Depression detection benefits from combining neurological and behavioral indicators, yet integrating heterogeneous modalities such as EEG and interview audio remains challenging. We propose a transformer-based multimodal framework that jointly models spectral, spatial, and temporal EEG features alongside linguistic and paralinguistic cues from interviews. By employing synchronized multi-head cross-attention and self-attention mechanisms, the model effectively captures intra- and inter-modal correlations. In addition, a flexible temporal sequence matching strategy reduces EEG channel requirements, enhancing device portability. Evaluated on the MODMA and DAIC-WOZ datasets, our approach achieves superior performance compared to state-of-the-art models, with a 4.7% improvement in accuracy and a 10% increase in precision. These results demonstrate the potential of the proposed framework for accurate, scalable, and cost-effective depression detection in both clinical and real-world settings.

Multimodal AI systems increasingly rely on biomedical signals such as EEG and eye movement data for emotion recognition. However, these models face challenges including limited training data, inter-subject variability, session-specific spurious correlations, and incomplete modality representation, all of which reduce generalization and reliability. We propose TEREE, a multimodal transformer-based model that integrates temporal, spatial, and spectral EEG features with eye movement data. To mitigate session-specific artifacts, Bayesian Spurious Correlation Minimization (BSCM) is applied. In addition, a holistic multimodal processing strategy enables robust handling of incomplete data. The model was trained and evaluated using the SEED and SEED-FRA benchmark datasets under one-to-one and multi-to-one transfer paradigms. TEREE achieved state-of-the-art performance, with average multi-to-one transfer accuracies of 97.7% on SEED and 98.8% on SEED-FRA. Ablation studies confirmed that fusing EEG with eye movement features consistently improved accuracy compared to unimodal baselines. Standard deviations across repeated experiments were below 5%, indicating stability. By addressing inter-subject variability, spurious correlations, and incomplete modality issues, TEREE enhances the robustness and generalization of emotion recognition systems. These findings suggest that multimodal transformer-based models can substantially improve the reliability of affective computing applications such as human–computer interaction and mental health monitoring.

Accurate and secure classifying informal documents related to mental disorders is challenging due to factors such as informal language, noisy data, cultural differences, personal information and mixed emotions. Conventional deep learning models often struggle to capture patterns in informal text, as they miss long-range dependencies, explain words and phrases literally, and have difficulty processing non-standard inputs like emojis. To address these limitations, we expand data dimensionality, transforming and fusing textual data and signs from a 1D to a 2D space. This enables the use of pre-trained 2D CNN models, such as AlexNet, Restnet-50, and VGG-16 removing the need to design and train new models from scratch. We apply this approach to a dataset of social media posts to classify informal documents as either related to suicide or non-suicide content. Our results demonstrate high classification accuracy, exceeding 99%. In addition, our 2D visual data representation conceals individual private information and helps explainability.

Advancements in large language models (LLMs) have opened new avenues for mental health monitoring through social media analysis. In this study, we present an iterative prompt engineering framework that significantly enhances the performance of the general-purpose LLM, GPT-4, for stress detection in social media posts, leveraging psychologist-informed hints. This approach achieved a substantial 17% accuracy improvement from 72% to 89% for the January 2025 version of GPT-4, alongside an 80% reduction in false positives compared to baseline zero-shot prompting. Our method not only surpassed domain-specific models like Mental-RoBERTa by 5% but also uniquely generates human-readable rationales. These rationales are crucial for mental health professionals, assisting them in understanding and validating the model’s outputs—a key benefit for sensitive mental health applications. These results highlight prompt engineering as a resource-efficient, transparent strategy to adapt general-purpose LLMs for specialized tasks, offering a scalable solution for mental health monitoring without the need for costly fine-tuning.

The COVID-19 disease pandemic spread rapidly worldwide and caused extensive human death and financial losses. Therefore, finding accurate, accessible, and inexpensive methods for diagnosing the disease has challenged researchers. To automate the process of diagnosing COVID-19 disease through images, several strategies based on deep learning, such as transfer learning and ensemble learning, have been presented. However, these techniques cannot deal with noises and their propagation in different layers. In addition, many of the datasets already being used are imbalanced, and most techniques have used binary classification, COVID-19, from normal cases. To address these issues, we use the blind/referenceless image spatial quality evaluator to filter out inappropriate data in the dataset. In order to increase the volume and diversity of the data, we merge two datasets. This combination of two datasets allows multi-class classification between the three states of normal, COVID-19, and types of pneumonia, including bacterial and viral types. A weighted multi-class cross-entropy is used to reduce the effect of data imbalance. In addition, a fuzzy fine-tuned Xception model is applied to reduce the noise propagation in different layers. Quantitative analysis shows that our proposed model achieves 96.60% accuracy on the merged test set, which is more accurate than previously mentioned state-of-the-art methods.