Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components

By Gernot R. Müller-Putz, Reinhold Scherer, Christian Brauneis, Gert Pfurtscheller. Journal of Neural Engineering, 2005

560 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/a6809b1755b996e234c33380b7bd1c55f21cee34

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Multiple Channel Detection of Steady-State Visual Evoked Potentials for Brain-Computer Interfaces (2007, methodology): “Like in previous studies [8], [10], there is a significant intersubject variation in SSVEP response strength and classification accuracy.”
An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method (2009, methodology): “When there were no harmonic frequency stimuli, higher harmonic information could be used for improved detection [7].”
EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy (2019, unknown): “Additionally, the harmonic frequencies were also determined as described in the literature [4].”
Towards an independent brain-computer interface using steady state visual evoked potentials. (2008, background): “Initial efforts have suggested that some of these avenues may be useful in SSVEP BCI systems (Wang et al., 2004; Kelly et al., 2005a,b; Lalor et al., 2005; Müller-Putz et al., 2005; Trejo et al., 2006).”
Brain–computer interface systems: progress and prospects (2007, background): “If users direct attention to one such stimulus, activity over occipital areas at corresponding frequencies can be used to infer user intent [65–70] .”
Visual and Auditory Brain–Computer Interfaces (2014, background): “The efficiency of combining multiple harmonic components has been well demonstrated in the SSVEP-based BCIs [11, 37, 95].”
A Brain–Computer Interface Controlled Auditory Event‐Related Potential (P300) Spelling System for Locked‐In Patients (2009, methodology): “In particular, the already existing BCI use steady state–evoked potentials 9 , 10 or the late ERP component P300.”
Control of an Electrical Prosthesis With an SSVEP-Based BCI (2008, methodology): “The power was calculated for the first three harmonics Hji (i = 1, 2, 3) for each stimulation frequency (j = 6, 7, 8, 13) [16].”
Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs (2006, background): “However, in those systems that reserve higher harmonics [16], the CCA approach is believed to benefit more from using higher harmonics other than the anti-noise ability.”
Brain-Computer Interfaces Based on Visual Evoked Potentials (2008, methodology): “Thebipolar electrode placement hasbeen applied in some systems to obtain a cleaner signal by canceling common background activities [6], [ 11 ].”
Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain–computer interface (2015, result): “The advantage of combining harmonic components in frequency detection has been demonstrated in previous studies (Müller-Putz et al 2005, Wang et al 2008).”
A practical VEP-based brain-computer interface. (2006, methodology): “approach was to use frequency detection of frequency-coded SSVEP to determine the gaze direction of the user [5]–[7].”
BCI Demographics: How Many (and What Kinds of) People Can Use an SSVEP BCI? (2010, background): “Intersubject variability often leads to the well-documented “BCI illiteracy” phenomenon; across different BCI approaches (SSVEP, P300, SMR1), about 10%–25% of users are unable to attain effective control [8], [12]–[18].”