) and 40 healthy controls (HCs) from three centers. We then used Transformer, a deep learning model, to conduct emotion recognition and depression detection. The experimental results demonstrate that: a) Transformer achieves the best accuracies of 89.21% and 92.19% in emotion recognition and depression detection, respectively; b) The HC group has higher accuracies than the DP group in emotion recognition for both subject-dependent and subject-independent experiments; c) The neural pattern differences do exist between DPs and HCs, and we find the consistent asymmetry of the neural patterns in DPs; d) For depression detection, using single oil painting achieves the best accuracies, and using negative oil paintings has higher accuracies than using positive oil paintings. These findings suggest that EEG and eye movement signals induced by oil paintings can be used to objectively identify depression.

Tagging Continuous Labels for EEG-based Emotion Classification.

EEG-based emotion classification has long been a critical task in the field of affective brain-computer interface (aBCI). The majority of leading researches construct supervised learning models based on labeled datasets. Several datasets have been released, including different kinds of emotions while utilizing various forms of stimulus materials. However, they adopt discrete labeling methods, in which the EEG data collected during the same stimulus material are given a same label. These methods neglect the fact that emotion changes continuously, and mislabeled data possibly exist. The imprecision of discrete labels may hinder the progress of emotion classification in concerned works. Therefore, we develop an efficient system in this paper to support continuous labeling by giving each sample a unique label, and construct a continuously labeled EEG emotion dataset. Using our dataset with continuous labels, we demonstrate the superiority of continuous labeling in emotion classification through experiments on several classification models. We further utilize the continuous labels to identify the EEG features under induced and non-induced emotions in both our dataset and a public dataset. Our experimental results reveal the learnability and generality of the relation between the EEG features and their continuous labels.

The landscape of patellofemoral arthroplasty research: a bibliometric analysis.

PURPOSE: Patellofemoral arthroplasty (PFA) was shown to be a potentially effective surgical technique for isolated patellofemoral osteoarthritis but varying reports on PFA-related implant failure and complications have rendered the procedure controversial. This study aimed to identify impactful publications, research interests/efforts, and collaborative networks in the field of PFA research. METHODS: The study used the Web of Science Core Collection (WOSCC) database, Medline, Springer, BIOSIS Citation Index, and PubMed to retrieve relevant publications on PFA research published between 1950-2022. Statistical tests in R software were used for analysis while VOSviewer, Bibliometrix, and CiteSpace were employed for data visualization. RESULTS: Two hundred forty-one articles were analyzed with the number of published papers increasing over time. Knee was the most frequent journal and Clinical Orthopaedics and Related Research was the most cited journal. Clinical outcomes, such as prosthesis survival, revision, and complications, were researched most frequently as demonstrated by keyword analysis. The United States was the top contributor to cooperative networks, followed by the United Kingdom while Technical University Munich formed close ties among authors. CONCLUSION: Publications on PFA research have witnessed a notable surge. They primarily came from a limited number of centers and were characterized by low-level evidence. The majority of studies primarily focused on the clinical outcomes of PFA, while revision of PFA and patient satisfaction have emerged as new research areas.

Deep Depression Detection with Resting-State and Cognitive-Task EEG.

Depression is a common mental disorder that negatively affects physical health and personal, social and occupational functioning. Currently, accurate and objective diagnosis of depression remains challenging, and electroencephalography (EEG) provides promising clinical practice or home use due to considerable performance and low cost. This work investigates the capabilities of deep neural networks with EEG-based neural patterns from both resting states and cognitive tasks for depression detection. We collect EEG signals from 33 depressed patients and 40 healthy controls using wearable dry electrodes and build Attentive Simple Graph Convolutional network and Transformer neural network for objective depression detection. Four experiment stages, including two resting states and two cognitive tasks, are designed to characterize the alteration of relevant neural patterns in the depressed patients, in terms of decreased energy and impaired performance in sustained attention and response inhibition. The Transformer model achieves an AUC of 0.94 on the Continuous Performance Test-Identical Pairs version (sensitivity: 0.87, specificity: 0.91) and the Stroop Color Word Test (sensitivity: 0.93, specificity: 0.88), and an AUC of 0.89 on the two resting states (sensitivity: 0.85 and 0.87, specificity: 0.88 and 0.90, respectively), indicating the potential of EEG-based neural patterns in identifying depression. These findings provide new insights into the research of depression mechanisms and EEG-based depression biomarkers.

Physically cross-linked organo-hydrogels for friction interfaces in joint replacements: design, evaluation and potential clinical applications.

This paper investigates physically crosslinked organo-hydrogels for total hip replacement surgery. Current materials in artificial joints have limitations in mechanical performance and biocompatibility. To overcome these issues, a new approach based on hydrogen bonds between polyvinyl alcohol, poly(2-hydroxyethyl methacrylate), and glycerin is proposed to develop bioactive organo-hydrogels with improved mechanical properties and biocompatibility. This study analyzes local pathological characteristics, systemic toxicity, and mechanical properties of the gels. The results show that the gels possess excellent biocompatibility and mechanical strength, suggesting their potential as an alternative material for total hip replacement surgery. These findings contribute to improving patient outcomes in joint replacement procedures.

Identifying sex differences in EEG-based emotion recognition using graph convolutional network with attention mechanism.

OBJECTIVE: Sex differences in emotions have been widely perceived via self-reports, peripheral physiological signals and brain imaging techniques. However, how sex differences are reflected in the EEG neural patterns of emotions remains unresolved. In this paper, we detect sex differences in emotional EEG patterns, investigate the consistency of such differences in various emotion datasets across cultures, and study how sex as a factor affects the performance of EEG-based emotion recognition models. APPROACH: We thoroughly assess sex differences in emotional EEG patterns on five public datasets, including SEED, SEED-IV, SEED-V, DEAP and DREAMER, systematically examine the sex-specific EEG patterns for happy, sad, fearful, disgusted and neutral emotions, and implement deep learning models for sex-specific emotion recognition. MAIN RESULTS: (1) Sex differences exist in various emotion types and both Western and Eastern cultures; (2) The emotion patterns of females are more stable than those of males, and the patterns of happiness from females are in sharp contrast with the patterns of sadness, fear and disgust, while the energy levels are more balanced for males; (3) The key features for emotion recognition are mainly located at the frontal and temporal sites for females and distributed more evenly over the whole brain for males, and (4) The same-sex emotion recognition models outperform the corresponding cross-sex models. SIGNIFICANCE: These findings extend efforts to characterize sex differences in emotional brain activation, provide new physiological evidence for sex-specific emotion processing, and reinforce the message that sex differences should be carefully considered in affective research and precision medicine.

Tracing the evolution of robotic-assisted total knee arthroplasty: a bibliometric analysis of the top 100 highly cited articles.

Robotic-assisted surgical systems hold promise in enhancing total knee arthroplasty (TKA) outcomes and patients' quality of life. This study aims to comprehensively analyze the literature on robot-assisted total knee arthroplasty (r-TKA), providing insights into its current development, clinical application, and research trends. A systematic search was conducted in the Web of Science Core Collection (WOSCC) to identify relevant articles. Data were collected from the top 100 highly cited articles. Article evidence levels were assessed following established guidelines. Statistical analyses and visualizations were performed to reveal publication trends, citations, research hotspots, and collaborative networks. The analysis covered 100 highly cited articles meeting the research criteria, with a focus on the last five years. The United States emerged as a major contributor, with most publications and citations in the Journal of Knee Surgery and Knee Surgery Sports Traumatology Arthroscopy. Research priorities revolved around clinical outcomes, accuracy, and alignment of r-TKA. Notably, higher evidence levels correlated with more citations, indicating greater attention. Interest in and research on r-TKA is steadily increasing, with a few countries at the forefront of these endeavors. While numerous studies have already reported short- to medium-term follow-up results, it is crucial to conduct longer-term investigations to gain a more comprehensive understanding of the clinical benefits that r-TKA offers compared to conventional techniques. Through ongoing research and a greater embrace of robotic technology, we can continue to improve the quality of life for patients undergoing knee arthroplasty.

Metformin reverses impaired osteogenesis due to hyperglycemia-induced neutrophil extracellular traps formation.

Diabetic patients suffer from delayed fracture healing and impaired osteogenic function, but the underlying pathophysiological mechanisms are not fully understood. Neutrophil extracellular traps (NETs) formed by neutrophils in high glucose microenvironments affect the healing of wounds and other tissues. Some evidence supports that NETs may inhibit osteogenic processes in the microenvironment through sustained inflammatory activation. In this study, we observed that high glucose-induced NETs led to sustained inflammatory activation of macrophages. Pro-inflammatory NETs inhibited the osteogenic function of osteoblasts in vitro. A bone defect healing model based on diabetic rat animal models confirmed that bone healing was impaired in a high glucose environment, but this process could be reversed by DNase I, a NETs clearance agent. More importantly, the classic hypoglycemic drug metformin had a similar antagonistic effect as DNase I and could reverse the inhibitory effect of NETs on osteogenesis in a high-glucose environment. In summary, we found that NETs formation induced by high glucose microenvironment is a potential cause of osteogenic dysfunction in diabetic patients, and metformin can reverse this osteogenic disadvantage.

Inflammation-Responsive Hydrogel Spray for Synergistic Prevention of Traumatic Heterotopic Ossification via Dual-Homeostatic Modulation Strategy.

Traumatic heterotopic ossification (THO) represents one of the most prominent contributors to post-traumatic joint dysfunction, which currently lacks an effective and definitive preventative approach. Inflammatory activation due to immune dyshomeostasis during the early stages of trauma is believed to be critical in initiating the THO disease process. This study proposes a dual-homeostatic modulation (DHM) strategy to synergistically prevent THO without compromising normal trauma repair by maintaining immune homeostasis and inducing stem cell homeostasis. A methacrylate-hyaluronic acid-based hydrogel spray device encapsulating a curcumin-loaded zeolitic imidazolate framework-8@ceric oxide (ZIF-8@CeO2, CZC) nanoparticles (CZCH) is designed. Photo-crosslinked CZCH is used to form hydrogel films fleetly in periosteal soft tissues to achieve sustained curcumin and CeO2 nanoparticles release in response to acidity and reactive oxygen species (ROS) in the inflammatory microenvironment. In vitro experiments and RNA-seq results demonstrated that CZCH achieved dual-homeostatic regulation of inflammatory macrophages and stem cells through immune repolarization and enhanced efferocytosis, maintaining immune cell homeostasis and normal differentiation. These findings of the DHM strategy are also validated by establishing THO mice and rat models. In conclusion, the CZCH hydrogel spray developed based on the DHM strategy enables synergistic THO prevention, providing a reference for a standard procedure of clinical operations.

Multimodal Emotion Recognition in Response to Oil Paintings.

Most previous affective studies use facial expression pictures, music or movie clips as emotional stimuli, which are either too simplified without contexts or too dynamic for emotion annotations. In this work, we evaluate the effectiveness of oil paintings as stimuli. We develop an emotion stimuli dataset with 114 oil paintings selected from subject ratings to evoke three emotional states (i.e., negative, neutral and positive), and acquire both EEG and eye tracking data from 20 subjects while watching the oil paintings. Furthermore, we propose a novel affective model for multimodal emotion recognition by 1) extracting informative features of EEG signals from both the time domain and the frequency domain, 2) exploring topological information embedded in EEG channels with graph neural networks (GNNs), and 3) combining EEG and eye tracking data with a deep autoencoder neural network. From the exper-iments, our model obtains an averaged classification accuracy of 94.72 % ± 1.47 %, which demonstrates the feasibility of using oil paintings as emotion elicitation material.

Exploring Sex Differences in Key Frequency Bands and Channel Connections for EEG-based Emotion Recognition.

Previous studies have demonstrated the existence of sex differences in emotion recognition by comparing the performance of same-sex and cross-sex training strategies. However, the EEG properties behind the sex differences have not been fully explored. To fill this research gap, we aim to investigate the sex differences in key frequency bands and channel connections of EEG signals. The single-modality attentive simple graph convolutional network (ASGC) is applied to three datasets SEED, SEED-IV and SEED-V under subject-dependence conditions. The classification rates are 90.86 ±4.84%, 83.14 ± 8.84% and 78.33±7.83%, respectively. The adjacency matrices learned by ASGC indicate that females and males have similar channel-connection patterns, but the degree of importance of channel connections varies by sex. Additionally, by comparing the classification results of 5 frequency bands, we find that males and females represent similar frequency band characteristics, i.e., high-frequency bands achieve better performance, indicating that these frequency bands are more related to emotion processing. Finally, we conduct the cross-subject experiment using ASGC and find that the same-sex strategy outperforms the cross-sex strategy, which is consistent with previous studies. The results also imply that males may be more suitable for sex generalization. However, this finding needs the support of more samples and advanced algorithms.

A Cross-modality Deep Learning Method for Measuring Decision Confidence from Eye Movement Signals.

Electroencephalography (EEG) signals can effectively measure the level of human decision confidence. However, it is difficult to acquire EEG signals in practice due to the ex-pensive cost and complex operation, while eye movement signals are much easier to acquire and process. To tackle this problem, we propose a cross-modality deep learning method based on deep canoncial correlation analysis (CDCCA) to transform each modality separately and coordinate different modalities into a hyperspace by using specific canonical correlation analysis constraints. In our proposed method, only eye movement signals are used as inputs in the test phase and the knowledge from EEG signals is learned in the training stage. Experimental results on two human decision confidence datasets demonstrate that our proposed method achieves advanced performance compared with the existing single-modal approaches trained and tested on eye movement signals and maintains a competitive accuracy in comparison with multimodal models.

OGSSL: A Semi-Supervised Classification Model Coupled With Optimal Graph Learning for EEG Emotion Recognition.

Electroencephalogram(EEG) signals are generated from central nervous system which are difficult to disguise, leading to its popularity in emotion recognition. Recently,semi-supervisedlearning exhibits promisingemotion recognition performance by involving unlabeled EEG data into model training. However, if we first build a graph to characterize the sample similarities and then perform label propagation on this graph, these two steps cannotwell collaborate with each other. In this paper, we propose an OptimalGraph coupledSemi-Supervised Learning (OGSSL) model for EEG emotion recognition by unifying the adaptive graph learning and emotion recognition into a single objective. Besides, we improve the label indicator matrix of unlabeledsamples in order to directly obtain theiremotional states. Moreover, the key EEG frequency bands and brain regions in emotion expression are automatically recognized by the projectionmatrix of OGSSL. Experimental results on the SEED-IV data set demonstrate that 1) OGSSL achieves excellent average accuracies of 76.51%, 77.08% and 81.29% in three cross-sessionemotion recognition tasks, 2) OGSSL is competent for discriminative EEG feature selection in emotion recognition, and 3) the Gamma frequency band, the left/righttemporal, prefrontal,and (central) parietal lobes are identified to be more correlated with the occurrence of emotions.

Identifying similarities and differences in emotion recognition with EEG and eye movements among Chinese, German, and French People.

Objective.Cultures have essential influences on emotions. However, most studies on cultural influences on emotions are in the areas of psychology and neuroscience, while the existing affective models are mostly built with data from the same culture. In this paper, we identify the similarities and differences among Chinese, German, and French individuals in emotion recognition with electroencephalogram (EEG) and eye movements from an affective computing perspective.Approach.Three experimental settings were designed: intraculture subject dependent, intraculture subject independent, and cross-culture subject independent. EEG and eye movements are acquired simultaneously from Chinese, German, and French subjects while watching positive, neutral, and negative movie clips. The affective models for Chinese, German, and French subjects are constructed by using machine learning algorithms. A systematic analysis is performed from four aspects: affective model performance, neural patterns, complementary information from different modalities, and cross-cultural emotion recognition.Main results.From emotion recognition accuracies, we find that EEG and eye movements can adapt to Chinese, German, and French cultural diversities and that a cultural in-group advantage phenomenon does exist in emotion recognition with EEG. From the topomaps of EEG, we find that theγandßbands exhibit decreasing activities for Chinese, while for German and French,θandαbands exhibit increasing activities. From confusion matrices and attentional weights, we find that EEG and eye movements have complementary characteristics. From a cross-cultural emotion recognition perspective, we observe that German and French people share more similarities in topographical patterns and attentional weight distributions than Chinese people while the data from Chinese are a good fit for test data but not suitable for training data for the other two cultures.Significance.Our experimental results provide concrete evidence of the in-group advantage phenomenon, cultural influences on emotion recognition, and different neural patterns among Chinese, German, and French individuals.

Investigating EEG-based functional connectivity patterns for multimodal emotion recognition.

Objective.Previous studies on emotion recognition from electroencephalography (EEG) mainly rely on single-channel-based feature extraction methods, which ignore the functional connectivity between brain regions. Hence, in this paper, we propose a novel emotion-relevant critical subnetwork selection algorithm and investigate three EEG functional connectivity network features: strength, clustering coefficient, and eigenvector centrality.Approach.After constructing the brain networks by the correlations between pairs of EEG signals, we calculated critical subnetworks through the average of brain network matrices with the same emotion label to eliminate the weak associations. Then, three network features were conveyed to a multimodal emotion recognition model using deep canonical correlation analysis along with eye movement features. The discrimination ability of the EEG connectivity features in emotion recognition is evaluated on three public datasets: SEED, SEED-V, and DEAP.Main results. The experimental results reveal that the strength feature outperforms the state-of-the-art features based on single-channel analysis. The classification accuracies of multimodal emotion recognition are95.08±6.42%on the SEED dataset,84.51±5.11%on the SEED-V dataset, and85.34±2.90%and86.61±3.76%for arousal and valence on the DEAP dataset, respectively, which all achieved the best performance. In addition, the brain networks constructed with 18 channels achieve comparable performance with that of the 62-channel network and enable easier setups in real scenarios.Significance.The EEG functional connectivity networks combined with emotion-relevant critical subnetworks selection algorithm we proposed is a successful exploration to excavate the information between channels.

Cross-session Emotion Recognition by Joint Label-common and Label-specific EEG Features Exploration.

Since Electroencephalogram (EEG) is resistant to camouflage, it has been a reliable data source for objective emotion recognition. EEG is naturally multi-rhythm and multi-channel, based on which we can extract multiple features for further processing. In EEG-based emotion recognition, it is important to investigate whether there exist some common features shared by different emotional states, and the specific features associated with each emotional state. However, such fundamental problem is ignored by most of the existing studies. To this end, we propose a Joint label-Common and label-Specific Features Exploration (JCSFE) model for semi-supervised cross-session EEG emotion recognition in this paper. To be specific, JCSFE imposes the ℓ2,1-norm on the projection matrix to explore the label-common EEG features and simultaneously the ℓ1-norm is used to explore the label-specific EEG features. Besides, a graph regularization term is introduced to enforce the data local invariance property, i.e., similar EEG samples are encouraged to have the same emotional state. Results obtained from the SEED-IV and SEED-V emotional data sets experimentally demonstrate that JCSFE not only achieves superior emotion recognition performance in comparison with the state-of-the-art models but also provides us with a quantitative method to identify the label-common and label-specific EEG features in emotion recognition.

Multimodal Vigilance Estimation Using Deep Learning.

The phenomenon of increasing accidents caused by reduced vigilance does exist. In the future, the high accuracy of vigilance estimation will play a significant role in public transportation safety. We propose a multimodal regression network that consists of multichannel deep autoencoders with subnetwork neurons (MCDAE sn ). After we define two thresholds of "0.35" and "0.70" from the percentage of eye closure, the output values are in the continuous range of 0-0.35, 0.36-0.70, and 0.71-1 representing the awake state, the tired state, and the drowsy state, respectively. To verify the efficiency of our strategy, we first applied the proposed approach to a single modality. Then, for the multimodality, since the complementary information between forehead electrooculography and electroencephalography features, we found the performance of the proposed approach using features fusion significantly improved, demonstrating the effectiveness and efficiency of our method.

A Novel Experiment Setting for Cross-subject Emotion Recognition.

Recently, cross-subject emotion recognition attracts widespread attention. The current emotional experiments mainly use video clips of different emotions as stimulus materials, but the videos watched by different subjects are the same, which may introduce the same noise pattern in the collected data. However, the traditional experiment settings for cross-subject emotion recognition models couldn't eliminate the impact of same video clips on recognition results, which may lead to a bias on classification. In this paper, we propose a novel experiment setting for cross-subject emotion recognition. We evaluate different experiment settings on four public emotion datasets, DEAP, SEED, SEED-IV and SEED-V. The experimental results demonstrate the deficiencies of the traditional experiment settings and the advantages of our proposed experiment setting.