A5003443286- EEG and Brain-Computer Interfaces
- Neuroscience and Neural Engineering
- Gaze Tracking and Assistive Technology
- Muscle activation and electromyography studies
- Cognitive Functions and Memory
- Biomedical Text Mining and Ontologies
- Genomics and Rare Diseases
- Advanced Memory and Neural Computing
- Chronic Disease Management Strategies
- Context-Aware Activity Recognition Systems
- Balance, Gait, and Falls Prevention
- Stroke Rehabilitation and Recovery
- Glycosylation and Glycoproteins Research
- Prosthetics and Rehabilitation Robotics
- Neural dynamics and brain function
- Frailty in Older Adults
Equipes Traitement de l'Information et Systèmes
2022-2024
Belgian Road Research Centre
2023-2024
Centre National de la Recherche Scientifique
2023-2024
Vrije Universiteit Brussel
2022-2024
École Nationale Supérieure de l'Électronique et de ses Applications
2023-2024
CY Cergy Paris Université
2023-2024
Physiotherapy New Zealand
2022
Robotics Research (United States)
2022
Improving the understanding of oligogenic nature diseases requires access to high-quality, well-curated Findable, Accessible, Interoperable, Reusable (FAIR) data. Although first steps were taken with development Digenic Diseases Database, leading novel computational advancements assist field, these also linked a number limitations, for instance, ad hoc curation protocol and inclusion only digenic cases. The OLIgogenic DAtabase (OLIDA) presents novel, transparent rigorous protocol,...
This study evaluates an innovative control approach to assistive robotics by integrating brain–computer interface (BCI) technology and eye tracking into a shared system for mobile augmented reality user interface. Aimed at enhancing the autonomy of individuals with physical disabilities, particularly those impaired motor function due conditions such as stroke, utilizes BCI interpret intentions from electroencephalography signals identify object focus, thus refining commands. integration...
: Brain-computer interface (BCI) control systems monitor neural activity to detect the user's intentions, enabling device through mental imagery. Despite their potential, decoding in real-world conditions poses significant challenges, making BCIs currently impractical compared traditional interaction methods. This study introduces a novel motor imagery (MI) BCI strategy for operating physically assistive robotic arm, addressing difficulties of MI from electroencephalogram (EEG) signals,...
Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances both software and hardware allow for the decoding of electroencephalogram (EEG) signals to improve control active prostheses with brain-computer interfaces (BCI). Most BCI research is focused on upper body. Although lower extremities has increased years, there are still gaps our knowledge neural patterns associated movement. Therefore, main objective this study show feasibility...
Falls are a major problem associated with ageing. Yet, fall-risk classification models identifying older adults at risk lacking. Current screening tools show limited predictive validity to differentiate between low- and high-risk of falling.This study aims factors higher falling by means quality-of-life questionnaire incorporating biological, behavioural, environmental socio-economic factors. These insights can aid the development algorithm community-dwelling falling.The was developed...
Brain–computer interfaces (BCIs) have the potential to enable individuals interact with devices by detecting their intention from brain activity. A common approach BCI is decode movement motor imagery (MI), mental representation of an overt action. However, research-grade electroencephalogram (EEG) acquisition a high number sensors are typically necessary achieve spatial resolution required for reliable analysis. This entails monetary and computational costs that make these approaches...
Brain-computer interfaces can be used to operate devices by detecting a person's intention from their brain activity. Decoding motor imagery (MI) electroencephalogram (EEG) signals is commonly approach for this purpose. To reliably identify MI EEG signals, sufficient number of sensors usually required. However, large increases the computational cost discriminating classes. Furthermore, consumer-grade that measure often employ reduced compared medical- or research-grade devices. In...
Brain-computer interfaces (BCI) enable users to control devices through their brain activity. Motor imagery (MI), the neural activity resulting from an individual imagining performing a movement, is common paradigm. This study introduces user-centric evaluation protocol for assessing performance and user experience of MI-based BCI system utilizing augmented reality. Augmented reality employed enhance interaction by displaying environment-aware actions, guiding on necessary imagined movements...