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A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics Vrije Universiteit Brussel
Prosthetic devices that replace a lost limb have become increasingly performant in recent years. Recent advances in both software and hardware allow for the decoding of electroencephalogram (EEG) signals to improve the control of active prostheses with brain-computer interfaces (BCI). Most BCI research is focused on the upper body. Although BCI research for the lower extremities has increased in recent years, there are still gaps in our ...
A Bayesian machine learning framework for true zero-training brain-computer interfaces Ghent University
Brain-Computer Interfaces (BCI) are developed to allow the user to take control of a computer (e.g. a spelling application) or a device (e.g. a robotic arm) by using just his brain signals. The concept of BCI was introduced in 1973 by Jacques Vidal. The early types of BCI relied on tedious user training to enable them to modulate their brain signals such that they can take control over the computer. Since then, training has shifted from the user ...
Brain-Computer Interfaces based on Electroencephalography and Visual Stimulation KU Leuven
This thesis discusses three independent studies on Brain-Computer Interfaces (BCIs) for communication based on electroencephalography (EEG) and visual stimulation. BCIs aim at establishing a direct communication channel between the brain and an external device and are therefore particularly interesting for patients whose motor output channels are severely impaired. The main objective of this doctoral project is to develop BCIs based on visual ...
Language Model Applications to Spelling with Brain-Computer Interfaces KU Leuven
Within the Ambient Assisted Living (AAL) community, Brain-Computer Interfaces (BCIs) have raised great hopes as they provide alternative communication means for persons with disabilities bypassing the need for speech and other motor activities. Although significant advancements have been realized in the last decade, applications of language models (e.g., word prediction, completion) have only recently started to appear in BCI systems. The main ...
A Comparison of Two Spelling Brain-Computer Interfaces Based on Visual P3 and SSVEP in Locked-In Syndrome KU Leuven
We study the applicability of a visual P3-based and a Steady State Visually Evoked Potentials (SSVEP)-based Brain-Computer Interfaces (BCIs) for mental text spelling on a cohort of patients with incomplete Locked-In Syndrome (LIS).
Bayesian Plan Recognition for Brain-Computer Interfaces KU Leuven
For people with very severe motor dysfunctions, Brain-Computer Interfaces (BCIs) may provide the solution to regain mobility and manipulation capabilities. Unfortunately, BCIs are characterized by a limited bandwidth and uncertainty on the BCI output. In the past, we have developed a Bayesian plan recognition framework that estimates from uncertain human-robot interface signals the task a robot should execute. This paper extends our plan ...
A brain-actuated wheelchair: Asynchronous and non-invasive Brain-computer interfaces for continuous control of robots KU Leuven
Objective: To assess the feasibility and robustness of an asynchronous and non-invasive EEG-based Brain-Computer Interface (BCI) for continuous mental control of a wheelchair.
Augmenting Information from Brain-Computer Interfaces through Bayesian Plan Recognition KU Leuven
For severely disabled people, Brain-Computer Interfaces(BCIs) may provide the means to regain mobility and manipulation capabilities. However, information obtained from current BCIs is uncertain and of limited bandwidth and resolution. This paper presents a Bayesian framework that estimates from uncertain BCI signals a richer representation of the task a robotic mobility or manipulation device should execute, such that these devices can be ...