arXiv AI By Ioannis N. Ziogas, Leontios J. Hadjileontiadis, Ahsan H. Khandoker, Aamna Al Shehhi

Self-Supervised Graph Representation Learning for In-The-Wild Wearable and Smartphone based Emotion Recognition

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arXiv AI
Jul 10

Graph-Regularized Deep Learning for EEG-Based Emotion Recognition with Psychologically-Grounded Label Structure

arXiv:2607. 07773v1 Announce Type: cross Abstract: EEG-based emotion recognition is critical for mental health monitoring and affective brain-computer interfaces, yet existing deep learning approaches often treat emotion classes as isolated labels, ignoring their psychological interdependencies.

By Dongyang Kuang, Zizheng Ma, Yushan Zhang, Xiaocong Zeng
Hugging Face Trending Papers
Jul 8

Graph-Regularized Deep Learning for EEG-Based Emotion Recognition with Psychologically-Grounded Label Structure

EEG-based emotion recognition is critical for mental health monitoring and affective brain-computer interfaces, yet existing deep learning approaches often treat emotion classes as isolated labels, ignoring their psychological interdependencies. We propose a graph-regularized learning framework that conceptualizes emotions as nodes in a graph where edges encode proximity based on dimensional emotion theories.

arXiv AI
Aug 18

Take it Personally: The Limits of General SSL Representations for Real-Life PPG Emotion Detection

arXiv:2608. 14675v1 Announce Type: cross Abstract: While Self-Supervised Learning (SSL) effectively extracts general representations from noisy, unconstrained physiological signals such as photoplethysmography (PPG), its suitability for highly subjective tasks remains unproven.

By Dominika Kunc, Przemys{\l}aw Kazienko, Stanis{\l}aw Saganowski
arXiv Machine Learning
Sep 21

From Stress to Affect: Multimodal Deep Learning for Physiological Emotion Recognition Across Wearable Sensor Modalities

The study compares temporal deep learning models—Bidirectional LSTM, Temporal Convolutional Network, and Transformer—for physiological emotion recognition using two multimodal wearable datasets, WESAD and EmoWear. Experiments evaluate wrist-only, chest-only, and multimodal sensor configurations with participant-independent leave-one-subject-out cross-validation, and also explore ensembles, sensor ablation, sampling frequency, and saliency analysis. Results show that the best architecture varies by dataset, multimodal sensing consistently outperforms single-site configurations, and a 4 Hz sampling rate offers a cost-effective operating point.

By Desta Haileselassie Hagos, Saurav Keshari Aryal, Legand L. Burge
arXiv Machine Learning
Sep 15

Bridging the Gap in ECG-Based Emotion Recognition: A Unified Evaluation of Deep Learning Models

The paper evaluates deep learning models for electrocardiogram‑based emotion recognition, focusing on generalization across datasets rather than dataset‑specific performance. It introduces two open‑source tools—ARRC for standardized benchmarking and ARDT for inter‑dataset training—to merge three public AER datasets (CUADS, ASCERTAIN, DREAMER) into a more variable benchmark. Using these tools, the authors compare three prominent deep learning architectures and two CNN baselines with hyperparameter tuning and 10‑fold cross‑validation, revealing trade‑offs between accuracy and model complexity and providing a reproducible benchmark for future research.

By Timothy C Sweeney-Fanelli, Ajan Ahmed, Masudul Imtiaz