Electroencephalogram (EEG)-based emotion recognition has garnered significant attention in brain–computer interface research and healthcare applications. While deep learning models have been extensively studied, most are designed for classification tasks and struggle to accurately predict continuous emotional scores in regression settings. In this paper, we introduce EEG-RegNet, a novel deep neural network tailored for precise emotional score prediction across the continuous valence–arousal–dominance (VAD) space. EEG-RegNet tackles two core challenges: extracting subject-independent, emotion-relevant EEG features and mapping these features to fine-grained, continuous emotional scores. The model leverages 2D convolutional neural networks (CNNs) for spatial feature extraction and a 1D CNN for temporal dynamics, providing robust spatiotemporal modeling. A key innovation is the hybrid loss function, which integrates mean squared error (MSE) and cross-entropy (CE) with a Bernoulli penalty to enhance probability estimation and address sparsity in the emotional space. Extensive experiments on the DEAP dataset show that EEG-RegNet achieves state-of-the-art results in continuous emotional score prediction and attains 95% accuracy in fine-grained emotion classification, highlighting its scalability and precision in emotion recognition.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that impairs cognitive function, making early detection crucial for timely intervention. This study proposes a novel AD detection framework integrating gaze and head movement analysis via a dual-pathway convolutional neural network (CNN). Unlike conventional methods relying on linguistic, speech, or neuroimaging data, our approach leverages non-invasive video-based tracking, offering a more accessible and cost-effective solution to early AD detection. To enhance feature representation, we introduce GazeMap, a novel transformation converting 1D gaze and head pose time-series data into 2D spatial representations, effectively capturing both short- and long-term temporal interactions while mitigating missing or noisy data. The dual-pathway CNN processes gaze and head movement features separately before fusing them to improve diagnostic accuracy. We validated our framework using a clinical dataset (112 participants) from Konkuk University Hospital and an out-of-distribution dataset from senior centers and nursing homes. Our method achieved 91.09% accuracy on in-distribution data collected under controlled clinical settings, and 83.33% on out-of-distribution data from real-world scenarios, outperforming several time-series baseline models. Model performance was validated through cross-validation on in-distribution data and tested on an independent out-of-distribution dataset. Additionally, our gaze-saliency maps provide interpretable visualizations, revealing distinct AD-related gaze patterns.

Anomaly detection in multivariate time series is crucial for safeguarding the reliability of industrial, financial, and cybersecurity systems. However, conventional deep learning approaches often struggle with noise sensitivity and limited ability to capture complex intra- and inter-channel dynamics. To address these challenges, we propose a vision-inspired framework that transforms multivariate time series into multi-channel two-dimensional representations, allowing convolutional neural networks (CNNs) to extract local temporal patterns and spatial dependencies across variables. This representation enhances noise robustness and facilitates the modeling of periodic and evolving patterns often overlooked by 1D methods. To further improve performance, we incorporate Instance Normalization (IN), which preserves instance- and channel-specific variations critical for detecting subtle anomalies, and a Squeeze-and-Excitation (SE) block for adaptive channel weighting. Experiments on five benchmark datasets demonstrate that our approach consistently outperforms state-of-the-art methods, particularly under noisy conditions, highlighting its effectiveness and practicality for real-world anomaly detection tasks.

Large-scale Visual-Language Models have demonstrated powerful adaptability in video recognition tasks. However, existing methods typically rely on fine-tuning or text prompt tuning. In this paper, we propose a visual-only prompting method that employs watermark and trademark prompts to bridge the distribution gap of spatial-temporal video data with Visual-Language Models. Our watermark prompts, designed by a trainable prompt generator, are customized for each video clip. Unlike conventional visual prompts that often exhibit noise signals, watermark prompts are intentionally designed to be imperceptible, ensuring they are not misinterpreted as an adversarial attack. The trademark prompts, bespoke for each video domain, establish the identity of specific video types. Integrating watermark prompts into video frames and prepending trademark prompts to per-frame embeddings significantly boosts the capability of the Visual-Language Model to understand video. Notably, our approach improves the adaptability of the CLIP model to various video action recognition datasets, achieving performance gains of 16.8%, 18.4%, and 13.8% on HMDB-51, UCF-101, and the egocentric dataset EPIC-Kitchen-100, respectively. Additionally, our visual-only prompting method demonstrates competitive performance compared with existing fine-tuning and adaptation methods while requiring fewer learnable parameters. Moreover, through extensive ablation studies, we find the optimal balance between imperceptibility and adaptability. Code will be made available.