21 citations · 66 across the 20 of their papers we have counts for
23 papers
Towards a Unified Modality-Agnostic Multimodal Framework for Cognitive Workload Assessment
Stefanos Gkikas, Christian Arzate Cruz, Calvin Joseph +2
Cognitive workload reflects the mental effort required during task performance and is central to the design of adaptive human-machine systems. The use of biosignals to measure cogn…
UNWIND: Any-Length Facial Video for Stress Detection without Temporal Windowing
Stefanos Gkikas, Christian Arzate Cruz, Eric Nichols +2
Automatic stress recognition from facial video provides a non-contact approach for affective monitoring. However, most existing video-based methods divide complete recordings into…
FUSE: Frame-Unified Stress Estimation from Facial Video
Stefanos Gkikas, Thomas Kassiotis, Yang Guo +2
Automatic stress detection from facial video offers a practical path to non-intrusive affect monitoring, yet existing video-based approaches commonly decompose full recordings into…
MUPAE: Multimodal Unified Perception with Asymmetric Attention for Emotion Assessment
Stefanos Gkikas, Eric Nichols, Christian Arzate Cruz +1
Automatic emotion assessment can benefit from combining neural and behavioral signals, but many multimodal approaches rely on separate, modality-specific feature-extraction pipelin…
Beyond the Raw Waveform: Fusing Visual Representations of EDA for Stress Detection
Stefanos Gkikas, Thomas Kassiotis, Yang Guo +5
Electrodermal activity (EDA) is widely used in automatic stress detection, yet most pipelines treat it only as a raw one-dimensional waveform. This study examines whether complemen…
A Multi-Scale Temporal Framework with Dynamic Fusion for EEG-Based Emotion Recognition
Stefanos Gkikas, Yang Guo, Guangliang Li +3
Mixed emotions represent a clinically relevant but still underexplored target for automatic emotion recognition. EEG provides millisecond-level access to neural activity, yet most…
Efficient and Interpretable Body-Based Emotion Recognition with Lightweight Temporal Convolutional Networks
Christian Arzate Cruz, Stefanos Gkikas, Houshyar Asadi
Body-based emotion recognition is important for real-time affective systems, but graph-based skeleton models can be computationally expensive. This paper studies whether lightweigh…
Explainable graph attention network for stress recognition (StressGAT) via differential action units
Thomas Kassiotis, Stefanos Gkikas, Nikolaos Smyrnis +1
Stress is a dynamic process characterized by significant individual variability in facial expression. Traditional architectures, such as Recurrent Neural Networks (RNNs) and Convol…
ReFace: Reorganizing Facial Spatiotemporal Representations for Improved Pain Assessment
Stefanos Gkikas, Yu Fang, Christian Arzate Cruz +2
Automatic pain assessment from facial video remains challenging due to the spatial heterogeneity of pain-related facial cues. This study proposes ReFace, a spatial reorganization p…
A Unified Tokenization Framework for Pain Recognition using Heterogeneous 3D Modalities
Stefanos Gkikas, Christian Arzate Cruz, Valentina Becchetti +3
Pain is a complex and pervasive phenomenon affecting a large percentage of the population, and accurate assessment is essential for effective clinical management and intervention.…
A comparative and critical study of EEGNet for fNIRS-driven cognitive load classification
Mehshan Ahmed Khan, Houshyar Asadi, Li Zhang +8
Accurately classifying cognitive load from functional near-infrared spectroscopy (fNIRS) signals remains a significant challenge due to temporal variability, inter-subject differen…
One-Block Transformer (1BT) for EEG-Based Cognitive Workload Assessment
Stefanos Gkikas, Christian Arzate Cruz, Thomas Kassiotis +3
Accurate and continuous estimation of cognitive workload is fundamental to creating adaptive human-machine systems. However, designing architectures that balance representational c…
A Lightweight Transformer for Pain Recognition from Brain Activity
Stefanos Gkikas, Christian Arzate Cruz, Yu Fang +6
Pain is a multifaceted and widespread phenomenon with substantial clinical and societal burden, making reliable automated assessment a critical objective. This paper presents a lig…
Efficient Emotion-Aware Iconic Gesture Prediction for Robot Co-Speech
Edwin C. Montiel-Vazquez, Christian Arzate Cruz, Stefanos Gkikas +3
Co-speech gestures increase engagement and improve speech understanding. Most data-driven robot systems generate rhythmic beat-like motion, yet few integrate semantic emphasis. To…
Efficient Pain Recognition via Respiration Signals: A Single Cross-Attention Transformer Multi-Window Fusion Pipeline
Stefanos Gkikas, Ioannis Kyprakis, Manolis Tsiknakis
Pain is a complex condition that affects a large portion of the population. Accurate and consistent evaluation is essential for individuals experiencing pain and supports the devel…
Multi-Representation Diagrams for Pain Recognition: Integrating Various Electrodermal Activity Signals into a Single Image
Stefanos Gkikas, Ioannis Kyprakis, Manolis Tsiknakis
Pain is a multifaceted phenomenon that affects a substantial portion of the population. Reliable and consistent evaluation supports individuals experiencing pain and enables the de…
Tiny-BioMoE: a Lightweight Embedding Model for Biosignal Analysis
Stefanos Gkikas, Ioannis Kyprakis, Manolis Tsiknakis
Pain is a complex and pervasive condition that affects a significant portion of the population. Accurate and consistent assessment is essential for individuals suffering from pain,…
A Pain Assessment Framework based on multimodal data and Deep Machine Learning methods
Stefanos Gkikas
From the original abstract: This thesis initially aims to study the pain assessment process from a clinical-theoretical perspective while exploring and examining existing automatic…
PainFormer: a Vision Foundation Model for Automatic Pain Assessment
Stefanos Gkikas, Raul Fernandez Rojas, Manolis Tsiknakis
Pain is a manifold condition that impacts a significant percentage of the population. Accurate and reliable pain evaluation for the people suffering is crucial to developing effect…
A Full Transformer-based Framework for Automatic Pain Estimation using Videos
Stefanos Gkikas, Manolis Tsiknakis
The automatic estimation of pain is essential in designing an optimal pain management system offering reliable assessment and reducing the suffering of patients. In this study, we…
Synthetic Thermal and RGB Videos for Automatic Pain Assessment utilizing a Vision-MLP Architecture
Stefanos Gkikas, Manolis Tsiknakis
Pain assessment is essential in developing optimal pain management protocols to alleviate suffering and prevent functional decline in patients. Consequently, reliable and accurate…
Twins-PainViT: Towards a Modality-Agnostic Vision Transformer Framework for Multimodal Automatic Pain Assessment using Facial Videos and fNIRS
Stefanos Gkikas, Manolis Tsiknakis
Automatic pain assessment plays a critical role for advancing healthcare and optimizing pain management strategies. This study has been submitted to the First Multimodal Sensing Gr…
Multi-task Neural Networks for Pain Intensity Estimation using Electrocardiogram and Demographic Factors
Stefanos Gkikas, Chariklia Chatzaki, Manolis Tsiknakis
Pain is a complex phenomenon which is manifested and expressed by patients in various forms. The immediate and objective recognition of it is a great of importance in order to atta…