Proceedings ArticleDOI
Optimal spatial filtering for the steady state visual evoked potential: BCI application
Gary Garcia-Molina,Danhua Zhu +1 more
- pp 156-160
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TLDR
This paper proposes a taxonomy to categorize these methods and extensively evaluate them using 22 stimulation frequencies and suggests improvements to existing methods to increase the SSVEP detection performance.Abstract:
Focusing of attention on a repetitive visual stimulation (RVS) at a constant frequency, elicits the so called steady-state visual evoked potential (SSVEP). This effect can be advantageously utilized in brain-computer interfaces (BCIs). SSVEP based BCIs can offer higher bitrates and require shorter training time as compared to other BCI modalities. Detection of the SSVEP from the EEG can be facilitated through spatial filtering (linear combination of the signals recorded at several electrodes). Literature offers several options to perform this. In this paper we propose a taxonomy to categorize these methods and we extensively evaluate them using 22 stimulation frequencies. We suggest improvements to existing methods to increase the SSVEP detection performance. We also consider practical aspects in the discussion of results.read more
Citations
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Journal ArticleDOI
The steady-state visual evoked potential in vision research: A review.
Anthony M. Norcia,L. Gregory Appelbaum,Justin M. Ales,Benoit R. Cottereau,Benoit R. Cottereau,Bruno Rossion +5 more
TL;DR: The purpose of this article is to describe the fundamental stimulation paradigms for steady-state visual evoked potentials and to illustrate these principles through research findings across a range of applications in vision science.
Journal ArticleDOI
To train or not to train? A survey on training of feature extraction methods for SSVEP-based BCIs.
TL;DR: This paper reviews the various state-of-the-art SSVEP feature extraction methods that have been developed and are most widely used in the literature and highlights the strengths and weaknesses of the three categories of SSVEp training methods.
Journal ArticleDOI
Review: Recent Development of Signal Processing Algorithms for SSVEP-based Brain Computer Interfaces
TL;DR: Algorithms that can be applied to nonlinear and non-stationary signal processing are increasingly employed rather than traditional Fourier-based transforms because they are more suitable for the characteristics of SSVEPs.
Journal ArticleDOI
Comparative analysis of strategies for feature extraction and classification in SSVEP BCIs
Sarah N. Carvalho,Sarah N. Carvalho,Thiago Bulhões da Silva Costa,Luisa Fernanda Suarez Uribe,Diogo C. Soriano,Glauco Ferreira Gazel Yared,Luis Coradine,Romis Attux +7 more
TL;DR: A comparative analysis of different signal processing techniques for each BCI system stage concerning steady state visually evoked potentials (SSVEP), which includes feature extraction performed by different spectral methods, leads to a representative and helpful comparative overview of robustness and efficiency of classical strategies.
Journal ArticleDOI
Maximally reliable spatial filtering of steady state visual evoked potentials
TL;DR: In this article, a spatial filtering method is proposed to project the data into a low-dimensional space in which the trial-to-trial spectral covariance is maximized, and the resulting technique recovers physiologically plausible components (i.e., the recovered topographies match the lead fields of the underlying sources).
References
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Human Brain Electrophysiology: Evoked Potentials and Evoked Magnetic Fields in Science and Medicine
TL;DR: In this article, the authors propose an approach to explore the potential of PE in the context of neurophysiologie and psychophysics, and propose a set of criteria for evaluating the applicability of PE.
Journal ArticleDOI
A survey of stimulation methods used in SSVEP-based BCIs
TL;DR: This paper reviews the literature on SSVEP-based BCIs and comprehensively reports on the different RVS choices in terms of rendering devices, properties, and their potential influence on BCI performance, user safety and comfort.