Electroencephalogram (EEG)-based emotion identification enables accurate emotional interaction in brain-computer fusion by decoding brain signals, thereby enhancing the intelligence of human-computer collaboration. Data augmentation (DA) techniques offer a promising solution to the challenge of data scarcity in emotion identification. However, traditional DA methods often overlook the physiological mechanisms underlying EEG data, limiting their effectiveness and constraining the performance of emotion classification. To address this, a DA model based on human nerve conduction mechanisms (NCMs), named the gustatory-emotion coupling model and multiblock attention module (GECM-MBAM), is proposed to improve the performance of emotion identification. First, the 1/ f characteristics and synchronization of brain responses are reproduced in the GECM output when stimulated by EEG. The bionic performance of the model in EEG processing is validated, demonstrating brain-like perception of EEG signals via the GECM. Second, the MBAM is designed based on the characteristics of the GECM output, facilitating data augmentation of emotion-related EEG. Comparative experiments demonstrate that GECM-MBAM remarkably outperforms multiple existing DA models in recognition accuracy ( p < 0.05), confirming its effectiveness and superiority in EEG data augmentation. Finally, when compared with state-of-the-art algorithms and in ablation studies, GECM-MBAM demonstrates superior performance in emotion recognition. Specifically, GECM-MBAM attains accuracies of 96.91 % and 94.52 %, recalls of 96.23 % and 93.86 %, and kappa coefficients of 95.45 % and 94.29 % on the SEED and SEED-IV datasets, respectively. In conclusion, the performance of emotion identification is improved using the GECM-MBAM, offering a novel bionic processing approach for affective computing.

Traditional fuzzy clustering cannot effectively characterize the structure of high-dimensional nonlinear data; furthermore, it is highly susceptible to outlier interference and suffers from cluster prototype deviation. This paper proposes an RKHS-basedSampleReconstructionAbility modulatedRobustKernelFuzzyClustering (SRA-RKFC) algorithm. Instead of utilizing standard kernel mapping, SRA-RKFC constructs an outlier-resistant projection subspace within the Reproducing Kernel Hilbert Space (RKHS) to accurately capture complex nonlinear distributions. Specifically, a robust binary weighting mechanism is introduced to mitigate the interference of outliers, and projection reconstruction is employed to preserve the primary structure of data. The global optimal mean is embedded into the joint optimization process to eliminate the mean shift caused by outliers and improve the accuracy of projection representation. Experiments are conducted on a synthetic nested spherical shell dataset and eight benchmark datasets. Results reveal that our proposed SRA-RKFC algorithm achieves superior clustering performance in comparison with current benchmark models across both original and corrupted data settings, indicating the effectiveness of coupling non-linear representation and noise resilience in improving the model robustness and stability. The source code is available fromhttps://github.com/SunseaIU/SRA-RKFC.

Decoding speech intentions from electroencephalogram (EEG) data is the primary task in speech brain-computer interface (BCI) systems, which remains challenging due to the unclear discriminative task-aware features, and underlying nonlinear properties besides the well-known low signal-to-noise ratio of EEG data. Existing approaches typically rely either on single-domain features or performing feature learning by deep neural networks; therefore, they either fail to capture comprehensive signal patterns, or typically require large-sized EEG data to fit the parameter spaces and often have limited interpretability. To address these limitations, we propose a Multi-view Manifold-Adaptive Kernel Regression (MMKR) model for speech recognition from EEG signals in this paper. By treating temporal, spectral, and statistical EEG representations as complementary feature views, view-specific manifold-adaptive kernels are constructed in MMKR to incorporate local graph structure into kernel similarity; besides, a data-driven adaptive view weighting mechanism is used to characterize their contributions. We evaluate MMKR on both overt and imagined speech EEG datasets and the results demonstrate that MMKR achieves superior classification accuracy and robustness compared to some representative single-view, multi-view, and kernel-based baselines. Moreover, analysis on the local manifold-modulated kernel matrix and the learned view contributions are provided.

Co-clustering enables the simultaneous clustering of features and samples by exploiting their associations. Based on the commonly used matrix tri-factorization objective function in co-clustering, we propose an adaptive feature-weighted co clustering with local coordinate coding (AFC-LCC) model in this paper, by making two improvements to enhance the data clustering performance. On one hand, a local coordinate constraint is introduced to enforce the smoothness of the sample cluster indicators along the scaled feature cluster spaces; on the other hand, a quantitative measurement is incorporated to adaptively learn the feature contributions in co-clustering for model discriminative ability enhancement. By co-optimizing the involved variables in the AFC-LCC model objective function, the experimental results not only show competitive clustering performance in comparison with related clustering models, but also depict the rationality and effectiveness of the local coordinate constraint and the feature importance descriptor.

Graph-based multi-view clustering aims to leverage the consistency and complementarity of multiple information sources (views) to enhance clustering performance. The introduction of multi-order graphs has brought significant performance gains by mitigating the sparsity of first-order graphs. However, different views, along with their derived high-order graphs, inevitably contain noise and view-specific information (i.e., diversity), which may hinder the learning of a consensus graph. To address this critical issue while retaining the benefits of multi-order structures, this paper proposes a novel framework, termed Consistency driven Decomposition for Multi-view Multi-order Graph Clustering (CDMMGC). In CDMMGC, multi-order graphs are utilized to mitigate the sparsity problem of first-order graphs and each multi-order graph from each view is decomposed into a consistency and a diversity component. Accordingly, the consensus graph is learned on the consistency component from multi-view multi-order graphs, which is expected to be more accurate in capturing the data semantics. Experiments on extensive datasets demonstrate the effectiveness and superiority of the proposed CDMMGC model in data clustering compared with the state-of-the-art methods. The code is available athttps://github.com/SunseaIU/CDMMGC

Biometric recognition using visually evoked potentials (VEPs), a type of neural response to visual stimuli recorded via electroencephalography (EEG), has shown great promise. However, the non-stationary nature of EEG signals poses a major challenge in cross-session scenarios, where data collected on different days often leads to performance degradation. To address this, we propose the Discriminative Robust Feature Network (DRFNet) to enhance the robustness and inter-subject discriminability of identity representations across sessions. DRFNet incorporates two key components: (1) A log-power transformation that amplifies inter-individual differences by capturing non-linear energy patterns from VEP features via signal squaring and logarithmic scaling; and (2) A hierarchical normalization strategy with adaptive attention to balance discriminative identity cues with inter-session invariance by stabilizing feature distributions across multiple levels (feature map, batch, and sample). On two public multi-session SSVEP datasets (Dataset A: 30 subjects, 6 s trials; Dataset B: 54 subjects, 4 s trials), our model outperformed state-of-the-art methods, achieving identification accuracies of 92.92% and 86.30%, and equal error rates of 3.92% and 4.09%, respectively. Further analysis demonstrates that filter bank processing and a reduced set of parietal-occipital electrodes can provide more discriminative features while offering a practical path toward system lightweighting.

Domainadaptation has proven effective for suppressing the inter-subject variability problem in cross-subject EEG classification tasks in which labeled data is available for source subjects while only unlabeled data is provided for target subjects. Existing domain adaptation methods typically reduced the distribution discrepancy between source and target domains by directly utilizing source domain samples or features. To safeguard the privacy of source domain data, we propose to construct a Proxy Domain by simultaneously considering the prediction Consistency and Confidence (PDCC) of locally trained source models on target EEG samples, serving as the substitute to the source domain. The framework commences with the augmentation and alignment of the source domain data to enhance feature generalizability, after which source models are trained independently on each source subject's data in a decentralized manner. Knowledge transfer from source to target domains is achieved exclusively through accessing to the source domain model, enabling the PDCC-based proxy domain construction that encapsulates the source knowledge. Finally, domain adaptation is performed using the proxy domain and target domain. As a result, PDCC eliminates the need to access source domain data while effectively leveraging source knowledge. Experimental results on four benchmark EEG datasets demonstrate that PDCC consistently outperforms eleven existing methods, including several advanced transfer learning and source-free methods. Especially, the effectiveness of the proxy domain is extensively investigated.

Automatic real image editing offers unprecedented freedom to modify the appearance of the image or to edit a few objects through natural language. Recent scalable model families such as diffusion models have showcased remarkable proficiency in editing highly realistic images due to the introduction of vast amounts of training data and large pretrained language models. However, these large diffusion models require iterative evaluation that would significantly hinder the pace of image editing. Moreover, the pioneering work in this field necessitates the learning of a unique textual token that corresponds to each input image, or a group of images containing the same object, leading to the generation of redundant and fragmented models. Given the aforementioned problems, we suggest a novel Large pretrained models Assistant text-guided image Editing adversarial Network (LAE-Net) in this paper. More concretely, we introduce a deep semantic editing network to globally transfer text information among different isolated editing blocks, which would extract features from the source image to differentiate text-required areas from text-irrelevant ones. Furthermore, based on idea that the multi-modal CLIP model, leveraging vision-language alignment, captures comprehensive global semantic cues, whereas the vision-centric DINO model specializes in delivering intricate, fine-grained pixel-level details, the powerful discriminator of LAE-Net is designed by harnessing the visual embeddings derived from both the CLIP and DINO models separately to boost the visual discriminant capability and facilitate training a strong generator for conditioning image generation. Comprehensive experimental evaluations show that our LAE-Net not only delivers outstanding performance but also surpasses several cutting-edge models.

Functional magnetic resonance imaging (fMRI) is a powerful tool for probing brain function, yet reliable clinical diagnosis is hampered by low signal-to-noise ratios, inter-subject variability, and the limited frequency awareness of prevailing CNN- and Transformer-based models. Moreover, most fMRI datasets lack textual annotations that could contextualize regional activation and connectivity patterns. We introduce RTGMFF, a framework that unifies automatic ROI-level text generation with multimodal feature fusion for brain-disorder diagnosis. RTGMFF consists of three components: (i) ROI-driven fMRI text generation deterministically condenses each subject's activation, connectivity, age, and sex into reproducible text tokens; (ii) Hybrid frequency-spatial encoder fuses a hierarchical waveletmamba branch with a cross-scale Transformer encoder to capture frequency-domain structure alongside long-range spatial dependencies; and (iii) Adaptive semantic alignment module embeds the ROI token sequence and visual features in a shared space, using a regularized cosine-similarity loss to narrow the modality gap. Extensive experiments on the ADHD-200 and ABIDE benchmarks show that RTGMFF surpasses current methods in diagnostic accuracy, achieving notable gains in sensitivity, specificity, and area under the ROC curve. Code is available at https://github.com/BeistMedAI/RTGMFF.

Recently, Electroencephalography (EEG) has been increasingly utilized in driving fatigue detection tasks. However, the inter-subject variabilities in EEG data render models trained on one subject ineffective for being directly applied to others. Transfer learning has been widely used to address this issue, but most existing transfer learning algorithms primarily focused on classification tasks. Therefore, we propose a transfer regression model for EEG-based driving fatigue detection, whose core idea is to learn the weights of models from various source domain data and a base model from target domain training data through an attention network. By assembling models trained on different domain data, predictions are obtained. We conducted experiments on the two subsets of the benchmark SEED-VIG dataset, and the results demonstrate that our transfer regression model effectively enhances the driving fatigue detection performance. The source code is available from https://github.com/SunseaIU/ATR-EEG.

Alzheimer’s Disease (AD) is a complex neurodegenerative disorder marked by memory loss, executive dysfunction, and personality changes. Early diagnosis is challenging due to subtle symptoms and varied presentations, often leading to misdiagnosis with traditional unimodal diagnostic methods due to their limited scope. This study introduces an advanced multimodal classification model that integrates clinical, cognitive, neuroimaging, and EEG data to enhance diagnostic accuracy. The model incorporates a feature tagger with a tabular data coding architecture and utilizes the TimesBlock module to capture intricate temporal patterns in Electroencephalograms (EEG) data. By employing Cross-modal Attention Aggregation module, the model effectively fuses Magnetic Resonance Imaging (MRI) spatial information with EEG temporal data, significantly improving the distinction between AD, Mild Cognitive Impairment, and Normal Cognition. Simultaneously, we have constructed the first AD classification dataset that includes three modalities: EEG, MRI, and tabular data. Our innovative approach aims to facilitate early diagnosis and intervention, potentially slowing the progression of AD. The source code and our private ADMC dataset are available at https://github.com/JustlfC03/MSTNet.

Clustering has long been a fundamental problem in machine learning and data mining, with the aim of grouping data samples on the basis of their intrinsic similarity. However, the consensus that different features often exhibit varying levels of discriminative power in clustering model learning is under explored sufficiently in collaboration with the pseudo-label guided unsupervised discriminative analysis. To this end, we propose an Adaptive Feature-Weighted Local-global data Clustering (AFW-LGC) model which is featured by two improvements. First, AFW-LGC takes into account both global separability (between-cluster scatter) and local compactness (within-cluster scatter) whose impacts are mediated by a learnable parameter. Second, the different contributions of features are adaptively learned in AFW-LGC for further discriminative ability enhancement. Both improvements are seamlessly integrated for feature-weighted unsupervised discriminative subspace clustering nature of AFW-LGC. Extensive experiments on eight data sets demonstrate the superior clustering performance of AFW-LGC over some SOTA methods as well as the rationality of our proposed feature importance exploration strategy.

Speech-related brain-computer interfaces (BCIs) have emerged as a promising paradigm for intuitive communication between human and external devices especially for people with language disorders. However, current Electroencephalogram (EEG)-based speech recognition performance remains inadequate for practical applications, primarily due to two challenges. One is the involvement of multiple brain networks in speech production, which renders single-domain features insufficient for comprehensive representation; the other is the low signal-to-noise ratio inherent in EEG data, coupled with variable data quality, which complicates decoding efforts. To address both issues, we propose an adaptive multi-granularity information exploration (AMIE) model for enhancing EEG-based speech decoding performance, which leverages complementary information across multiple feature domains and incorporates a tripartite dynamic weighting mechanism, taking all the domain, sample and feature importance into consideration, to improve model robustness and emphasize discriminative features. Experimental results on two public EEG data sets demonstrate the competitive performance in speech recognition as well as the effectiveness of the multi-granularity importance exploration.

Muscle-driven human–machine systems (HMSs) have advanced significantly in recent years, yet practical applications are often challenged by issues like muscle fatigue. This study introduces an innovative system that captures both surface electromyographic (sEMG) signals and muscle force myography (FMG) signals from the human arm simultaneously. The sEMG signals provide information about the electrical activity of muscle contractions, while the FMG signals monitor morphological changes in the muscles, all in a noninvasive manner. We developed a portable, wearable hybrid sEMG-FMG acquisition system, consisting of a signal acquisition module and an armband to gather both types of signals from the same skin area. Using this system, we examined the sensitivity of sEMG and FMG signals to muscle fatigue and assessed whether combining these two signals could mitigate the effects of muscle fatigue on gesture recognition accuracy. The sEMG and FMG signals were recorded during various hand gestures under both fatigued and nonfatigued conditions. Additionally, a cascade fuzzy forest (CFF) algorithm was developed to enhance hand motion recognition accuracy using the combined sEMG-FMG signals. Experimental results show that FMG signals are resilient to muscle fatigue, and the integration of sEMG with FMG signals significantly reduces the adverse effects of muscle fatigue. The CFF algorithm further improves recognition accuracy, demonstrating the effectiveness of the proposed approach.

Multimodal sarcasm analysis is one of the most challenging research branch of the sentiment analysis area, due to the presence of cross-modality incongruity. However, existing works mainly attend to the coarse-grained incongruity analysis, and totally ignore the sentiment semantic coupling issue. This indeed limits the discriminate capability and robustness of the sarcasm analysis model. In order to address the above issue, we propose a novel Fine-grained Semantic Disentanglement Network (FSDN). Specifically, the intra-modality semantic disentanglement is performed to investigate the more intrinsic semantic cues of the same modality. Additionally, the inter-modality semantic disentanglement is leveraged to simultaneously facilitate the common and intrinsic semantic cues across modalities. Furthermore, the dual-spatial semantic interaction block is presented to explore the long-range cross-spatial semantic context between the obtained verbal and non-verbal semantic space with the global view. The above semantic disentanglement processes with both local and global views significantly unleash much more robustness even for the sarcasm case consisting of multiple semantic message. Various experiments indicate that the FSDN can receive state-of-the-art or competitive performance.

We present AEGIS-Net, a novel indoor place recognition model that takes in RGB point clouds and generates global place descriptors by aggregating lower-level color, geometry features and higher-level implicit semantic features. However, rather than simple feature concatenation, self-attention modules are employed to select the most important local features that best describe an indoor place. Our AEGIS-Net is made of a semantic encoder, a semantic decoder and an attention-guided feature embedding. The model is trained in a 2-stage process with the first stage focusing on an auxiliary semantic segmentation task and the second one on the place recognition task. We evaluate our AEGIS-Net on the ScanNetPR dataset and compare its performance with a pre-deep-learning feature-based method and five state-of-the-art deep-learning-based methods. Our AEGIS-Net achieves exceptional performance and outperforms all six methods.

Surface electromyography (sEMG) noninvasively records muscle activities. It provides valuable information about muscle contractions and enables real-time decoding into hand gestures. Recently many studies have successfully demonstrated this capability. However, the accuracy of gesture recognition decreases significantly due to electrode shifts. Without increasing the density of electrodes which may cause the curse of dimensionality and result in higher costs, we propose a label rectified and graph adaptive semi-supervised regression (LRGASR) model for electrode shifted gesture recognition. LRGASR on one hand learns an optimal graph to characterize the underlying semantic connectionship of both non-shifted and shifted sEMG samples and takes advantage of label rectification to reduce the feature-label inconsistency of shifted ones. Experimental results show that LRGASR achieved the average recognition accuracies 78.20% and 87.28% on the SeNic and ISRMyo sEMG data sets, which outperforms six existing models.

Text-to-image synthesis aims to generate highquality realistic images conditioned on text description. The great challenge of this task depends on deeply and seamlessly integrating image and text information. Thus, in this paper, we propose a deep multimodal fusion generative adversarial networks (DMF-GAN) that allows effective semantic interactions for finegrained text-to-image generation. Specifically, through a novel recurrent semantic fusion network, DMF-GAN could consistently manipulate global assignment of text information among isolated fusion blocks. With the assistance of a multi-head attention module, DMF-GAN could model word information from different perspectives and further improve the semantic consistency. In addition, a word-level discriminator is proposed to provide the generator with fine-grained feedback related to each word. Compared with current state-of-the-art methods, our proposed DMFGAN could efficiently synthesize realistic and text-alignment images and achieve better performance on challenging benchmarks. The code link:https://github.com/xueqinxiang/DMF-GAN

Attention-based networks currently identify their effectiveness in multimodal sentiment analysis. However, existing methods ignore the redundancy of auxiliary modalities. More importantly, existing methods only attend to top-down attention (static process) or down-top attention (implicit process), leading to the coarse-grained multimodal sentiment context. In this paper, during the preprocessing period, we first propose the multimodal dynamic enhanced block to capture the intra-modality sentiment context. This can effectively decrease the intra-modality redundancy of auxiliary modalities. Furthermore, the bi-direction attention block is proposed to capture fine-grained multimodal sentiment context via the novel bi-direction multimodal dynamic routing mechanism. Specifically, the bi-direction attention block first highlights the explicit and low-level multimodal sentiment context. Then, the low-level multimodal context is transmitted to a carefully designed bi-direction multimodal dynamic routing procedure. This allows us to dynamically update and investigate high-level and much more fine-grained multimodal sentiment contexts. The experiments demonstrate that our fusion network can achieve state-of-the-art performance. Notably, our model outperforms the best baseline on the metric ‘Acc-7’ with an improvement of 6.9%.

Due to the weak and nonstationary properties, electroencephalogram (EEG) data present significant individual differences. To align data distributions of different subjects, transfer learning showed promising performance in cross-subject EEG emotion recognition. However, most of the existing models sequentially learned the domain-invariant features and estimated the target domain label information. Such a two-stage strategy breaks the inner connections of both processes, inevitably causing the suboptimality. In this article, we propose a joint EEG feature transfer and semisupervised cross-subject emotion recognition model in which the shared subspace projection matrix and target label are jointly optimized toward the optimum. Extensive experiments are conducted on SEED-IV and SEED, and the results show that the emotion recognition performance is significantly enhanced by the joint learning mode and the spatial-frequency activation patterns of critical EEG frequency bands and brain regions in cross-subject emotion expression are quantitatively identified by analyzing the learned shared subspace.

Though Electroencephalogram (EEG) could objectively reflect emotional states of our human beings, its weak, non-stationary, and low signal-to-noise properties easily cause the individual differences. To enhance the universality of affective brain-computer interface systems, transfer learning has been widely used to alleviate the data distribution discrepancies among subjects. However, most of existing approaches focused mainly on the domain-invariant feature learning, which is not unified together with the recognition process. In this paper, we propose a joint feature adaptation and graph adaptive label propagation model (JAGP) for cross-subject emotion recognition from EEG signals, which seamlessly unifies the three components of domain-invariant feature learning, emotional state estimation and optimal graph learning together into a single objective. We conduct extensive experiments on two benchmark SEED_IV and SEED_V data sets and the results reveal that 1) the recognition performance is greatly improved, indicating the effectiveness of the triple unification mode; 2) the emotion metric of EEG samples are gradually optimized during model training, showing the necessity of optimal graph learning, and 3) the projection matrix-induced feature importance is obtained based on which the critical frequency bands and brain regions corresponding to subject-invariant features can be automatically identified, demonstrating the superiority of the learned shared subspace.

Measuring the sleep quality is important or even crucial for people who are engaged in dangerous jobs such as the high-speed train drivers. Since the scalp EEG data are generated by the neural activities of the brain cortex, it is collected from subjects with different hours of sleep time (4 hours, 6 hours and 8 hours) to conduct sleep quality evaluation. To suppress the cross-subject variances of EEG data, in this paper, we propose a joint feature auto-weighting and semi-supervised classification model, termed GRLSR, which is formulated by introducing an auto-weighting variable into the least square regression to adaptively and quantitatively measure the importance of each dimension of the feature. Once the model is solved, besides the measurement results, we can use the auto-weighting variable to 1) analyze the importance of each frequency band in sleep quality expression and 2) identify the capacity of different channels connecting to the sleep effect. Therefore, the proposed GRLSR is a pure data-driven computing model for EEG-based cross-subject sleep quality evaluation. Experimental results show its effectiveness.

Non-negative matrix factorization (NMF) is an efficient model in learning parts-based data representation. Since the local geometrical structure can be effectively modeled by a nearest neighbor graph, the graph regularized NMF (GNMF) was proposed to make the learned representation more faithfully and better characterize the intrinsic structure of data. However, GNMF shares a similar paradigm with most of existing graph-based learning models which perform learning tasks on a fixed input graph. In this paper, we propose a new Flexible NMF model with adaptively learned Graph regularization (FNMFG) in which the graph is jointly learned with simultaneous performing the matrix factorization. An efficient iterative method with guaranteed convergence and relative low complexity is developed to optimize the FNMFG objective. Experiments compare FNMFG method with state-of-the-art algorithms and demonstrate its improved performance.

As one of the matrix factorization models, concept factorization (CF) achieved promising performance in learning data representation in both original feature space and reproducible kernel Hilbert space (RKHS). Based on the consensuses that 1) learning performance of models can be enhanced by exploiting the geometrical structure of data and 2) jointly performing structured graph learning and clustering can avoid the suboptimal solutions caused by the two-stage strategy in graph-based learning, we developed a new CF model with self-expression. Our model has a combined coefficient matrix which is able to learn more efficiently. In other words, we propose a CF-based joint structured graph learning and clustering model (JSGCF). A new efficient iterative method is developed to optimize the JSGCF objective function. Experimental results on representative data sets demonstrate the effectiveness of our new JSGCF algorithm.

The central task in graph-based unsupervised feature selection (GUFS) depends on two folds, one is to accurately characterize the geometrical structure of the original feature space with a graph and the other is to make the selected features well preserve such intrinsic structure. Currently, most of the existing GUFS methods use a two-stage strategy which constructs graph first and then perform feature selection on this fixed graph. Since the performance of feature selection severely depends on the quality of graph, the selection results will be unsatisfactory if the given graph is of low-quality. To this end, we propose a joint graph learning and unsupervised feature selection (JGUFS) model in which the graph can be adjusted to adapt the feature selection process. The JGUFS objective function is optimized by an efficient iterative algorithm whose convergence and complexity are analyzed in detail. Experimental results on representative benchmark data sets demonstrate the improved performance of JGUFS in comparison with state-of-the-art methods and therefore we conclude that it is promising of allowing the feature selection process to change the data graph.

Non-negative Matrix Factorization (NMF) is a popular model in machine learning, which can learn parts-based representation by seeking for two non-negative matrices whose product can best approximate the original matrix. However, the manifold structure is not considered by NMF and many of the existing work use the graph Laplacian to ensure the smoothness of the learned representation coefficients on the data manifold. Further, beyond smoothness, it is suggested by recent theoretical work that we should ensure second order smoothness for the NMF mapping, which measures the linearity of the NMF mapping along the data manifold. Based on the equivalence between the gradient field of a linear function and a parallel vector field, we propose to find the NMF mapping which minimizes the approximation error, and simultaneously requires its gradient field to be as parallel as possible. The continuous objective function on the manifold can be discretized and optimized under the general NMF framework. Extensive experimental results suggest that the proposed parallel field regularized NMF provides a better data representation and achieves higher accuracy in image clustering.

In recent years, there are many great successes in using deep architectures for unsupervised feature learning from data, especially for images and speech. In this paper, we introduce recent advanced deep learning models to classify two emotional categories (positive and negative) from EEG data. We train a deep belief network (DBN) with differential entropy features extracted from multichannel EEG as input. A hidden markov model (HMM) is integrated to accurately capture a more reliable emotional stage switching. We also compare the performance of the deep models to KNN, SVM and Graph regularized Extreme Learning Machine (GELM). The average accuracies of DBN-HMM, DBN, GELM, SVM, and KNN in our experiments are 87.62%, 86.91%, 85.67%, 84.08%, and 69.66%, respectively. Our experimental results show that the DBN and DBN-HMM models improve the accuracy of EEG-based emotion classification in comparison with the state-of-the-art methods.

Slow eye movement (SEM) regarded as a sign of onset of sleep is very significant for detecting driver fatigue, but its characteristics and detection algorithm have been rarely involved in the study of driver fatigue detection. In this study, some new features were extracted based on wavelet singularity analysis and statistics to detect SEMs. Six subjects participated in this simulated driving experiment, and for each subject, a more than 2 hours electro-oculogram (EOG) session was recorded. Each session was divided into SEM epochs and non-SEM epochs according to the common judgments made by the two of three experts by the visual recognition criteria of SEMs. Regarding the problem of detecting SEMs as an imbalance classification problem, and through the under-sampling and over-sampling methods a 2s horizontal electro-oculogram (HEO) signal could finally be recognized as the category of SEMs or non-SEMs with the classifiers SVM, GELM, and KNN respectively. Results prove that the proposed features was a little better than the wavelet energy features, and through the combination of the wavelet energy features and the new features based on wavelet singularity analysis and statistics, the classification results were improved obviously.

This study aims at finding the relationship between EEG signals and human emotional states. Movie clips are used as stimuli to evoke positive, neutral and negative emotions of subjects. We introduce a new effective classifier named discriminative graph regularized extreme learning machine (GELM) for EEG-based emotion recognition. The average classification accuracy of GELM using differential entropy (DE) features on the whole five frequency bands is 80.25%, while the accuracy of SVM is 76.62%. These results indicate that GELM is more suitable for emotion recognition than SVM. Additionally, the accuracies of GELM using DE features on Beta and Gamma bands are 79.07%, 79.93% respectively. This suggests that these two bands are more relevant to emotion. The experimental results indicate that the EEG patterns for emotion are generally stable among different experiments and subjects. By using minimal-redundancy-maximal-relevance (MRMR) algorithm and correlation coefficients to select effective features, we get the distribution of top 20 subject-independent features and build a manifold model to monitor the trajectory of emotion changes with time.

Being a part of IEEE project 802, the 802.11 medium access control (MAC) is used to support asynchronous and time bounded delivery of radio data packets. It is proposed that a distributed coordination function (DCF), which uses carrier sense multiple access with collision avoidance (CSMA/CA) and binary slotted exponential backoff, be the basis of the IEEE 802.11 WLAN MAC protocols. This paper proposes a throughput enhancement mechanism for DCF by adjusting the contention window (CW) resetting scheme. Moreover, an analytical model based on Markov chain is introduced to compute the enhanced throughput of 802.11 DCF. The accuracy of the model and the enhancement of the proposed scheme are verified by elaborate simulations.

IEEE 802.11 medium access control (MAC) is proposed to support asynchronous and time bounded delivery of radio data packets in infrastructure and ad hoc networks. The basis of the IEEE 802.11 WLAN MAC protocol is a distributed coordination function (DCF), which is a carrier sense multiple access with collision avoidance (CSMA/CA) with a binary slotted exponential back-off scheme. Since IEEE 802.11 MAC has its own characteristics that are different from other wireless MAC protocols, the performance of reliable transport protocol over 802.11 needs further study. This paper proposes a scheme named DCF+, which is compatible with DCF, to enhance the performance of reliable transport protocol over WLAN. To analyze the performance of DCF and DCF+, this paper also introduces an analytical model to compute the saturated throughput of WLAN. Compared with other models, this model is shown to be able to predict the behavior of 802.11 more accurately. Moreover, DCF+ is able to improve the performance of TCP over WLAN, which is verified by modeling and elaborate simulation results.