Biosignals and computer vision analysis methods for biomedical applications
Over the last years, significant advancements in biosignal processing, imaging technologies and computer vision have profoundly enhanced biomedical applications, particularly in neurology and affective computing. Advanced computational models combined with robust machine/deep learning and artificial intelligence techniques have enabled the extraction and interpretation of complex biomedical signals, images or videos advancing our diagnostic and therapeutic capabilities.
These technologies provide deeper insights into biomedical mechanisms and behavioural patterns, paving the way for innovative interventions. Moreover, real-time data analysis, enabled by edge computing and cloud integration, enhances responsiveness and scalability in biomedical systems, allowing for continuous and adaptive patient monitoring.
Multimodal analysis provides a comprehensive approach to problem-solving by integrating the specific and relevant to the pathology information from each modality. Fusion approaches aim at integrating data analyses and establishing synergic relationships for improved diagnostic accuracy. Recent advances in machine/deep learning (ML/DL) and artificial intelligence (AI) analyze the dataset and provide a computational model for automatic classification or decision making for the problem under investigation. The data analysis interpretation and the connection with the underlying physiological mechanisms may lead to is a deeper understanding of the pathophysiological states.
This special session aims to showcase the latest advancements and innovations in biosignal analysis, imaging, computer vision and computational techniques for biomedical applications, with a special focus on neurology, affective computing and psychology/psychiatry.
Session Organizer
Giorgos Giannakakis is an Associate Professor (under appointment) at the department of Electronic Engineering, Hellenic Mediterranean University and a Research Associate at the Institute of Computer Science, Foundation for Research and Technology Hellas (FORTH). He received his Dipl.-Ing. in Electrical and Computer Engineering from the National Technical University of Athens (NTUA) in 2003, his MSc and PhD in Biomedical Engineering from the Faculty of Medicine of the University of Patras – School of Electrical and Computer Engineering, NTUA in 2005 and 2009 respectively. As a principal investigator or research team member, he has attracted external funding in 8 European/national programs and has been involved in 24 research projects (10 European, 14 national projects), serving as principal investigator in 4 research projects. His research has been recognized with awards from journals (e.g., Biomedical Signal Processing and Control, Signals, IEEE Reviews in Biomedical Engineering), institutions, international competitions (e.g. ACII2024) and scholarships (e.g., IKY postdoctoral fellowship). He has authored over 75 publications in international journals and conferences in the fields of medical informatics, biosignal processing/analysis, computer vision, machine learning, affective computing, and computational neuroscience
Topics of interest
Submissions are invited on innovative approaches, tools, and methodologies that contribute to the following outcomes:
- Biosignals and neurophysiological analysis methods (e.g. EEG, ERP, EMG, EDA, ECG, HRV, PPG)
- computer vision analysis methods (video analysis, facial videos, body pose, eye-tracking)
- neuroimaging methods (e.g. MRI/fMRI, PET, CT, SPECT)
- neurophysiological and imaging methods for neurological applications (e.g. epilepsy, Alzheimer)
- computer vision and biosignals methods in affective computing
- multimodal biomedical data fusion
- machine/deep learning applications in biomedicine