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About

About

João Paulo Cunha is Full Professor of Bioengineering & Electrical & Computers Engineering (ECE) at the Department of ECE of the Faculty of Engineering of the University of Porto (FEUP), Portugal; Senior Researcher at the INESC-TEC: Institute for Systems and Computer Engineering where he created and coordinates the BRAIN – Biomedical Research And INnovation - research group and co-founded the Center for Biomedical Engineering Research (C-BER) that aggregates ~40 researchers. Prof. Cunha is also a mentor/co-founder and contributor to several MedTech/DeepTech startups (eight until now) by advising and licensing intellectual property of innovative biomedical technology developed for several years in his lab, such as Biodevices (http://www.biodevices.pt), iLof-Intelligent Lab-on-Fiber (https://ilof.tech) and inSignals Neurotech (http://www.insignals-neurotech.com). He is visiting professor at the Neurology Dep., Faculty of Medicine of the University of Munich, Germany since 2002 and at the Carnegie Mellon University – Silicon Valley Campus, USA, between 2016 and 2021. He serves as Scientific Director of the Carnegie-Mellon | Portugal program since 2014.

He earned a degree in Electronics and Telecommunications engineering (1989), a Ph.D. (1996) and an Habilitation (“Agregação”) degree (2009) in Electrical Engineering all at the University of Aveiro, Portugal.

Dr. Cunha is Senior Member of the IEEE – Institute of Electrical & Electronics Engineers - (2004), member of the Editorial Board of NATURE/Scientific Reports (5th most cited journal in 2022) and Associate Editor of FRONTIERS/Signal Processing journal. He is also habitual reviewer of several IEEE journals and other relevant scientific journals such as PLoS ONE or Movement Disorders. He is an internationally renowned expert in advanced biosignal processing, human motion analysis and neuroimaging. He has supervised and co-supervised more than 15 PhD & Post-doc students in his areas of R&D. He received several awards, being the most relevant the European Epilepsy Academy (EUREPA) “Best Contribution for Clinical Epileptology” Award in 2002. Prof. Cunha is co-author of +200 scientific publications and 43 patents from 10 patent families, holding an h-index of 34 (Google Scholar), with +4,500 citations.

Details

Details

  • Name

    João Paulo Cunha
  • Role

    Centre Coordinator
  • Since

    01st January 2013
028
Publications

2025

P083 ASSESSING FUNCTIONAL THALAMO-CORTICAL CONNECTIVITY IN ADULTS WITH FRONTAL AND TEMPORAL LOBE EPILEPSY

Authors
Dias, AM; Cunha, JP; Mehrkens, J; Kaufmann, E;

Publication
Neuromodulation: Technology at the Neural Interface

Abstract

2025

Exploring image and skeleton-based action recognition approaches for clinical in-bed classification of simulated epileptic seizure movements

Authors
Karácsony, T; Fearns, N; Birk, D; Trapp, SD; Ernst, K; Vollmar, C; Rémi, J; Jeni, LA; De la Torre, F; Cunha, JPS;

Publication
EXPERT SYSTEMS WITH APPLICATIONS

Abstract
Epileptic seizure classification based on seizure semiology requires automated, quantitative approaches to support the diagnosis of epilepsy, which affects 1 % of the world's population. Current approaches address the problem on a seizure level, neglecting the detailed evaluation of the classification of the underlying action features, also known as Movements of Interest (MOIs), which are critical for epileptologists in determining their classifications. Moreover, it hinders objective comparison of these approaches and attribution of performance differences due to datasets, intra-dataset MOI distribution, or architecture variations. Objective evaluation of action recognition techniques is crucial, with MOIs serving as foundational elements of semiology for clinical in-bed applications to facilitate epileptic seizure classification. However, until now, there were no MOI datasets available nor benchmarks comparing different action recognition approaches for this clinical problem. Therefore, as a pilot, we introduced a novel, simulated seizure semiology dataset carried out by 8 experienced epileptologists in an EMU bed, consisting of 7 MOI classes. We compare several computer vision methods for MOI classification, two image-based (I3D and Uniformerv2), and two skeleton-based (ST-GCN++ and PoseC3D) action recognition approaches. This study emphasizes the advantages of a 2-stage skeleton-based action recognition approach in a transfer learning setting (4 classes) and the multi-scale challenge of MOI classification (7 classes), advocating for the integration of skeleton-based methods with hand gesture recognition technologies in the future. The study's controlled MOI simulation dataset provides us with the opportunity to advance the development of automated epileptic seizure classification systems, paving the way for enhancing their performance and having the potential to contribute to improved patient care.

2025

Fiber correlational tractography with neurovascular coupling and cognition in hypertension

Authors
Fortunato, M; Morais, R; Santana, I; Castro, P; Polónia, J; Azevedo, E; Cunha, JP; Monteiro, A;

Publication
NEUROSCIENCE

Abstract
Hypertension is the primary risk factor for cerebral small vessel disease (CSVD). However, its mechanistic links are yet to be completely understood. Advancements in diffusion-weighted magnetic resonance imaging (dMRI) increased sensitivity in detecting subtle white matter (WM) structural integrity changes. 44 hypertension patients without symptomatic CSVD underwent multi-modal evaluation of cerebral structure and function, including dMRI, neuropsychological tests and transcranial Doppler monitoring of the right middle cerebral artery (MCA) and left posterior cerebral artery (PCA) to assess neurovascular coupling (NVC). In the PCA, the modeled NVC curve was studied. We examined the cross-sectional relationship of WM integrity with NVC and cognitive performance, using correlational tractography. Diffusion measures from two dMRI models were used: fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity from diffusion tensor imaging, and quantitative anisotropy (QA) and isotropy from q-space diffeomorphic reconstruction. Regarding the NVC in the PCA, vascular elastic properties and initial response speed markers indicated better functional hyperemia with better WM integrity. However, the amplitude suggested increased NVC with worse WM integrity. In the MCA, increased NVC was associated with lower WM integrity. Better cognitive performance associated with preserved WM integrity. Increased functional hyperemia despite worse WM integrity may reflect less efficient NVC in hypertensive patients, potentially arising from (mal)adaptive mechanisms and brain network reorganization in response to CSVD. This observational study highlights the potential of transcranial Doppler and QA as susceptibility markers of pre-symptomatic CSVD.

2024

Studying the Influence of Multisensory Stimuli on a Firefighting Training Virtual Environment

Authors
Narciso, D; Melo, M; Rodrigues, S; Cunha, JP; Vasconcelos-Raposo, J; Bessa, M;

Publication
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS

Abstract
How we perceive and experience the world around us is inherently multisensory. Most of the Virtual Reality (VR) literature is based on the senses of sight and hearing. However, there is a lot of potential for integrating additional stimuli into Virtual Environments (VEs), especially in a training context. Identifying the relevant stimuli for obtaining a virtual experience that is perceptually equivalent to a real experience will lead users to behave the same across environments, which adds substantial value for several training areas, such as firefighters. In this article, we present an experiment aiming to assess the impact of different sensory stimuli on stress, fatigue, cybersickness, Presence and knowledge transfer of users during a firefighter training VE. The results suggested that the stimulus that significantly impacted the user's response was wearing a firefighter's uniform and combining all sensory stimuli under study: heat, weight, uniform, and mask. The results also showed that the VE did not induce cybersickness and that it was successful in the task of transferring knowledge.

2024

Assessing the perceptual equivalence of a firefighting training exercise across virtual and real environments

Authors
Narciso, D; Melo, M; Rodrigues, S; Dias, D; Cunha, J; Vasconcelos Raposo, J; Bessa, M;

Publication
VIRTUAL REALITY

Abstract
The advantages of Virtual Reality (VR) over traditional training, together with the development of VR technology, have contributed to an increase in the body of literature on training professionals with VR. However, there is a gap in the literature concerning the comparison of training in a Virtual Environment (VE) with the same training in a Real Environment (RE), which would contribute to a better understanding of the capabilities of VR in training. This paper presents a study with firefighters (N = 12) where the effect of a firefighter training exercise in a VE was evaluated and compared to that of the same exercise in a RE. The effect of environments was evaluated using psychophysiological measures by evaluating the perception of stress and fatigue, transfer of knowledge, sense of presence, cybersickness, and the actual stress measured through participants' Heart Rate Variability (HRV). The results showed a similar perception of stress and fatigue between the two environments; a positive, although not significant, effect of the VE on the transfer of knowledge; the display of moderately high presence values in the VE; the ability of the VE not to cause symptoms of cybersickness; and finally, obtaining signs of stress in participants' HRV in the RE and, to a lesser extent, signs of stress in the VE. Although the effect of the VE was shown to be non-comparable to that of the RE, the authors consider the results encouraging and discuss some key factors that should be addressed in the future to improve the results of the training VE.

Supervised
thesis

2023

Person Authentication in Hazardous Work Environments: Exploring ECG and Respiration Signals as a continuous biometric method

Author
Mafalda Alexandra Faria Ferreira

Institution
UP-FEUP

2023

Explainable Deep Learning Based Epileptic Seizure Classification with Clinical 3D Motion Capture

Author
Tamás Karácsony

Institution
UP-FEUP

2023

Towards a Novel Neuroengineering Approach to Adaptive Neurostimulation in Epilepsy

Author
Ana Marta de Oliveira Dias

Institution
UP-FEUP

2023

Robust Distributed Real-Time Processing Architecture for Man-Machine Cyber-Symbiosis

Author
Luís Miguel Maia Marques Torres e Silva

Institution
UP-FEUP

2022

PhysioIntent: Towards Biosignal-based Robot Awareness Identification of Human Movement Intentions

Author
Ana Filipa de Sousa Ferreira

Institution
UP-FEUP