The 1st SpeechWellness Challenge (SW1) aims to advance methods for detecting current suicide risk in adolescents using speech analysis techniques. Suicide among adolescents is a critical public health issue globally. Early detection of suicidal tendencies can lead to timely intervention and potentially save lives. Traditional methods of assessment often rely on self-reporting or clinical interviews, which may not always be accessible. The SW1 challenge addresses this gap by exploring speech as a non-invasive and readily available indicator of mental health. We release the SW1 dataset which contains speech recordings from 600 adolescents aged 10-18 years. By focusing on speech generated from natural tasks, the challenge seeks to uncover patterns and markers that correlate with current suicide risk.

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 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.

Recent research has shown Convolutional Neural Networks (CNNs) trained in a fully-supervised fashion achieve promising performance on extreme low-light image denoising task. However, a large amount of "noisy-clean" image pairs are required to train a network, which are difficult to obtain. In this paper, we propose a compact yet effective noise model to generate synthetic noisy images for training. Especially, we address the severe color distortion problem in low-light images by identifying a novel noise component, black calibration error, as its physical origin. We prove that a small error at the sensing stage will strongly affect the following in-camera signal processing (ISP) pipeline and eventually lead to color bias. Experiment results demonstrate that the proposed model is superior in preserving perceptual quality and achieves state-of-the-art performance among existing noise synthesis methods.

Tens of millions of people suffer from depression worldwide. It is urgent to explore an effective method for diagnosing depression. This study developed a novel of multimodal feature fusion depression recognition method based on functional near-infrared spectroscopy (fNIRS). Sixty volunteers, including thirty patients with depression and thirty healthy controls, participated in the study. The 22-channel fNIRS device recorded the participants' brain oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) concentration changes in the positive, neutral and negative affective words' stimulation. K-nearest neighbors (KNN) and support vector machine (SVM) classifiers were used to recognize depressed patients from normal people, and 10-fold cross-validation was used to verify the classification result. Under the three single-mode features, the accuracy rates were 85.69%, 88.32% and 86.77%, corresponding to the positive condition, neutral condition and negative condition. Then, we used concatenation and linear combination for feature fusion. For the concatenation fusion method, the principal component analysis (PCA) was used to reduce the dimension. The result showed that feature fusion can relatively improve the recognition rate of people with depression, compared with single-model features. The optimal feature fusion method is to concatenate the neutral features and negative features, and the best accuracy reaches 94.45%. The study may provide a more accurate and convenient method for depression detection.

Tens of millions of people suffer from depression worldwide. It is urgent to explore an effective method for diagnosing depression. This study developed a novel of multimodal feature fusion depression recognition method based on functional near-infrared spectroscopy (fNIRS). Sixty volunteers, including thirty patients with depression and thirty healthy controls, participated in the study. The 22-channel fNIRS device recorded the participants’ brain oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) concentration changes in the positive, neutral and negative affective words’ stimulation. K-nearest neighbors (KNN) and support vector machine (SVM) classifiers were used to recognize depressed patients from normal people, and 10-fold cross-validation was used to verify the classification result. Under the three single-mode features, the accuracy rates were 85.69%, 88.32% and 86.77%, corresponding to the positive condition, neutral condition and negative condition. Then, we used concatenation and linear combination for feature fusion. For the concatenation fusion method, the principal component analysis (PCA) was used to reduce the dimension. The result showed that feature fusion can relatively improve the recognition rate of people with depression, compared with single-model features. The optimal feature fusion method is to concatenate the neutral features and negative features, and the best accuracy reaches 94.45%. The study may provide a more accurate and convenient method for depression detection.

Emotion, as an advanced function of the human brain, affects kinds of human behaviors. Electroencephalographs (EEG) are widely used in the field of emotion classification owing to their low cost and portability. In this work, we study the effects of a non-linear EEG feature and a channel selection method on emotion recognition. First, the fractal dimension(FD) which could reflect the state of the brain is extracted with a sliding window. The top seven channels are screened out by calculating the F-score from the whole samples. Then, based on the signals from forehead channels, filtered channels and associated channels, emotions on valence and arousal are classified by Support Vector Machine(SVM) and K Nearest Neighbours(KNN). The result shows that the forehead channels Fp2, AF8, Fpz play an important role in valence classification. When combining the forehead channels with other channels that have higher F-score, the SVM classifier has a better accuracy on the whole set with 89.37% on valence and 87.07% on arousal. Besides, the overall accuracy calculated on each participants with associated channels get significant improvement. Especially, the KNN classifier has a much better result on every subject. This phenomenon indicates that by combining the higher F-score channels with the forehead channels, the associated channels can not only take advantage of the forehead channels' ability to categorize emotions but also consider individual differences.

ABSTRACT By combining multiple factors during authentication, a service can provide better assurance of security. However, the users are likely to feel inconvenient, or even discard the service. This paper, therefore, addresses this issue and introduces a novel method, referred to as the Quantified riSk and Benefit adaptive Authentication Factors combination (QSBAF). QSBAF balances the requirements for both security and usability in the authentication of an information system and improves the system's ability to respond quickly to emerging risky events. In QSBAF, the authentication factors can be dynamically combined on the basis of quantified risk, benefit measurements, and combination policies. Furthermore, QSBAF provides an adaptive mechanism, which is driven by history data to justify the measurements of risk and benefit. In this paper, we use the online banking system as a typical scenario to demonstrate the usage of QSBAF. We also implement a prototype of QSBAF to evaluate the performance of its feasibility in real application scenarios. Copyright © 2013 John Wiley & Sons, Ltd.