Numerous blood oxygenation level-dependent (BOLD) imaging studies have shown that generalized anxiety disorder (GAD) can lead to abnormal activation of specific brain regions in patients. However, these methods lack sufficient temporal resolution to explain the underlying brain dynamics of GAD. The electroencephalogram (EEG) microstate allows us to explore brain dynamics at the subsecond level. We performed microstate analysis and source localization on the EEG data of 15 GADs and 14 healthy controls (HCs). We found two kinds of noncanonical microstate topologies (MS-4 and MS-5) in the episodic recall tasks. Compared with HCs, the duration and coverage of MS-5 were significantly reduced in GADs and positively correlated with the GAD-7 scores. The results of source localization showed obvious activation in the prefrontal lobe, parietal lobe, temporal lobe, and fusiform gyri. Moreover, we propose an improved capsule network to capture EEG spatial features and combine them with temporal parameters of microstates for more reliable GAD detection. The sensor-level EEG data and the source-level EEG data obtained by source reconstruction are used as input to the model. The optimal configuration combined the spatial features of source-level data with microstate features and achieved the highest classification accuracy. Collectively, the statistical results indicated remarkable differences in dynamic brain parameters between the two groups, and patients with GAD may have abnormalities in their higher sensory cortex that affect the processing of anxiety signals. Furthermore, our proposed fusion framework provides a reliable method for GAD automatic detection.

BACKGROUND:With a large number of accidents caused by the decline in the vigilance of operators, finding effective automatic vigilance monitoring methods is a work of great significance in recent years. Based on physiological signals and machine learning algorithms, researchers have opened up a path for objective vigilance estimation. METHODS:Sparse representation (SR)-based recognition algorithms with excellent performance and simple models are very promising approaches in this field. This paper aims to study the adaptability and performance improvement of truncated l1 distance (TL1) kernel on SR-based algorithm in the context of physiological signal vigilance estimation. Compared with the traditional radial basis function (RBF), the TL1 kernel has good adaptiveness to nonlinearity and is suitable for the discrimination of complex physiological signals. A recognition framework based on TL1 and SR theory is proposed. Firstly, the inseparable physiological features are mapped to the reproducing kernel Kreĭn space through the infinite-dimensional projection of the TL1 kernel. Then the obtained kernel matrix is converted into the symmetric positive definite matrix according to the eigenspectrum approaches. Finally, the final prediction result is obtained through the sparse representation regression process. RESULTS:We verified the performance of the proposed framework on the popular SEED-VIG dataset containing physiological signals (electroencephalogram and electrooculogram) associated with vigilance. In the experimental results, the TL1 kernel is superior to the RBF kernel in both performance and kernel parameter stability. CONCLUSIONS:This demonstrates the effectiveness of the TL1 kernel in distinguishing physiological signals and the excellent vigilance estimation capability of the proposed framework. Moreover, the contribution of our research motivates the development of physiological signal recognition based on kernel methods.

The automatic seizure detection in electroencephalogram (EEG) signals is crucial for the monitoring, diagnosis, and treatment of epilepsy. In this study, an intelligent detection framework with the discriminative Stein kernel-based sparse representation (DSK-SR) is constructed to distinguish epileptic EEG signals. Specifically, in the scheme of DSK-SR, EEG samples are presented by symmetric positive definite (SPD) matrices in the form of covariance descriptors (CovDs). Taking into account the non-Euclidean geometry of the Riemannian manifold of SPD matrices, the traditional SR in Euclidean space cannot be applied in its original form on the manifold. To this end, the DSK defined on the manifold can permit us to embed the manifold into a high-dimensional reproducing kernel Hilbert space (RKHS) to perform SR. Then, test samples are sparsely coded over the training sets, and the classification decision is performed by assessing which class generates the minimal reconstructed residuals. Eventually, competitive experimental results on three widely recognized EEG datasets against the state-of-the-art methods demonstrate the efficacy of the proposed DSK-SR in identifying epileptic EEG signals, indicating its powerful application potential in the automatic seizure detection.

Pupil segmentation is a first and important topic of iris recognition, identity recognition and eye movement information extraction for mental analysis. However, due to the negative effects of eyelash occlusion, eyelid occlusion and off-gaze deflection, making a precise pupil segmentation is a difficult task. Therefore, we propose a precise and robust algorithm for pupil segmentation, namely Angle Variance based Filterable Sample Consensus (AVBFSC), which is composed of an outlier filter and a boundary locator. The outlier filter can eliminate negative effects mentioned above, and also a best pupil segmentation is performed with our boundary locator, which learns a circular mathematical equation by selecting sub-samples from pupil edge pixels randomly. Experiment results in comparison with state-of-the-art methods on CASIA-Iris-V4-Interval dataset, indicate that our algorithm achieved best performance, that is, Accuracy of 98.99%, False Acceptance Rate (FAR) of 2.09% and Genuine Acceptance Rate (GAR) of 98.54%. In addition, it also has robust results under the condition of specific non-ideal scenes from CASIA-Iris-V4-Lamp dataset and Indian Institute of Technology Delhi (IITD) dataset including dark light conditions, eyelash occlusion, eyelid occlusion and off-gaze deflection.

Automatic monitoring technology for fatigued driving can greatly reduce traffic accidents caused by drivers due to decreased attention. The use of monitoring algorithms based on physiological signal sensors is of great significance for objective monitoring. In this paper, a novel algorithm with excellent performance and strong generalization ability, kernel sparse representation regression based on generalized minimax-concave (GMC-KSRR), is proposed to identify fatigue physiological signals. Specifically, in the proposed algorithm, the training samples of physiological features are mapped in reproducing kernel Hilbert space (RKHS) for linear separability. Then, in RKHS, the sparse coefficients of the test samples are obtained by generalized minimax-concave (GMC) penalty-based sparse representation (SR). Different from the traditional $\ell _{{1}}$ -norm, the GMC penalty we used does not underestimate the high-amplitude component of sparsity coefficients, resulting in a more accurate regression result. Finally, the regression decision is performed in the label subspace according to the obtained sparse coefficients. Meanwhile, a multimodality algorithm based on GMC-KSRR has also been proposed that weighs each modality according to quality for robust fatigue monitoring. Eventually, competitive experimental results on the well-known SEED-VIG dataset against the state-of-the-art methods demonstrate the efficacy of the proposed algorithms in identifying fatigue physiological signals, indicating its powerful application potential in driving fatigue monitoring.

In this paper, to explore the application of depression EEG data in semi-supervised classification, we designed an improved semi-supervised graph convolutional neural network model for depression identification. Fifty-three volunteers, including 24 patients with depression and 29 healthy controls, participated in the study. Electroencephalogram (EEG) data from 128 channels were recorded in the resting state for 5 minutes. The differential entropy feature of EEG is obtained and its Pearson matrix is used to construct the node and adjacency matrix of the graph. For the classifier, we combined self-organizing incremental neural network (SOINN) and graph convolutional neural network (GCN) self-training to expand the training set, improve the effect of classification, and adopted 10-fold cross validation to verify the classification results. Compared with convolutional neural networks (CNN), long short-term memory (LSTM) neural network and classical fully supervised algorithms, such as support vector machine (SVM), the classification accuracy of the proposed model is 70.53% and 92.23% under the condition of label data of 50 and 600, respectively. Compared with the original GCN model, our method has significantly improved the performance indicators of Accuracy (Acc), Recall (Rec), Specificity (Spec) and Precision (Pre) and improved the ability of GCN label propagation. This study provides a semi-supervised learning model for detecting depression and a new method for diagnosing depression based on EEG signals.

Automatic seizure onset detection in electroencephalogram (EEG) signals is vital for the monitoring and diagnosis of epilepsy. In this paper, we develop a novel detection framework employing the common spatial pattern (CSP) and discriminative log-Euclidean kernel-based Gaussian process (DLEK-GP) for distinguishing epileptic EEG signals. In the framework, the CSP is utilized as a feature extractor to reduce the dimension of multi-channel data representation and obtain distinguishing features. Afterwards, the DLEK defined on the Riemannian manifold of symmetric positive definite (SPD) matrices is combined with the GP classifier to categorize the EEG signals. The assessment results obtained from the CHB-MIT EEG dataset demonstrate that the proposed framework can achieve an average segment-based sensitivity of 97.57%, specificity of 97.26%, accuracy of 97.42%, and an average event-based sensitivity of 98.57%, false detection rate (FDR) of 0.54/h, latency of 1.02 s. The satisfactory results show that the proposed seizure detection framework holds the promising potential for clinical practice.

In recent years, major depressive disorder (MDD) has been shown to negatively impact physical recovery in a variety of patients. Functional near-infrared spectroscopy (fNIRS) is a tool that can potentially supplement clinical interviews and mental state examinations to establish a psychiatric diagnosis and monitor treatment progress. Thirty-two subjects, including 16 patients clinically diagnosed with MDD and 16 healthy controls (HCs), participated in the study. Brain oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) responses were recorded using a 22-channel continuous-wave fNIRS device while the subjects performed the emotional sound test. This study evaluated the difference between MDD patients and HCs using a variety of methods. In a comparison of the Pearson correlation coefficients between the HbO/HbR responses of each fNIRS channel and four scores, MDD patients and HCs had significantly different Athens Insomnia Scale (AIS) scores. By quantitative evaluation of the functional association, we found that MDD patients had aberrant functional connectivity compared with HCs. Furthermore, we concluded that compared with HCs, there were marked abnormalities in blood oxygen in the bilateral ventrolateral prefrontal cortex (VLPFC) and bilateral dorsolateral prefrontal cortex (DLPFC). Four statistical-based features extracted from HbO signals and four vector-based features from both HbO and HbR served as inputs to four simple neural networks (multilayer neural network (MNN), feedforward neural network (FNN), cascade forward neural network (CFNN) and recurrent neural network (RNN)). Through an analysis of combinations of different features, the combination of 4 common features (mean, STD, area under the receiver operating characteristic curve (AUC) and slope) yielded the highest classification accuracy of 89.74% for fear emotion. The combination of four novel feature (CBV, COE, |L | and K) resulted in a classification accuracy of 99.94% for fear emotion. The top 10 common and novel features were selected by the ReliefF feature selection algorithm, resulting in classification accuracies of 83.52% and 91.99%, respectively. This study identified the AUC and angle K as specific neuromarkers for predicting MDD across specific depression-related regions of the prefrontal cortex (PFC). These findings suggest that the fNIRS measurement of the PFC may serve as a supplementary test in routine clinical practice to further support a diagnosis of MDD.