BCI Demographics: How Many (and What Kinds of) People Can Use an SSVEP BCI?

By Brendan Z. Allison, T. Luth, Diana Valbuena, Amir Teymourian, Ivan Volosyak, A. Graser. IEEE transactions on neural systems and rehabilitation engineering, 2010

372 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/d761df4f3afd55e1ff8626a65d5f7eb0c49f373b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain–computer interface (2015, result): “Second, previous studies reported ‘ BCI illiteracy ’ with the SSVEP-based BCI (Allison et al 2010).”
Could Anyone Use a BCI? (2010, background): “Another study found that older subjects performed worse with SSVEP BCIs, but otherwise found no correlation between performance and many other factors ( Allison et al. 2010a ).”
Brain-Computer Interfaces (2015, positive): “If the subject focuses on the checkerboard at 6 Hz, then their brain response, measured with the EEG, will increase in that specific frequency and the BCI will be able to decode that attention is being paid to it (Allison et al. 2010).”
Single-trial analysis and classification of ERP components - A tutorial (2011, positive): “However, regardless of the particular techniques employed for feature extraction or classification, there is substantial variability in the classification accuracy both between subjects (Guger et al., 2003, 2009; Blankertz et al., 2007a; Krauledat et al., 2008; Dickhaus et al., 2009; Allison et al.,”
Brain-Computer Interfaces: A Gentle Introduction (2009, methodology): “For example, the task described above, in which users move a cursor to a target and then select it, has been validated with both P300 and SSVEP BCIs [15, 17].”
EEG dataset and OpenBMI toolbox for three BCI paradigms: an investigation into BCI illiteracy (2019, positive): “Previous studies have primarily reported the problem of BCI illiteracy with respect to the MI paradigm [21,34,35] or, when examined across multiple paradigms, only with small subject groups (less than five) [36].”
The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology (2010, background): “For results on large-scale studies and performance-related demographics, see Allison et al. (2009) for an SSVEP-and Guger et al. (2009) for a P300-based system.”
Toward a hybrid brain–computer interface based on imagined movement and visual attention (2010, background): “Demographic information might help identify the best BCI approach and parameters for each user [57, 58].”