Publicaties
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Phase-Spatial Beamforming Renders a Visual Brain Computer Interface Capable of Exploiting EEG Electrode Phase Shifts in Motion-Onset Target Responses KU Leuven
OBJECTIVE: in this work, we aim to develop a more efficient visual motion-onset based Brain-computer interface (BCI). Brain-computer interfaces provide communication facilities that do not rely on the brain's usual pathways. Visual BCIs are based on changes in EEG activity in response to attended flashing or flickering targets. A less taxing way to encode such targets is with briefly moving stimuli, the onset of which elicits a lateralized EEG ...
An uncued brain-computer interface using reservoir computing Universiteit Gent
Brain-Computer Interfaces are an important and promising avenue for possible next-generation assistive devices. In this article, we show how Reservoir Comput- ing – a computationally efficient way of training recurrent neural networks – com- bined with a novel feature selection algorithm based on Common Spatial Patterns can be used to drastically improve performance in an uncued motor imagery based Brain-Computer Interface (BCI). The objective ...
Brain-computer interfaces with machine learning : a symbiotic approach Universiteit Gent
Investigation of surface properties of boron doped diamond for developing neuron -machine interface Universiteit Hasselt
Summary The main goal of this thesis is to study how diamond films advance the construction of the hybrid biological-solid state interfaces and to construct Micro-Electrode Arrays (MEAs) for neural recordings. First, a literature study was carried out to review diamond applications in biology for in vitro and in vivo and for Microelectrode arrays (MEAs). In order to develop diamond MEAs, nano crystalline diamond (NCD) and boron-doped nano ...
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 ...
Comparison of classification methods for P300 Brain-Computer Interface on disabled subjects KU Leuven
In this paper, we report on tests with the P300 Brain-Computer Interface (BCI) typing paradigm on neurological patients suffering from motor and speech disabilities. We investigate the accuracy of different classifiers: Fisher's Linear Discriminant Analysis (LDA), Bayesian Linear Discriminant Analysis (BLDA), Stepwise Linear Discriminant Analysis (SLDA), a method based on Feature Extraction (FE), linear Support Vector Machine (SVM), Gaussian ...
Error-related Potential recorded by EEG in the context of a P300 Mind Speller Brain-Computer Interface KU Leuven
The Mind Speller is a Brain-Computer Interface (BCI) which enables subjects to spell text on a computer screen by detecting P300 Event-Related Potentials in their electro-encephalograms (EEG). This BCI application is of particular interest for disabled patients who have lost all means of verbal and motor communication. Error-related Potentials (ErrP) in the EEG are generated by the subject's perception of an error. We report on the possibility ...
An Application of Feature Selection to On-Line P300 Detection in Brain-Computer Interface KU Leuven
We propose a new EEG-based wireless brain computer interface (BCI) with which subjects can ldquomind-typerdquo text on a computer screen. The application is based on detecting P300 event-related potentials in EEG signals recorded on the scalp of the subject. The BCI uses a linear classifier which takes as input a set of simple amplitude-based features that are optimally selected using the group method of data handling (GMDH) feature ...