Online SSVEP-based BCI using Riemannian geometry
نویسندگان
چکیده
منابع مشابه
Online SSVEP-based BCI using Riemannian geometry
Challenges for the next generation of Brain Computer Interfaces (BCI) are to mitigate the common sources of variability (electronic, electrical, biological) and to develop online and adaptive systems following the evolution of the subject's brain waves. Studying electroencephalographic (EEG) signals from their associated covariance matrices allows the construction of a representation which is i...
متن کاملUsing Riemannian geometry for SSVEP-based Brain Computer Interface
Riemannian geometry has been applied to Brain Computer Interface (BCI) for brain signals classification yielding promising results. Studying electroencephalographic (EEG) signals from their associated covariance matrices allows a mitigation of common sources of variability (electronic, electrical, biological) by constructing a representation which is invariant to these perturbations. While work...
متن کاملMulti-target SSVEP-based BCI using Multichannel SSVEP Detection
Spatial filtering method and fast Fourier transform (FFT) based spectrum estimation method are applied to reveal the presence of steady state visual evoked potential (SSVEP) in multiple-electrodes electroencephalogram (EEG) signals used in Brain-Computer Interface (BCI) system. The SSVEP responses are elicited by visual stimuli in the form of flickering light emitting diode (LED) array and comp...
متن کاملRiemannian Geometry Applied to BCI Classification
In brain computer interface based on motor imagery, covariances matrices are widely used through spatial filters computation and other signal processing methods. Covariances matrices lie in the space of Semi-definite Positives (SPD) matrices and therefore, fall within the Riemannian geometry domain. Using a differential geometry frameworks, we propose different algorithms in order to classify c...
متن کاملEliciting dual-frequency SSVEP using a hybrid SSVEP-P300 BCI
BACKGROUND Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) generate weak SSVEP with a monitor and cannot use harmonic frequencies, whereas P300-based BCIs need multiple stimulation sequences. These issues can decrease the information transfer rate (ITR). NEW METHOD In this paper, we introduce a novel hybrid SSVEP-P300 speller that generates dual-frequency S...
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ژورنال
عنوان ژورنال: Neurocomputing
سال: 2016
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2016.01.007