DSFE: Decoding EEG-based finger motor imagery using feature-dependent frequency, feature fusion and ensemble learning.

Accurate decoding finger motor imagery is essential for fine motor control using EEG signals. However, decoding finger motor imagery is particularly challenging compared with ordinary motor imagery. This paper proposed a novel EEG decoding method of featuredependent frequency band selection, feature fusion, and ensemble learning (DSFE) for finger motor imagery. First, a feature-dependent frequency band selection method based on correlation coefficient (FDCC) was proposed to select feature-specific effective bands. Second, a feature fusion method was proposed to fuse different types of candidate features to produce multiple refined sets of decoding features. Finally, an ensemble model using the weighted voting strategy was proposed to make full use of these diverse sets of final features. The results on a public EEG dataset of five fingers motor imagery showed that the DSFE method is effective and achieves the highest decoding accuracy of 50.64%, which is 7.64% higher than existing studies using exactly the same data. The experiments further revealed that both the effective frequency bands of different subjects and the effective frequency bands of different types of features are different in finger motor imagery. Furthermore, compared with two-hand motor imagery, the effective decoding information of finger motor imagery is transferred to the lower frequency. The idea and findings in this paper provide a valuable perspective for understanding fine motor imagery in-depth.

Automatically Extracting and Utilizing EEG Channel Importance Based on Graph Convolutional Network for Emotion Recognition.

Graph convolutional network (GCN) based on the brain network has been widely used for EEG emotion recognition. However, most studies train their models directly without considering network dimensionality reduction beforehand. In fact, some nodes and edges are invalid information or even interference information for the current task. It is necessary to reduce the network dimension and extract the core network. To address the problem of extracting and utilizing the core network, a core network extraction model (CWGCN) based on channel weighting and graph convolutional network and a graph convolutional network model (CCSR-GCN) based on channel convolution and style-based recalibration for emotion recognition have been proposed. The CWGCN model automatically extracts the core network and the channel importance parameter in a data-driven manner. The CCSR-GCN model innovatively uses the output information of the CWGCN model to identify the emotion state. The experimental results on SEED show that: (1) the core network extraction can help improve the performance of the GCN model; (2) the models of CWGCN and CCSR-GCN achieve better results than the currently popular methods. The idea and its implementation in this paper provide a novel and successful perspective for the application of GCN in brain network analysis of other specific tasks. The code is available at https://github.com/ykhdu/CWGCN-CCSR-GCN.

Combining Early Ischemic Change and Collateral Extent for Functional Outcomes After Endovascular Therapy: An Analysis From AcT Trial.

BACKGROUND: Early ischemic change and collateral extent are colinear with ischemic core volume (ICV). We investigated the relationship between a combined score using the Alberta Stroke Program Early Computed Tomography Score and multiphase computed tomography angiography (mCTA) collateral extent, named mCTA-ACE score, on functional outcomes in endovascular therapy-treated patients. METHODS: We performed a post hoc analysis of a subset of endovascular therapy-treated patients from the Alteplase Compared to Tenecteplase trial which was conducted between December 2019 and January 2022 at 22 centers across Canada. Ten-point mCTA collateral corresponding to M2 to M6 regions of the Alberta Stroke Program Early Computed Tomography Score grid was evaluated as 0 (poor), 1 (moderate), or 2 (normal) and additively combined with the 10-point Alberta Stroke Program Early Computed Tomography Score to produce a 20-point mCTA-ACE score. We investigated the association of mCTA-ACE score with modified Rankin Scale score ≤2 and return to prestroke level of function at 90 to 120 days using mixed-effects logistic regression. In the subset of patients who underwent baseline computed tomography perfusion imaging, we compared the mCTA-ACE score and ICV for outcome prediction. RESULTS: Among 1577 intention-to-treat population in the trial, 368 (23%; 179 men; median age, 73 years) were included, with Alberta Stroke Program Early Computed Tomography Score, mCTA collateral, and combination of both (mCTA-ACE score: median [interquartile range], 8 [7-10], 9 [8-10], and 17 [16-19], respectively). The probability of modified Rankin scale score ≤2 and return to prestroke level of function increased for each 1-point increase in mCTA-ACE score (odds ratio, 1.16 [95% CI, 1.06-1.28] and 1.22 [95% CI, 1.06-1.40], respectively). Among 173 patients in whom computed tomography perfusion data was assessable, the mCTA-ACE score was inversely correlated with ICV (ρ=-0.46; P<0.01). The mCTA-ACE score was comparable to ICV to predict a modified Rankin scale score ≤2 and return to prestroke level of function (C statistics 0.71 versus 0.69 and 0.68 versus 0.64, respectively). CONCLUSIONS: The mCTA-ACE score had a significant positive association with functional outcomes after endovascular therapy and had a similar predictive performance as ICV.

Bifidobacterium Relieved Fluoride-Induced Hepatic and Ileal Toxicity via Inflammatory Response and Bile Acid Transporters in Mice.

Fluoride is a pervasive environmental contaminant. Prolonged excessive fluoride intake can inflict severe damage on the liver and intestines. Previous 16S rDNA sequencing revealed a decrease in ileal Bifidobacterium abundance during fluoride-induced hepatointestinal injury. Hence, this work aimed to investigate the possible mitigating function of Bifidobacterium on hepatointestinal injury caused by fluoride. Thirty-six 6-week-old C57BL/6J mice (equally divided between males and females) were allotted randomly to three groups: Ctrl group (distilled water), NaF group, and NaF + Ba group (100 mg/L NaF distilled water). After 10 weeks, the mice were given 1 × 109 CFU/mL Bifidobacterium solution (0.2 mL/day) intragastrically in the NaF + Ba group for 8 weeks, and the mice in other groups were given the same amount of distilled water. Dental damage, bone fluoride content, blood routine, liver and intestinal microstructure and function, inflammatory factors, and regulatory cholic acid transporters were examined. Our results showed that fluoride increased glutamic-oxalacetic transaminase (GOT), glutamic-pyruvic transaminase (GPT) activities, and the levels of lipopolysaccharide (LPS), IL-1ß, IL-6, TNF-α, and IL-10 levels in serum, liver, and ileum. However, Bifidobacterium intervention alleviated fluoride-induced changes in the above indicators. In addition, Bifidobacterium reduced the mRNA expression levels of bile acid transporters ASBT, IBABP, OST-α, and OST-ß in the ileum. In summary, Bifidobacterium supplementation relieved fluoride-induced hepatic and ileal toxicity via an inflammatory response and bile acid transporters in the liver and ileum of mice.

Fluoride induces pyroptosis via IL-17A-mediated caspase-1/11-dependent pathways and Bifidobacterium intervention in testis.

Fluoride, a ubiquitous environmental pollutant, poses a significant public health threat. Our previous study revealed a correlation between fluoride-induced testicular pyroptosis and male reproductive dysfunction. However, the underlying mechanism remains unclear. Wild-type and interleukin 17A knockout mice were exposed to sodium fluoride (100 mg/L) in deionized drinking water for 18 weeks. Bifidobacterium intervention (1 × 109 CFU/mL, 0.2 mL/day, administered via gavage) commenced in the 10th week. Sperm quality, testicular morphology, key pyroptosis markers, spermatogenesis key genes, IL-17A signaling pathway, and pyroptosis pathway related genes were determined. The results showed that fluoride reduced sperm quality, damaged testicular morphology, affected spermatogenesis, elevated IL-17A levels, and induced testicular pyroptosis. Bifidobacterium intervention alleviated adverse reproductive outcomes. Fluoride-activated testicular pyroptosis through both typical and atypical pathways, with IL-17A involvement. Bifidobacterium supplementation attenuated pyroptosis by downregulating IL-17A, inhibiting NLRP3 and PYRIN-mediated caspase-1 and caspase-11 dependent pathways in testis, thereby alleviating fluoride-induced male reproductive damage. In summary, this study uncovers the mechanism underlying fluorine-induced testicular pyroptosis and illustrates the novel protecting feature of Bifidobacterium against fluoride-induced harm to male reproduction, along with its potential regulatory mechanism. These results provide fresh perspectives on treating male reproductive dysfunction resulting from fluoride or other environmental toxins.

Riboflavin Attenuates Fluoride-Induced Testicular Injury via Interleukin 17A-Mediated Classical Pyroptosis.

Male reproductive toxicity of fluoride is of great concern worldwide, yet the underlying mechanism is unclear. Pyroptosis is a novel mode of inflammatory cell death, and riboflavin with anti-inflammatory properties has the potential to protect against fluoride damage. However, it is unknown whether pyroptosis is involved in fluoride-induced testicular injury and riboflavin intervention. Here, we first found that riboflavin could alleviate fluoride-caused lower sperm quality and damaged testicular morphology by reducing pyroptosis based on a model of ICR mice treated with NaF (100 mg/L) and/or riboflavin supplementation (40 mg/L) via drinking water for 13 weeks. And then, together with the results of in vitro Leydig cell modelsm it was confirmed that the pyroptosis occurs predominantly through classical NLRP3/Caspase-1/GSDMD pathway. Furthermore, our results reveal that interleukin-17A mediates the process of pyroptosis in testes induced by fluoride and riboflavin attenuation according to the results of our established models of riboflavin- and/or fluoride-treated IL-17A knockout mice. The results not only declare a new mechanism by which fluoride induces testicular injury via interleukin 17A-mediated classical pyroptosis but also provide evidence for the potential clinical application of riboflavin as an effective therapy for fluoride toxicity.

spatialHeatmap: visualizing spatial bulk and single-cell assays in anatomical images.

Visualizing spatial assay data in anatomical images is vital for understanding biological processes in cell, tissue, and organ organizations. Technologies requiring this functionality include traditional one-at-a-time assays, and bulk and single-cell omics experiments, including RNA-seq and proteomics. The spatialHeatmap software provides a series of powerful new methods for these needs, and allows users to work with adequately formatted anatomical images from public collections or custom images. It colors the spatial features (e.g. tissues) annotated in the images according to the measured or predicted abundance levels of biomolecules (e.g. mRNAs) using a color key. This core functionality of the package is called a spatial heatmap plot. Single-cell data can be co-visualized in composite plots that combine spatial heatmaps with embedding plots of high-dimensional data. The resulting spatial context information is essential for gaining insights into the tissue-level organization of single-cell data, or vice versa. Additional core functionalities include the automated identification of biomolecules with spatially selective abundance patterns and clusters of biomolecules sharing similar abundance profiles. To appeal to both non-expert and computational users, spatialHeatmap provides a graphical and a command-line interface, respectively. It is distributed as a free, open-source Bioconductor package (https://bioconductor.org/packages/spatialHeatmap) that users can install on personal computers, shared servers, or cloud systems.

A time segment adaptive optimization method based on separability criterion and correlation analysis for motor imagery BCIs.

Motor imagery (MI) plays a crucial role in brain-computer interface (BCI), and the classification of MI tasks using electroencephalogram (EEG) is currently under extensive investigation. During MI classification, individual differences among subjects in terms of response and time latency need to be considered. Optimizing the time segment for different subjects can enhance subsequent classification performance. In view of the individual differences of subjects in motor imagery tasks, this article proposes a Time Segment Adaptive Optimization method based on Separability criterion and Correlation analysis (TSAOSC). The fundamental principle of this method involves applying the separability criterion to various sizes of time windows within the training data, identifying the optimal raw reference signal, and adaptively adjusting the time segment position for each trial's data by analyzing its relationship with the optimal reference signal. We evaluated our method on three BCI competition datasets, respectively. The utilization of the TSAOSC method in the experiments resulted in an enhancement of 4.90% in average classification accuracy compared to its absence. Additionally, building upon the TSAOSC approach, this study proposes a Nonlinear-TSAOSC method (N-TSAOSC) for analyzing EEG signals with nonlinearity, which shows improvements in the classification accuracy of certain subjects. The results of the experiments demonstrate that the proposed method is an effective time segment optimization method, and it can be integrated into other algorithms to further improve their accuracy.

Riboflavin alleviates fluoride-induced ferroptosis by IL-17A-independent system Xc-/GPX4 pathway and iron metabolism in testicular Leydig cells.

Fluoride is widely found in groundwater, soil, animal and plant organisms. Excessive fluoride exposure can cause reproductive dysfunction by activating IL-17A signaling pathway. However, the adverse effects of fluoride on male reproductive system and the mechanisms remain elusive. In this study, the wild type and IL-17A knockout C57BL/6J mouse were treated with 24 mg/kg·bw·d sodium fluoride and/or 5 mg/kg·bw·d riboflavin-5'-phosphate sodium for 91 days. Results showed that fluoride caused dental fluorosis, increased the levels of ROS in testicular Leydig cells and GSSG in testicular tissue, and did not affect the iron and serum hepcidin levels in testicular tissue. Riboflavin alleviated above adverse changes, whereas IL-17A does not participate in the oxidative stress-mediated reproductive toxicity of fluoride. Based on this, TM3 cells were used to verify the injury of fluoride on Leydig cells. Results showed that fluoride increased mRNA levels of ferroptosis marker SLC3A2, VDAC3, TFRC, and SLC40A1 and decreased Nrf2 mRNA levels in TM3 cells. The ferroptosis inhibitor Lip-1 and DFO were used to further investigate the relationship between male reproductive toxicity and ferroptosis induced by fluoride. We found that the fluoride-induced decrease in cell viability, increase in xCT, TFRC, and FTH protein expression, and decrease in GPX4 protein expression, can all be rescued by Lip-1 and DFO. Similar results were also observed in the riboflavin treatment group. Moreover, riboflavin mitigated fluoride-induced increases in ROS levels and SLC3A2 protein levels. In all, our work revealed that riboflavin inhibited ferroptosis in testicular Leydig cells and improved the declined male reproductive function caused by fluoride. This study provides new perspectives for revealing new male reproductive toxicity mechanisms and mitigating fluoride toxicity damage.

Transfer learning with data alignment and optimal transport for EEG based motor imagery classification.

Objective. The non-stationarity of electroencephalogram (EEG) signals and the variability among different subjects present significant challenges in current Brain-Computer Interfaces (BCI) research, which requires a time-consuming specific calibration procedure to address. Transfer Learning (TL) offers a potential solution by leveraging data or models from one or more source domains to facilitate learning in the target domain, so as to address these challenges.Approach. In this paper, a novel Multi-source domain Transfer Learning Fusion (MTLF) framework is proposed to address the calibration problem. Firstly, the method transforms the source domain data with the resting state segment data, in order to decrease the differences between the source domain and the target domain. Subsequently, feature extraction is performed using common spatial pattern. Finally, an improved TL classifier is employed to classify the target samples. Notably, this method does not require the label information of target domain samples, while concurrently reducing the calibration workload.Main results. The proposed MTLF is assessed on Datasets 2a and 2b from the BCI Competition IV. Compared with other algorithms, our method performed relatively the best and achieved mean classification accuracy of 73.69% and 70.83% on Datasets 2a and 2b respectively.Significance.Experimental results demonstrate that the MTLF framework effectively reduces the discrepancy between the source and target domains and acquires better classification performance on two motor imagery datasets.

Exploring the mechanism of Jingshen Xiaoke decoction in treating T2DM mice based on network pharmacology and molecular docking.

BACKGROUND: Jingshen Xiaoke decoction (JS) was prepared by studying the classic prescriptions of famous scholars in the past dynasties to prevent and treat diabetes. The related mechanism of JS against hyperlipidemia has yet to be revealed. OBJECTIVE: To investigate the mechanism of action of JS in treating diabetes mellitus by using bioinformatics methods. METHODS: A database was used to search the active ingredients and targets of the JS and targets for type 2 diabetes mellitus (T2DM). The protein interaction between the intersection targets, and the constructed the PPI network diagram was analyzed using the STRING database. Furthermore, the gene annotation tool DAVID was used to enrich the intersecting targets for the Gene ontology (GO) function and Kyoto encyclopedia of genes and genomes (KEGG) signaling pathway. Finally, Maestro software was used for molecular docking to verify the binding ability of the active ingredients to the core target genes. RESULTS: A total of 45 active ingredients in JS were screened out corresponding to 239 effective targets, of which 64 targets were potential targets for treating T2DM. The analysis of PPI network diagram analysis revealed that the ingredients' active components are quercetin, ß-sitosterol, stigmasterol, luteolin, and 7-Methoxy-2-methyl isoflavone. GO functional enrichment analysis indicated 186 biological processes (BP), 23 molecular functions (MF) and 13 cellular components (CC). KEGG pathway enrichment analysis revealed the enrichment of 59 signal pathways. The molecular docking results demonstrated that the active ingredients and core targets had a good docking affinity with a binding activity less than -7 kcal/mol. Finally, the western blotting illustrated that JS could up-regulate the liver PI3K/AKT-signaling pathway. CONCLUSION: JS can regulate glucolipid metabolism, reduce the inflammatory response, improve insulin resistance and modulate the immune response through PI3K/AKT signaling pathway treating of T2DM and its complications effects.

Instance-representation transfer method based on joint distribution and deep adaptation for EEG emotion recognition.

Electroencephalogram (EEG) emotion recognition technology is essential for improving human-computer interaction. However, the practical application of emotion recognition technology is limited due to the variety of subjects and sessions. Transfer learning has been applied to address this issue and has received extensive research and application. Studies mainly concentrate on either instance transfer or representation transfer methods. This paper proposes an emotion recognition method called Joint Distributed Instances Represent Transfer (JD-IRT), which includes two core components: Joint Distribution Deep Adaptation (JDDA) and Instance-Representation Transfer (I-RT). Specifically, JDDA is different from common representation transfer methods in transfer learning. It bridges the discrepancies of marginal and conditional distributions simultaneously and combines multiple adaptive layers and kernels for deep domain adaptation. On the other hand, I-RT utilizes instance transfer to select source domain data for better representation transfer. We performed experiments and compared them with other representative methods in the SEED, SEED-IV, and SEED-V datasets. In cross-subject experiments, our approach achieved an average accuracy of 83.21% in SEED, 52.12% in SEED-IV, and 60.17% in SEED-V. Similarly, in cross-session experiments, the accuracy was 91.29% in SEED, 59.02% in SEED-IV, and 65.91% in SEED-V. These results demonstrate the improvement in the accuracy of EEG emotion recognition using the proposed approach.

The criterion for dividing the surrounding rock EDZs of underground caverns based on the energy dissipation degree.

An energy calculation parameter named the energy dissipation degree (RUd) is introduced based on the analysis of the energy dissipation mechanism and energy evolution characteristics during conventional triaxial tests of the granite of Shuangjiangkou. The deviatoric stress‒strain curve of rock can be divided into five stages using four stress thresholds (crack closure stress σcc, crack initiation stress σci, damage stress σcd and peak stress σp), which also correspond to the four RUd thresholds (RUdc, RUdi, RUdd and RUdp) on the energy dissipation degree-strain curve. A given stress threshold increases with increasing confining pressure; however, a given RUd threshold is basically stable under different confining pressures. Then, a new criterion for dividing the excavation damaged zones (EDZs) in the rock surrounding underground caverns based on the monotonically increasing characteristics of the energy dissipation degree‒axial strain relationship curve is proposed, and it allows for the classification of the surrounding rock into five types of zones through quantitative analysis of the RUd thresholds. Based on the criterion for dividing the EDZs of the surrounding rock mass of the underground cavern, the EDZs of the surrounding rocks of the underground cavern group of the Shuangjiangkou Hydropower Station are analyzed. The distribution characteristics of the EDZs of the rock surrounding underground caverns obtained by numerical simulation calculations based on RUd are basically the same as those obtained by in situ elastic wave tests. However, the RUd-based method for classifying the EDZs of the surrounding rock has the obvious advantage of being able to probe the boundaries of the undamaged zone (UDZ) of the surrounding rock more explicitly, while the method based on wave velocity testing is not sufficiently explicit. The damage zoning of the surrounding rock based on RUd can provide support design advice for the excavation of the surrounding rock, such as the support method, the length of the free section and anchor section of the prestressing anchor, etc.

Impact of vessel tortuosity and radiological thrombus characteristics on the choice of first-line thrombectomy strategy: Results from the ESCAPE-NA1 trial.

INTRODUCTION: Despite improvements in device technology, only one-third of stroke patients undergoing endovascular thrombectomy (EVT) achieve first-pass effect (FPE). We investigated the effect of arterial tortuosity and thrombus characteristics on the relationship between first-line EVT strategy and angiographic outcomes. PATIENTS AND METHODS: Patients with thin-slice baseline CT-angiography from the ESCAPE-NA1 trial (Efficacy and safety of nerinetide for the treatment of acute ischemic stroke) were included. Tortuosity was estimated using the tortuosity index extracted from catheter pathway, and radiological thrombus characteristics were length, non-contrast density, perviousness and hyperdense artery sign. We assessed the association of first-line EVT strategy (stent-retriever [SR] versus contact aspiration [CA] versus combined SR+CA) with FPE (eTICI score 2c/3 after one pass), final eTICI 2b/3, number of passes and procedure duration using multivariable regression. Interaction of tortuosity and thrombus characteristics with first-line technique were assessed using interaction terms. RESULTS: Among 520 included patients, SR as a first-line modality was used in 165 (31.7%) patients, CA in 132 (25.4%), and combined SR+CA in 223 (42.9%). FPE was observed in 166 patients (31.9%). First-line strategy was not associated with FPE. Tortuosity had a significant effect on FPE only in the CA group (aOR = 0.90 [95% CI 0.83-0.98]) compared with stent-retrievers and combined first-line approach (p interaction = 0.03). There was an interaction between thrombus length and first-line strategy for number of passes (p interaction = 0.04). Longer thrombi were associated with higher number of passes only in the CA group (acOR 1.03 [95% CI 1.00-1.06]). CONCLUSION: Our study suggests that vessel tortuosity and longer thrombi may negatively affect the performance of first-line contact aspiration catheters in acute stroke patients undergoing EVT.

Melatonin alleviated fluoride-induced impairment of spermatogenesis and sperm maturation process via Interleukin-17A.

Fluoride-induced male reproductive failure is a major environmental and human health concern, but interventions are still lacking. Melatonin (MLT) has potential functions in regulating testicular damage and interleukin-17 (IL-17) production. This study aims to explore whether MLT can mitigate fluoride-induced male reproductive toxicity through IL-17A, and screen the potential targets. So the wild type and IL-17A knockout mice were employed and treated with sodium fluoride (100 mg/L) by drinking water and MLT (10 mg/kg.BW, intraperitoneal injection per two days starting from week 16) for 18 weeks. Bone F- concentrations, grade of dental damage, sperm quality, spermatogenic cells counts, histological morphology of testis and epididymis, and the mRNA expression of spermatogenesis and maturation, classical pyroptosis related and immune factor genes were detected respectively. The results revealed that MLT supplementations alleviated fluoride-induced impairment of spermatogenesis and maturation process, protecting the morphology of testis and epididymis through IL-17A pathway, and Tesk1 and Pten were identified as candidate targets from 29 regulation genes. Taken together, this study demonstrated a new physiological role for MLT in the protection against fluoride-induced reproductive injury and possible regulation mechanisms, which providing a useful therapeutic strategy for male reproductive function failure caused by fluoride or other environmental pollutants.

ULFAC-Net: Ultra-Lightweight Fully Asymmetric Convolutional Network for Skin Lesion Segmentation.

Segmentation of skin lesions is a critical step in the process of skin lesion diagnosis. Such segmentation is challenging due to the irregular shape, fuzzy contours and severe noise interference in the skin lesion region. Existing deep learning-based skin lesion segmentation methods are usually computationally expensive, hindering their deployment in dermoscopic devices with poor computational power. To address these challenges, we propose an ultralightweight fully asymmetric convolutional network for skin lesion segmentation, called ULFAC-Net. we use a parallel asymmetric convolutional (PAC) module to extract features instead of the traditional square convolution, and innovatively propose a PAC module with dual attention (Att-PAC) to enhance the feature representation. Based on the PAC and Att-PAC modules, we further propose a lightweight textual information submodule. To balance the number of parameters and performance of the model, we also hand-design an asymmetric encoder-decoder architecture. In this paper, we validate the effectiveness and robustness of the proposed ULFAC-Net on four publicly available skin lesion segmentation datasets (ISIC2018, ISBI2017, ISIC2016 and PH2 datasets). The experimental results show that ULFAC-Net achieves competitive segmentation performance with only 0.842 million(0.842M) parameters and 3.71 gigabytes of floating point operations (GFLOPs) compared to other state-of-the-art methods.

Cross-Subject Emotion Recognition Based on Domain Similarity of EEG Signal Transfer Learning.

For solving the problem of the inevitable decline in the accuracy of cross-subject emotion recognition via Electroencephalograph (EEG) signal transfer learning due to the negative transfer of data in the source domain, this paper offers a new method to dynamically select the data suitable for transfer learning and eliminate the data that may lead to negative transfer. The method which is called cross-subject source domain selection (CSDS) consists of the next three parts. 1) First, a Frank-copula model is established according to Copula function theory to study the correlation between the source domain and the target domain, which is described by the Kendall correlation coefficient. 2) The calculation method for the Maximum Mean Discrepancy is improved to determine the distance between classes in a single source. After normalization, the Kendall correlation coefficient is superimposed, and the threshold is set to identify the source-domain data most suitable for transfer learning. 3) In the process of transfer learning, on the basis of Manifold Embedded Distribution Alignment, the Local Tangent Space Alignment method is used to provide a low-dimensional linear estimation of the local geometry of nonlinear manifolds, which maintains the local characteristics of the sample data after dimensionality reduction. Experimental results show that compared with the traditional methods, the CSDS increases the accuracy of emotion classification by approximately 2.8% and reduces the runtime by approximately 65%.

Improved Domain Adaptation Network Based on Wasserstein Distance for Motor Imagery EEG Classification.

Motor Imagery (MI) paradigm is critical in neural rehabilitation and gaming. Advances in brain-computer interface (BCI) technology have facilitated the detection of MI from electroencephalogram (EEG). Previous studies have proposed various EEG-based classification algorithms to identify the MI, however, the performance of prior models was limited due to the cross-subject heterogeneity in EEG data and the shortage of EEG data for training. Therefore, inspired by generative adversarial network (GAN), this study aims to propose an improved domain adaption network based on Wasserstein distance, which utilizes existing labeled data from multiple subjects (source domain) to improve the performance of MI classification on a single subject (target domain). Specifically, our proposed framework consists of three components, including a feature extractor, a domain discriminator, and a classifier. The feature extractor employs an attention mechanism and a variance layer to improve the discrimination of features extracted from different MI classes. Next, the domain discriminator adopts the Wasserstein matrix to measure the distance between source domain and target domain, and aligns the data distributions of source and target domain via adversarial learning strategy. Finally, the classifier uses the knowledge acquired from the source domain to predict the labels in the target domain. The proposed EEG-based MI classification framework was evaluated by two open-source datasets, the BCI Competition IV Datasets 2a and 2b. Our results demonstrated that the proposed framework could enhance the performance of EEG-based MI detection, achieving better classification results compared with several state-of-the-art algorithms. In conclusion, this study is promising in helping the neural rehabilitation of different neuropsychiatric diseases.