Projects
Decoding responses to periodic visual motion stimulation using multiway regression for self-paced brain computer interfacing KU Leuven
Brain-Computer Interfaces (BCIs) decode neural activity with the aim to establish a communication channel. As traditional neural pathways are bypassed, they have been hailed as a solution for patients with impaired muscular control. BCIs based on brain implants yield superior decoding performance but they require surgery and the observed loss in signal quality is a recurring concern. Non-invasive BCIs, primarily EEG-based ones, curb these ...
Insights in pathophysiological changes connected to BcI-2-dependence at the endoplasmic recticulum in diffuse large B-cell lymphomas. KU Leuven
We aim to get a better insight in the pathology of diffuse large B-cell lymphoma (DLBCL) on the cellular and molecular level. To do this, we studied two different aspects of the anti-apoptotic role of the protein B-cell lymphoma-2 (Bcl-2), a protein involved in cell fate decisions.
Typically, Bcl-2 prevents cell death by sequestering pro-apoptotic proteins via a hydrophobic groove on its surface. This working mechanism of Bcl-2 can be ...
Brain-computer interfacing based on electrocorticography and visual stimulation. KU Leuven
In Brain-Computer Interfacing (BCI), brain activity is translated into actions that communicate the user’s intent and that bypass the need for muscular activity. Recent developments capitalize on the relation between the kinematics of human movements and localized activity in motor- and somatosensory regions of the brain. The ability to control a robotic limb or regain control over a paralyzed limb with a motor-BCI has promoted the latter as ...
Signal processing algorithms for attention decoding of brain responses to natural stimuli in brain-computer interfaces KU Leuven
Brain-computer interfaces (BCI) enable the human brain to interact with machines, opening doors to various high-impact applications. However, most experimental BCI paradigms require the user to concentrate on synthetic and repeated stimuli, inducing fatigue and interfering with natural behavior. This unnatural interaction blocks the widespread usage of BCIs in daily-life situations beyond a few niche clinical applications.
In this ...
Signal processing algorithms for attention decoding of brain responses to natural stimuli in brain-computer interfaces. KU Leuven
Development of a modular product-architecture for wearable EEG headsets (Ctrl-Mind). University of Antwerp
Brain Computer Interfacing based on Time Domain EEG Response Detection combined with Beamforming. KU Leuven
Patients suffering from severe motor- and communication disorders are no longer able to interact with their surroundings, and even communication with their families becomes nearly impossible. Due to this disability, they highly depend on caregivers with whom the communication is often troublesome or laborious. Therefore, modern technology that would be able to re-establish a reliable communication channel could considerably improve their ...
Real-time multiway decoding of performed, observed and imagined movement electrocorticography supported by avatar-based user training KU Leuven
Brain-Computer Interfaces (BCIs) decode brain activity with the aim to establish a communication channel that does not rely on muscular control. BCIs usually rely on EEG signals acquired from the subject's scalp or on electrophysiological signals from brain implants. The latter yield a superior decoding performance, but as the implant damages the cortical tissue, long-term signal stability is a concern. EEG does not require surgery but the ...
Real-time motor brain-computer interface based on electrocorticography in humans. KU Leuven
Disabled patients can be assisted in their communication needs with a BCI: a device that decodes brain activity directly, bypassing the need for muscular activity. The journal Science selected in 2012 BCI as one of ten runner-ups for “Breakthrough of the year”. However, the main thrust of current BCI research is with the decoding of EEG recordings acquired from the subject’s scalp or invasive ones obtained from deep brain implants. Both have ...