WARM-VR provides a new public dataset of wristband and ECG signals from 31 people in VR stress-relaxation experiences with olfactory cues, plus baseline ML classification results for valence and arousal.
DEAP: A database for emotion analysis; Using physiological signals
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
fields
cs.LG 3years
2026 3roles
background 2polarities
background 2representative citing papers
CNNs achieve the best overall accuracy and efficiency for PPG-based affect recognition, with Transformers and Mamba showing no consistent advantage under identical evaluation conditions.
A review synthesizes evidence from EEG, EMG, ECG, PPG and ocular signals to argue that waveform morphology, rather than modality or model class, primarily determines TSC performance and interpretability.
citing papers explorer
-
Introducing WARM-VR: Benchmark Dataset for Multimodal Wearable Affect Recognition in Virtual Reality
WARM-VR provides a new public dataset of wristband and ECG signals from 31 people in VR stress-relaxation experiences with olfactory cues, plus baseline ML classification results for valence and arousal.
-
PPG-Based Affect Recognition with Long-Range Deep Models: A Measurement-Driven Comparison of CNN, Transformer, and Mamba Architectures
CNNs achieve the best overall accuracy and efficiency for PPG-based affect recognition, with Transformers and Mamba showing no consistent advantage under identical evaluation conditions.
-
Modality vs. Morphology: A Framework for Time Series Classification for Biological Signals
A review synthesizes evidence from EEG, EMG, ECG, PPG and ocular signals to argue that waveform morphology, rather than modality or model class, primarily determines TSC performance and interpretability.