Guillaume Chanel

Guillaume Chanel is a researcher at the University of Geneva, affiliated with the Department of Computer Science under the Faculty of Sciences, and the Centre interfacultaire en sciences affectives. His work spans affective computing, emotion recognition, and physiological signal analysis in human-computer interaction contexts.

• Affective Computing
• Emotion Recognition
• Brain-Computer Interfaces
• Multimodal Interaction
• Physiological Computing
• Human-Computer Interaction

Chanel's research focuses on integrating physiological signals (e.g., EEG, electrodermal activity) with computational models to enhance emotion detection and adaptive systems. Recent work includes attention mechanisms in U-Net for medical imaging, variational autoencoders for PPG denoising, and biofeedback-driven gaming systems. His publications emphasize multimodal fusion, transfer learning, and latent representation techniques.

His 15 most recent articles (2025–2020) analyze advanced deep learning architectures for emotion and impression recognition, physiological signal processing, and adaptive game design. Key themes include attention mechanisms, variational autoencoders, multimodal synchronization, and biofeedback applications.

Chanel collaborates with the Computer Vision and Multimedia Laboratory and the Centre universitaire d'informatique, contributing to open datasets and tools like the Toolbox for Emotional feAture extraction from Physiological signals (TEAP).