Master
FAQ
 
 

Below you will find frequently asked questions, divided over four different groups. First, a generic FAQ with information applying to a broad set of master degrees and then more specific FAQs applying to specific programs only.

FAQ for all master degrees

“Signal Processing in the AI era” was the tagline of this year’s IEEE International Conference on Acoustics, Speech and Signal Processing, taking place in Rhodes, Greece.

In this context, Brent de Weerdt, Xiangyu Yang, Boris Joukovsky, Alex Stergiou and Nikos Deligiannis presented ETRO’s research during poster sessions and oral presentations, with novel ways to process and understand graph, video, and audio data. Nikos Deligiannis chaired a session on Graph Deep Learning, attended the IEEE T-IP Editorial Board Meeting, and had the opportunity to meet with collaborators from the VUB-Duke-Ugent-UCL joint lab.

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General

Management

Public

Location-based Lists

Occupation-based Lists

On Januari 21st 2025 at 17:00, Fahimeh Akbarian will defend their PhD entitled “INVESTIGATING EXCITATION/INHIBITION BALANCE IN MS THROUGH APERIODIC NEUROPHYSIOLOGICAL ACTIVITY”.

Everybody is invited to attend the presentation in room I.0.01 or online via this link.

Abstract ■

Multiple sclerosis (MS) is a chronic central nervous system disease characterised by neuroinflammation, demyelination, and neurodegeneration, leading to physical and cognitive impairments. Cognitive deficits frequently affect working memory, information processing speed, and attention. Although their mechanisms are not fully understood, evidence suggests that synaptic loss, particularly of inhibitory synapses, disrupts cortical excitation–inhibition (E/I) balance and contributes to cognitive dysfunction.

In this PhD project, we used magnetoencephalography (MEG) to investigate changes in the aperiodic 1/f spectral slope, a proposed marker of cortical E/I balance. A steeper slope indicates increased inhibition or reduced excitation. MEG data from healthy controls (HCs) and people with MS (pwMS) were analysed during resting-state, visuo-verbal n-back, and auditory oddball tasks. Data were source reconstructed, parcellated into 42 brain regions, and decomposed into periodic and aperiodic components using the specparam algorithm. Neuropsychological assessments measured information processing speed, verbal fluency, and visuospatial memory.

During resting-state, pwMS taking benzodiazepines showed steeper slopes in occipital, temporal, and prefrontal regions compared with pwMS who did not take benzodiazepines, independent of beta power, supporting the slope as an oscillation-independent measure. Among pwMS who did not take benzodiazepines, those with cognitive impairment displayed steeper slopes than cognitively preserved pwMS and HCs, suggesting compensatory overinhibition mechanism.

In the n-back task, a consistent post-stimulus steepening (increased inhibition) was observed across participants. However, pwMS showed flatter slopes following distractors, consistent with impaired inhibitory control. Greater task-induced steepening predicted better visuospatial memory in pwMS, whereas the opposite relationship was observed in HCs.

In the auditory oddball task, slope steepening persisted even after correcting for event-related fields. Salient stimuli induced stronger steepening, and trials needed response showed enhanced sensorimotor steepening. Slope modulation was correlated across oddball and n-back tasks, suggesting a trait-like index of cognitive control.

Overall, the 1/f slope captured both tonic and phasic inhibitory dynamics, differentiated medication effects, correlated with cognitive performance, and generalised across paradigms. These findings support its potential as a non-invasive biomarker for cognitive dysfunction in MS, warranting further longitudinal and multimodal validation.

De Vrije Universiteit Brussel wil met een supercomputer rampen zoals de waterbom in Wallonië van 4 jaar geleden, beter kunnen voorspellen. De nieuwe supercomputer kan met AI de informatie die radars en satellieten leveren sneller verwerken en zo sneller updates geven over de weersomstandigheden. 

https://www.vrt.be/vrtnws/nl/2025/11/30/supercomputer-rampen-waterbom

Hiva Houshyar participated in the final pitch event of the FARI AI Accelerator and presented her business idea and her pitch was selected as “Best Pitch” by the jury! 🎉

Her project, BB-GO, is building a navigation platform tailored to wheelchair users, focusing on personalised routing and urban accessibility. The vision is a world where wheelchair users can explore their city with the same confidence as anyone else.

Want to help shape BB-GO or link us to public authorities? Get in touch via Johan.Stiens@vub.be or Seyedeh.Hiva.Houshyar.Yazdian@vub.be

ETRO was highly visible and omni-present at the HealthTech Brussels event hosted by FARI, showcasing cutting-edge AI expertise in health. We hope the networking opportunities helped create valuable new connections with the entrepreneurs and clinicians who attended.

Loris Giordano got the best student paper award at the AMAI workshop of MICCAI 2025 for the paper “A modular deep-learning pipeline for automated aorta characterization on CT”, co-authored by Loris Giordano, Jakub Ceranka, Selene De Sutter, Kaoru Tanaka, Gert Van Gompel, Tom Lenaerts, and Jef Vandemeulebroucke.

Anass Hamdi got awarded an PhD fellowship strategic basic research for his research “AI-driven radiogenomic analysis for spatial glioblastoma subtyping” under supervision of Catharina Olsen, Jef Vandemeulebroucke, Johnny Duerinck and Wim Vranken.

Sarah Al Omari got awarded a PhD Fellowship fundamental research for her research “Exploring Neuromuscular Fatigue in Stroke Survivors: Central-Peripheral Interplay and the Potential of Transcranial Alternating Current Stimulation (tACS)” under supervision of Eva Swinnen, David BeckwĂ©e, Mahyar Firouzi and Bart Jansen.

Manuel Montoya received the best oral presentation award at the International Conference on Applied Physics & Imaging (ICAPI) 2025 in Tartu, Estonia, for the work “Efficient simulations of partially coherent light using the Generalized Van Cittert – Zernike Schell Propagator”, co-authored by Manuel Montoya, Maria J. Lopera Acosta, Yunfeng Nie, and David Blinder.

On October 8th 2025 at 16:00, Xinxin Dai will defend their PhD entitled “LEARNING BASED RECONSTRUCTION AND MEASUREMENT OF 3D HANDS USING A SINGLE DEPTH CAMERA”.

Everybody is invited to attend the presentation in room D.2.01 or online via this link.

Abstract ■

Accurate 3D reconstruction and measurement extraction of the human hand are critical for a wide range of hand-centric applications, such as the design of immobilization devices, prosthetic limb fabrication, and osteoarthritis evaluation. However, the recovery of high-fidelity hand geometry remains challenging due to the inherently incomplete and occluded nature of point clouds acquired from commodity depth sensors, which are limited by viewpoint constraints and self-occlusion. Furthermore, traditional manual measurement methods, which require static hand postures and the expertise of trained anthropometrists, are inadequate for capturing measurements under realistic, task-specific hand motions, limiting their applicability in dynamic or non-standard scenarios.

To address these limitations, this thesis introduces deep learning-based methodologies aimed at addressing key challenges in the reconstruction and measurement extraction of 3D hand shapes. Specifically, the main research challenges include: (i) What is the optimal hand posture for precise and reliable measurement? (ii) How to fast and precisely reconstruct a complete hand shape from multi-view partial point clouds under different postures? and (iii) How can we simultaneously complete partial point clouds and reconstruct their surfaces while preserving the raw data? (iv) How to achieve human identification by the shape and posture of hands? The first challenge derives from the complexity of the human hand, which consists of 34 muscles and 27 bones. This intricate structure enables a wide range of postural variations, often resulting in significant geometric deformations that introduce considerable biases in measurement accuracy. Second, depth cameras inherently capture only partial point clouds due to limited viewpoints and self-occlusions, resulting in incomplete representations that restrict the accurate reconstruction of full hand geometry. Third, the lack of high-resolution surface details in a single partial point cloud makes it challenging to simultaneously achieve both point cloud completion and high-fidelity surface reconstruction. Lastly, while previous studies on human identification have primarily focused on recording the velocities of pressing and releasing different keys, these approaches lack integration with vision-based hand motion analysis.

To overcome the aforementioned challenges, this thesis introduces four deep learning-based models. The first model is  Measure4DHand, designed for automatic extraction of dynamic hand measurements from partial hand point cloud sequences. By analyzing the variation in measurement values induced by skin deformation across different hand postures, this model facilitates the identification of optimal hand postures for accurate and consistent measurements. The second model is PatientHandNet, which focuses on reconstructing a high-fidelity 3D hand shape in a canonical open-palm pose using four depth images captured from different viewpoints by a single commodity depth sensor To facilitate the proposed model, a large-scale multi-view synthetic dataset with a wide variety of hand shapes and hand poses and corresponding ground truth hand shapes in a canonical open palm pose is proposed and a novel real-world dataset by capturing 18 subjects (13 males and 5 females) via a structure sensor Mark I employed in an iPad and hired a professional anthropometrist to obtain corresponding ground-truth hand biometric measurements. The third contribution proposed TailoredTemplateFit, which is, to the best of our knowledge, the first deep learning-based method in the literature is proposed to simultaneously address point cloud completion and surface reconstruction while preserving the raw data of the input. This model is trained and validated on two large-scale datasets: a large-scale 50K head dataset and 300K hand dataset with a wide variety of shapes and poses and corresponding ground truth shapes. Lastly, we present KD-Net, which explores a novel visual modality of keystroke dynamics for human identification from RGB-D image sequences. To support this research, a novel dataset dubbed KD-MultiModal is created, comprising 243.2 K frames of RGB images and depth images.

Our proposed methods consistently outperform the reference methods from the literature, as demonstrated through comprehensive experimentation. The research works have been published in various reputable journals and conferences, highlighting their impact in both academic and industrial contexts.

On October 6th 2025 at 16:00, Ran Zhao will defend their PhD entitled “DEEP LEARNING-BASED HUMAN POSTURE NORMALIZATION AND AUTOMATIC ANTHROPOMETRIC MEASUREMENT”.

Everybody is invited to attend the presentation in room D.2.01 or online via this link.

Abstract ■

Accurate and user-friendly anthropometric measurement remains a major challenge in computer vision, as existing approaches typically require controlled scanning conditions, standard postures, or unclothed bodies. These constraints limit their usability in practical scenarios.

This thesis proposes a sequence of deep learning-based solutions to overcome these limitations. We first introduce OrienNormNet, an iterative network for robust orientation normalization, ensuring that scans are consistently aligned without manual preprocessing. Building on this, PoseNormNet is presented as the first posture normalization framework that transforms arbitrarily posed scans into a canonical T-pose while preserving identity details, removing the need for skeleton rigging. Next, W2H-Net demonstrates the feasibility of directly estimating the waist-to-hip ratio from partial dressed scans, showing that reliable indicators can be derived even from incomplete data. Finally, MeasureXpert provides a breakthrough toward real-world usability: it enables automatic extraction of anthropometric measurements from only two unregistered, partial, and clothed scans acquired in arbitrary poses.

To support these developments, the BWM dataset was synthesized for training, validation, and evaluation. Comprehensive experiments on both synthetic and real-world data confirm the effectiveness and robustness of the proposed methods. Collectively, the contributions progressively address key challenges related to cost, posture, and clothing, moving the field closer to practical, flexible, and accessible body measurement solutions.

The algorithms presented in this thesis have been disseminated through prestigious journals and conferences, demonstrating a modest yet meaningful impact on both academic research and industrial applications.

Non-EER staff needs a single permit, this request can take up to several months. The VUB requests this but the employee needs to deliver a large list of docs.

Karin needs to know if you want to hire someone new, or if the status of your researcher changes from PhD to post doc or researcher long in advance (3 M) to make sure all the steps are taken.

Karin cannot launch the procedure without a position and cannot create a position without budget (PKC) for at least 3 M

M&O – Wie vraagt de Single permit aan?

M&O – Wat gebeurt er als mijn statuut wijzigt? (Single Permit)




FAQ for the Master Applied Computer Science

“Signal Processing in the AI era” was the tagline of this year’s IEEE International Conference on Acoustics, Speech and Signal Processing, taking place in Rhodes, Greece.

In this context, Brent de Weerdt, Xiangyu Yang, Boris Joukovsky, Alex Stergiou and Nikos Deligiannis presented ETRO’s research during poster sessions and oral presentations, with novel ways to process and understand graph, video, and audio data. Nikos Deligiannis chaired a session on Graph Deep Learning, attended the IEEE T-IP Editorial Board Meeting, and had the opportunity to meet with collaborators from the VUB-Duke-Ugent-UCL joint lab.

Featured articles:

General

Management

Public

Location-based Lists

Occupation-based Lists

On Januari 21st 2025 at 17:00, Fahimeh Akbarian will defend their PhD entitled “INVESTIGATING EXCITATION/INHIBITION BALANCE IN MS THROUGH APERIODIC NEUROPHYSIOLOGICAL ACTIVITY”.

Everybody is invited to attend the presentation in room I.0.01 or online via this link.

Abstract ■

Multiple sclerosis (MS) is a chronic central nervous system disease characterised by neuroinflammation, demyelination, and neurodegeneration, leading to physical and cognitive impairments. Cognitive deficits frequently affect working memory, information processing speed, and attention. Although their mechanisms are not fully understood, evidence suggests that synaptic loss, particularly of inhibitory synapses, disrupts cortical excitation–inhibition (E/I) balance and contributes to cognitive dysfunction.

In this PhD project, we used magnetoencephalography (MEG) to investigate changes in the aperiodic 1/f spectral slope, a proposed marker of cortical E/I balance. A steeper slope indicates increased inhibition or reduced excitation. MEG data from healthy controls (HCs) and people with MS (pwMS) were analysed during resting-state, visuo-verbal n-back, and auditory oddball tasks. Data were source reconstructed, parcellated into 42 brain regions, and decomposed into periodic and aperiodic components using the specparam algorithm. Neuropsychological assessments measured information processing speed, verbal fluency, and visuospatial memory.

During resting-state, pwMS taking benzodiazepines showed steeper slopes in occipital, temporal, and prefrontal regions compared with pwMS who did not take benzodiazepines, independent of beta power, supporting the slope as an oscillation-independent measure. Among pwMS who did not take benzodiazepines, those with cognitive impairment displayed steeper slopes than cognitively preserved pwMS and HCs, suggesting compensatory overinhibition mechanism.

In the n-back task, a consistent post-stimulus steepening (increased inhibition) was observed across participants. However, pwMS showed flatter slopes following distractors, consistent with impaired inhibitory control. Greater task-induced steepening predicted better visuospatial memory in pwMS, whereas the opposite relationship was observed in HCs.

In the auditory oddball task, slope steepening persisted even after correcting for event-related fields. Salient stimuli induced stronger steepening, and trials needed response showed enhanced sensorimotor steepening. Slope modulation was correlated across oddball and n-back tasks, suggesting a trait-like index of cognitive control.

Overall, the 1/f slope captured both tonic and phasic inhibitory dynamics, differentiated medication effects, correlated with cognitive performance, and generalised across paradigms. These findings support its potential as a non-invasive biomarker for cognitive dysfunction in MS, warranting further longitudinal and multimodal validation.

De Vrije Universiteit Brussel wil met een supercomputer rampen zoals de waterbom in Wallonië van 4 jaar geleden, beter kunnen voorspellen. De nieuwe supercomputer kan met AI de informatie die radars en satellieten leveren sneller verwerken en zo sneller updates geven over de weersomstandigheden. 

https://www.vrt.be/vrtnws/nl/2025/11/30/supercomputer-rampen-waterbom

Hiva Houshyar participated in the final pitch event of the FARI AI Accelerator and presented her business idea and her pitch was selected as “Best Pitch” by the jury! 🎉

Her project, BB-GO, is building a navigation platform tailored to wheelchair users, focusing on personalised routing and urban accessibility. The vision is a world where wheelchair users can explore their city with the same confidence as anyone else.

Want to help shape BB-GO or link us to public authorities? Get in touch via Johan.Stiens@vub.be or Seyedeh.Hiva.Houshyar.Yazdian@vub.be

ETRO was highly visible and omni-present at the HealthTech Brussels event hosted by FARI, showcasing cutting-edge AI expertise in health. We hope the networking opportunities helped create valuable new connections with the entrepreneurs and clinicians who attended.

Loris Giordano got the best student paper award at the AMAI workshop of MICCAI 2025 for the paper “A modular deep-learning pipeline for automated aorta characterization on CT”, co-authored by Loris Giordano, Jakub Ceranka, Selene De Sutter, Kaoru Tanaka, Gert Van Gompel, Tom Lenaerts, and Jef Vandemeulebroucke.

Anass Hamdi got awarded an PhD fellowship strategic basic research for his research “AI-driven radiogenomic analysis for spatial glioblastoma subtyping” under supervision of Catharina Olsen, Jef Vandemeulebroucke, Johnny Duerinck and Wim Vranken.

Sarah Al Omari got awarded a PhD Fellowship fundamental research for her research “Exploring Neuromuscular Fatigue in Stroke Survivors: Central-Peripheral Interplay and the Potential of Transcranial Alternating Current Stimulation (tACS)” under supervision of Eva Swinnen, David BeckwĂ©e, Mahyar Firouzi and Bart Jansen.

Manuel Montoya received the best oral presentation award at the International Conference on Applied Physics & Imaging (ICAPI) 2025 in Tartu, Estonia, for the work “Efficient simulations of partially coherent light using the Generalized Van Cittert – Zernike Schell Propagator”, co-authored by Manuel Montoya, Maria J. Lopera Acosta, Yunfeng Nie, and David Blinder.

On October 8th 2025 at 16:00, Xinxin Dai will defend their PhD entitled “LEARNING BASED RECONSTRUCTION AND MEASUREMENT OF 3D HANDS USING A SINGLE DEPTH CAMERA”.

Everybody is invited to attend the presentation in room D.2.01 or online via this link.

Abstract ■

Accurate 3D reconstruction and measurement extraction of the human hand are critical for a wide range of hand-centric applications, such as the design of immobilization devices, prosthetic limb fabrication, and osteoarthritis evaluation. However, the recovery of high-fidelity hand geometry remains challenging due to the inherently incomplete and occluded nature of point clouds acquired from commodity depth sensors, which are limited by viewpoint constraints and self-occlusion. Furthermore, traditional manual measurement methods, which require static hand postures and the expertise of trained anthropometrists, are inadequate for capturing measurements under realistic, task-specific hand motions, limiting their applicability in dynamic or non-standard scenarios.

To address these limitations, this thesis introduces deep learning-based methodologies aimed at addressing key challenges in the reconstruction and measurement extraction of 3D hand shapes. Specifically, the main research challenges include: (i) What is the optimal hand posture for precise and reliable measurement? (ii) How to fast and precisely reconstruct a complete hand shape from multi-view partial point clouds under different postures? and (iii) How can we simultaneously complete partial point clouds and reconstruct their surfaces while preserving the raw data? (iv) How to achieve human identification by the shape and posture of hands? The first challenge derives from the complexity of the human hand, which consists of 34 muscles and 27 bones. This intricate structure enables a wide range of postural variations, often resulting in significant geometric deformations that introduce considerable biases in measurement accuracy. Second, depth cameras inherently capture only partial point clouds due to limited viewpoints and self-occlusions, resulting in incomplete representations that restrict the accurate reconstruction of full hand geometry. Third, the lack of high-resolution surface details in a single partial point cloud makes it challenging to simultaneously achieve both point cloud completion and high-fidelity surface reconstruction. Lastly, while previous studies on human identification have primarily focused on recording the velocities of pressing and releasing different keys, these approaches lack integration with vision-based hand motion analysis.

To overcome the aforementioned challenges, this thesis introduces four deep learning-based models. The first model is  Measure4DHand, designed for automatic extraction of dynamic hand measurements from partial hand point cloud sequences. By analyzing the variation in measurement values induced by skin deformation across different hand postures, this model facilitates the identification of optimal hand postures for accurate and consistent measurements. The second model is PatientHandNet, which focuses on reconstructing a high-fidelity 3D hand shape in a canonical open-palm pose using four depth images captured from different viewpoints by a single commodity depth sensor To facilitate the proposed model, a large-scale multi-view synthetic dataset with a wide variety of hand shapes and hand poses and corresponding ground truth hand shapes in a canonical open palm pose is proposed and a novel real-world dataset by capturing 18 subjects (13 males and 5 females) via a structure sensor Mark I employed in an iPad and hired a professional anthropometrist to obtain corresponding ground-truth hand biometric measurements. The third contribution proposed TailoredTemplateFit, which is, to the best of our knowledge, the first deep learning-based method in the literature is proposed to simultaneously address point cloud completion and surface reconstruction while preserving the raw data of the input. This model is trained and validated on two large-scale datasets: a large-scale 50K head dataset and 300K hand dataset with a wide variety of shapes and poses and corresponding ground truth shapes. Lastly, we present KD-Net, which explores a novel visual modality of keystroke dynamics for human identification from RGB-D image sequences. To support this research, a novel dataset dubbed KD-MultiModal is created, comprising 243.2 K frames of RGB images and depth images.

Our proposed methods consistently outperform the reference methods from the literature, as demonstrated through comprehensive experimentation. The research works have been published in various reputable journals and conferences, highlighting their impact in both academic and industrial contexts.

On October 6th 2025 at 16:00, Ran Zhao will defend their PhD entitled “DEEP LEARNING-BASED HUMAN POSTURE NORMALIZATION AND AUTOMATIC ANTHROPOMETRIC MEASUREMENT”.

Everybody is invited to attend the presentation in room D.2.01 or online via this link.

Abstract ■

Accurate and user-friendly anthropometric measurement remains a major challenge in computer vision, as existing approaches typically require controlled scanning conditions, standard postures, or unclothed bodies. These constraints limit their usability in practical scenarios.

This thesis proposes a sequence of deep learning-based solutions to overcome these limitations. We first introduce OrienNormNet, an iterative network for robust orientation normalization, ensuring that scans are consistently aligned without manual preprocessing. Building on this, PoseNormNet is presented as the first posture normalization framework that transforms arbitrarily posed scans into a canonical T-pose while preserving identity details, removing the need for skeleton rigging. Next, W2H-Net demonstrates the feasibility of directly estimating the waist-to-hip ratio from partial dressed scans, showing that reliable indicators can be derived even from incomplete data. Finally, MeasureXpert provides a breakthrough toward real-world usability: it enables automatic extraction of anthropometric measurements from only two unregistered, partial, and clothed scans acquired in arbitrary poses.

To support these developments, the BWM dataset was synthesized for training, validation, and evaluation. Comprehensive experiments on both synthetic and real-world data confirm the effectiveness and robustness of the proposed methods. Collectively, the contributions progressively address key challenges related to cost, posture, and clothing, moving the field closer to practical, flexible, and accessible body measurement solutions.

The algorithms presented in this thesis have been disseminated through prestigious journals and conferences, demonstrating a modest yet meaningful impact on both academic research and industrial applications.

Non-EER staff needs a single permit, this request can take up to several months. The VUB requests this but the employee needs to deliver a large list of docs.

Karin needs to know if you want to hire someone new, or if the status of your researcher changes from PhD to post doc or researcher long in advance (3 M) to make sure all the steps are taken.

Karin cannot launch the procedure without a position and cannot create a position without budget (PKC) for at least 3 M

M&O – Wie vraagt de Single permit aan?

M&O – Wat gebeurt er als mijn statuut wijzigt? (Single Permit)




FAQ for the Master Biomedical Engineering

“Signal Processing in the AI era” was the tagline of this year’s IEEE International Conference on Acoustics, Speech and Signal Processing, taking place in Rhodes, Greece.

In this context, Brent de Weerdt, Xiangyu Yang, Boris Joukovsky, Alex Stergiou and Nikos Deligiannis presented ETRO’s research during poster sessions and oral presentations, with novel ways to process and understand graph, video, and audio data. Nikos Deligiannis chaired a session on Graph Deep Learning, attended the IEEE T-IP Editorial Board Meeting, and had the opportunity to meet with collaborators from the VUB-Duke-Ugent-UCL joint lab.

Featured articles:

General

Management

Public

Location-based Lists

Occupation-based Lists

On Januari 21st 2025 at 17:00, Fahimeh Akbarian will defend their PhD entitled “INVESTIGATING EXCITATION/INHIBITION BALANCE IN MS THROUGH APERIODIC NEUROPHYSIOLOGICAL ACTIVITY”.

Everybody is invited to attend the presentation in room I.0.01 or online via this link.

Abstract ■

Multiple sclerosis (MS) is a chronic central nervous system disease characterised by neuroinflammation, demyelination, and neurodegeneration, leading to physical and cognitive impairments. Cognitive deficits frequently affect working memory, information processing speed, and attention. Although their mechanisms are not fully understood, evidence suggests that synaptic loss, particularly of inhibitory synapses, disrupts cortical excitation–inhibition (E/I) balance and contributes to cognitive dysfunction.

In this PhD project, we used magnetoencephalography (MEG) to investigate changes in the aperiodic 1/f spectral slope, a proposed marker of cortical E/I balance. A steeper slope indicates increased inhibition or reduced excitation. MEG data from healthy controls (HCs) and people with MS (pwMS) were analysed during resting-state, visuo-verbal n-back, and auditory oddball tasks. Data were source reconstructed, parcellated into 42 brain regions, and decomposed into periodic and aperiodic components using the specparam algorithm. Neuropsychological assessments measured information processing speed, verbal fluency, and visuospatial memory.

During resting-state, pwMS taking benzodiazepines showed steeper slopes in occipital, temporal, and prefrontal regions compared with pwMS who did not take benzodiazepines, independent of beta power, supporting the slope as an oscillation-independent measure. Among pwMS who did not take benzodiazepines, those with cognitive impairment displayed steeper slopes than cognitively preserved pwMS and HCs, suggesting compensatory overinhibition mechanism.

In the n-back task, a consistent post-stimulus steepening (increased inhibition) was observed across participants. However, pwMS showed flatter slopes following distractors, consistent with impaired inhibitory control. Greater task-induced steepening predicted better visuospatial memory in pwMS, whereas the opposite relationship was observed in HCs.

In the auditory oddball task, slope steepening persisted even after correcting for event-related fields. Salient stimuli induced stronger steepening, and trials needed response showed enhanced sensorimotor steepening. Slope modulation was correlated across oddball and n-back tasks, suggesting a trait-like index of cognitive control.

Overall, the 1/f slope captured both tonic and phasic inhibitory dynamics, differentiated medication effects, correlated with cognitive performance, and generalised across paradigms. These findings support its potential as a non-invasive biomarker for cognitive dysfunction in MS, warranting further longitudinal and multimodal validation.

De Vrije Universiteit Brussel wil met een supercomputer rampen zoals de waterbom in Wallonië van 4 jaar geleden, beter kunnen voorspellen. De nieuwe supercomputer kan met AI de informatie die radars en satellieten leveren sneller verwerken en zo sneller updates geven over de weersomstandigheden. 

https://www.vrt.be/vrtnws/nl/2025/11/30/supercomputer-rampen-waterbom

Hiva Houshyar participated in the final pitch event of the FARI AI Accelerator and presented her business idea and her pitch was selected as “Best Pitch” by the jury! 🎉

Her project, BB-GO, is building a navigation platform tailored to wheelchair users, focusing on personalised routing and urban accessibility. The vision is a world where wheelchair users can explore their city with the same confidence as anyone else.

Want to help shape BB-GO or link us to public authorities? Get in touch via Johan.Stiens@vub.be or Seyedeh.Hiva.Houshyar.Yazdian@vub.be

ETRO was highly visible and omni-present at the HealthTech Brussels event hosted by FARI, showcasing cutting-edge AI expertise in health. We hope the networking opportunities helped create valuable new connections with the entrepreneurs and clinicians who attended.

Loris Giordano got the best student paper award at the AMAI workshop of MICCAI 2025 for the paper “A modular deep-learning pipeline for automated aorta characterization on CT”, co-authored by Loris Giordano, Jakub Ceranka, Selene De Sutter, Kaoru Tanaka, Gert Van Gompel, Tom Lenaerts, and Jef Vandemeulebroucke.

Anass Hamdi got awarded an PhD fellowship strategic basic research for his research “AI-driven radiogenomic analysis for spatial glioblastoma subtyping” under supervision of Catharina Olsen, Jef Vandemeulebroucke, Johnny Duerinck and Wim Vranken.

Sarah Al Omari got awarded a PhD Fellowship fundamental research for her research “Exploring Neuromuscular Fatigue in Stroke Survivors: Central-Peripheral Interplay and the Potential of Transcranial Alternating Current Stimulation (tACS)” under supervision of Eva Swinnen, David BeckwĂ©e, Mahyar Firouzi and Bart Jansen.

Manuel Montoya received the best oral presentation award at the International Conference on Applied Physics & Imaging (ICAPI) 2025 in Tartu, Estonia, for the work “Efficient simulations of partially coherent light using the Generalized Van Cittert – Zernike Schell Propagator”, co-authored by Manuel Montoya, Maria J. Lopera Acosta, Yunfeng Nie, and David Blinder.

On October 8th 2025 at 16:00, Xinxin Dai will defend their PhD entitled “LEARNING BASED RECONSTRUCTION AND MEASUREMENT OF 3D HANDS USING A SINGLE DEPTH CAMERA”.

Everybody is invited to attend the presentation in room D.2.01 or online via this link.

Abstract ■

Accurate 3D reconstruction and measurement extraction of the human hand are critical for a wide range of hand-centric applications, such as the design of immobilization devices, prosthetic limb fabrication, and osteoarthritis evaluation. However, the recovery of high-fidelity hand geometry remains challenging due to the inherently incomplete and occluded nature of point clouds acquired from commodity depth sensors, which are limited by viewpoint constraints and self-occlusion. Furthermore, traditional manual measurement methods, which require static hand postures and the expertise of trained anthropometrists, are inadequate for capturing measurements under realistic, task-specific hand motions, limiting their applicability in dynamic or non-standard scenarios.

To address these limitations, this thesis introduces deep learning-based methodologies aimed at addressing key challenges in the reconstruction and measurement extraction of 3D hand shapes. Specifically, the main research challenges include: (i) What is the optimal hand posture for precise and reliable measurement? (ii) How to fast and precisely reconstruct a complete hand shape from multi-view partial point clouds under different postures? and (iii) How can we simultaneously complete partial point clouds and reconstruct their surfaces while preserving the raw data? (iv) How to achieve human identification by the shape and posture of hands? The first challenge derives from the complexity of the human hand, which consists of 34 muscles and 27 bones. This intricate structure enables a wide range of postural variations, often resulting in significant geometric deformations that introduce considerable biases in measurement accuracy. Second, depth cameras inherently capture only partial point clouds due to limited viewpoints and self-occlusions, resulting in incomplete representations that restrict the accurate reconstruction of full hand geometry. Third, the lack of high-resolution surface details in a single partial point cloud makes it challenging to simultaneously achieve both point cloud completion and high-fidelity surface reconstruction. Lastly, while previous studies on human identification have primarily focused on recording the velocities of pressing and releasing different keys, these approaches lack integration with vision-based hand motion analysis.

To overcome the aforementioned challenges, this thesis introduces four deep learning-based models. The first model is  Measure4DHand, designed for automatic extraction of dynamic hand measurements from partial hand point cloud sequences. By analyzing the variation in measurement values induced by skin deformation across different hand postures, this model facilitates the identification of optimal hand postures for accurate and consistent measurements. The second model is PatientHandNet, which focuses on reconstructing a high-fidelity 3D hand shape in a canonical open-palm pose using four depth images captured from different viewpoints by a single commodity depth sensor To facilitate the proposed model, a large-scale multi-view synthetic dataset with a wide variety of hand shapes and hand poses and corresponding ground truth hand shapes in a canonical open palm pose is proposed and a novel real-world dataset by capturing 18 subjects (13 males and 5 females) via a structure sensor Mark I employed in an iPad and hired a professional anthropometrist to obtain corresponding ground-truth hand biometric measurements. The third contribution proposed TailoredTemplateFit, which is, to the best of our knowledge, the first deep learning-based method in the literature is proposed to simultaneously address point cloud completion and surface reconstruction while preserving the raw data of the input. This model is trained and validated on two large-scale datasets: a large-scale 50K head dataset and 300K hand dataset with a wide variety of shapes and poses and corresponding ground truth shapes. Lastly, we present KD-Net, which explores a novel visual modality of keystroke dynamics for human identification from RGB-D image sequences. To support this research, a novel dataset dubbed KD-MultiModal is created, comprising 243.2 K frames of RGB images and depth images.

Our proposed methods consistently outperform the reference methods from the literature, as demonstrated through comprehensive experimentation. The research works have been published in various reputable journals and conferences, highlighting their impact in both academic and industrial contexts.

On October 6th 2025 at 16:00, Ran Zhao will defend their PhD entitled “DEEP LEARNING-BASED HUMAN POSTURE NORMALIZATION AND AUTOMATIC ANTHROPOMETRIC MEASUREMENT”.

Everybody is invited to attend the presentation in room D.2.01 or online via this link.

Abstract ■

Accurate and user-friendly anthropometric measurement remains a major challenge in computer vision, as existing approaches typically require controlled scanning conditions, standard postures, or unclothed bodies. These constraints limit their usability in practical scenarios.

This thesis proposes a sequence of deep learning-based solutions to overcome these limitations. We first introduce OrienNormNet, an iterative network for robust orientation normalization, ensuring that scans are consistently aligned without manual preprocessing. Building on this, PoseNormNet is presented as the first posture normalization framework that transforms arbitrarily posed scans into a canonical T-pose while preserving identity details, removing the need for skeleton rigging. Next, W2H-Net demonstrates the feasibility of directly estimating the waist-to-hip ratio from partial dressed scans, showing that reliable indicators can be derived even from incomplete data. Finally, MeasureXpert provides a breakthrough toward real-world usability: it enables automatic extraction of anthropometric measurements from only two unregistered, partial, and clothed scans acquired in arbitrary poses.

To support these developments, the BWM dataset was synthesized for training, validation, and evaluation. Comprehensive experiments on both synthetic and real-world data confirm the effectiveness and robustness of the proposed methods. Collectively, the contributions progressively address key challenges related to cost, posture, and clothing, moving the field closer to practical, flexible, and accessible body measurement solutions.

The algorithms presented in this thesis have been disseminated through prestigious journals and conferences, demonstrating a modest yet meaningful impact on both academic research and industrial applications.

Non-EER staff needs a single permit, this request can take up to several months. The VUB requests this but the employee needs to deliver a large list of docs.

Karin needs to know if you want to hire someone new, or if the status of your researcher changes from PhD to post doc or researcher long in advance (3 M) to make sure all the steps are taken.

Karin cannot launch the procedure without a position and cannot create a position without budget (PKC) for at least 3 M

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M&O – Wat gebeurt er als mijn statuut wijzigt? (Single Permit)