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Real-Time Navigation in Google Street View® Using a Motor Imagery-Based BCI KU Leuven
Navigation in virtual worlds is ubiquitous in games and other virtual reality (VR) applications and mainly relies on external controllers. As brain-computer interfaces (BCI)s rely on mental control, bypassing traditional neural pathways, they provide to paralyzed users an alternative way to navigate. However, the majority of BCI-based navigation studies adopt cue-based visual paradigms, and the evoked brain responses are encoded into navigation ...
An unsupervised plug and play BCI with consumer grade hardware Ghent University
In this work we use the classic P300 speller where the user is presented a grid of characters. Groups of characters are repeatedly highlighted while the user is asked to count silently when the attended symbol is flashed. This elicits an increase in the potential difference recorded in the EEG, ~300 ms after a target stimulus is presented. By discriminating between the presence or absence of this so-called P300 waveform, it is possible to ...
Zero training for BCI-reality for BCI systems based on event-related potentials Ghent University
This contribution reviews how usability in Brain- Computer Interfaces (BCI) can be enhanced. As an ex- ample, an unsupervised signal processing approach is pre- sented, which tackles usability by an algorithmic improve- ment from the field of machine learning. The approach completely omits the necessity of a calibration recording for BCIs based on event-related potential (ERP) paradigms. The positive effect is twofold - first, the experimental ...
An unsupervised plug and play BCI with consumer grade hardware Ghent University
In this work we use the classic P300 speller where the user is presented a grid of characters. Groups of characters are repeatedly highlighted while the user is asked to count silently when the attended symbol is flashed. This elicits an increase in the potential difference recorded in the EEG, ~300 ms after a target stimulus is presented. By discriminating between the presence or absence of this so-called P300 waveform, it is possible to ...
Detecting attention levels in ADHD children with a video game and the measurement of brain activity with a single-channel BCI headset Ghent University
Tensor-Based Classification of Auditory Mobile BCI without Subject-Specific Calibration Phase KU Leuven
OBJECTIVE: One of the major drawbacks in EEG brain-computer interfaces (BCI) is the need for subject-specific training of the classifier. By removing the need for a supervised calibration phase, new users could potentially explore a BCI faster. In this work we aim to remove this subject-specific calibration phase and allow direct classification. APPROACH: We explore canonical polyadic decompositions and block term decompositions of the EEG. ...
Reducing BCI calibration time with transfer learning: a shrinkage approach Ghent University
Introduction: A brain-computer interface system (BCI) allows subjects to make use of neural control signals to drive a computer application. Therefor a BCI is generally equipped with a decoder to differentiate between types of responses recorded in the brain. For example, an application giving feedback to the user can benefit from recognizing the presence or absence of a so-called error potential (Errp), elicited in the brain of the user when ...
Using Actual and Imagined Walking Related Desynchronization Features in a BCI KU Leuven
Recently, brain-computer interface (BCI) research has extended to investigate its possible use in motor rehabilitation. Most of these investigations have focused on the upper body. Only few studies consider gait because of the difficulty of recording EEG during gross movements. However, for stroke patients the rehabilitation of gait is of crucial importance. Therefore, this study investigates if a BCI can be based on walking related ...
EEG beamforming to extract better features of motor imagery in a two-class real-time BCI Ghent University
A general problem in the design of an EEG-BCI system is the poor quality and low robustness of the extracted features, affecting overall performance. However, BCI systems that are applicable in real-time and outside clinical settings require high performance. Therefore, we have to improve the current methods for feature extraction. In this work, we investigated EEG source reconstruction techniques to enhance the extracted features based on a ...