A novel P300-based brain-computer interface stimulus presentation paradigm: moving beyond rows and columns

By George Townsend, B. LaPallo, Chadwick B. Boulay, Dean J. Krusienski, G. Frye, Christopher K. Hauser, N. E. Schwartz, Theresa M. Vaughan, Jonathan R. Wolpaw, Eric W. Sellers. Clinical Neurophysiology, 2010

587 citations recorded by the source corpus.

Objective An electroencephalographic brain-computer interface (BCI) can provide a non-muscular means of communication for people with amyotrophic lateral sclerosis (ALS) or other neuromuscular disorders. We present a novel P300-based BCI stimulus presentation – the checkerboard paradigm (CBP). CBP performance is compared to that of the standard row/column paradigm (RCP) introduced by Farwell and Donchin (1988). Methods Using an 8×9 matrix of alphanumeric characters and keyboard commands, 18 participants used the CBP and RCP in counter-balanced fashion. With approximately 9 – 12 minutes of calibration data, we used a stepwise linear discriminant analysis for online classification of subsequent data. Results Mean online accuracy was significantly higher for the CBP, 92%, than for the RCP, 77%. Correcting for extra selections due to errors, mean bit rate was also significantly higher for the CBP, 23 bits/min, than for the RCP, 17 bits/min. Moreover, the two paradigms produced significantly different waveforms. Initial tests with three advanced ALS participants produced similar results. Furthermore, these individuals preferred the CBP to the RCP. Conclusions These results suggest that the CBP is markedly superior to the RCP in performance and user acceptability. Significance The CBP has the potential to provide a substantially more effective BCI than the RCP. This is especially important for people with severe neuromuscular disabilities.

Subfields: Biomedical Engineering, Cognitive Neuroscience, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/d58ee0745c09873c26109231207ddb4b8b3d112d

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Citations in the corpus

P300 brain computer interface: current challenges and emerging trends (2012, methodology): “Several human perceptual phenomena such as attentional blink, repetition blindness, and habituation are potential sources of error in P300 detection and have been addressed in the literature (Fazel-Rezai, 2007, 2009; Citi et al., 2008; Jin et al., 2010; Townsend et al., 2010).”
High performance communication by people with paralysis using an intracortical brain-computer interface (2017, background): “Similarly, information transfer rates are also a meaningful point of comparison, though they are less reflective of practical communication rates than bitrate (which takes into account the need to correct errors; detailed in Nuyujukian et al. (2015) ; Townsend et al. (2010) ).”
Brain-Computer Interfaces for Communication and Control (2011, unknown): “Omitting time between trials increased Townsend et al. 31 results from 3.66 to 5.92 characters/second.”
Flashing characters with famous faces improves ERP-based brain–computer interface performance (2011, background): “…distance (Salvaris and Sepulveda 2009), background color (Salvaris and Sepulveda 2009), chromatic differences in the ﬂashing pattern (Takano et al 2009) and the ﬂashing pattern itself (e.g. Townsend et al (2010), Frye et al 2011, Guger et al (2009), Jin et al (2010)) affect the accuracy level.”
Brain–Computer Interface Spellers: A Review (2018, methodology): “As presented in Figure 6, it was composed of a 9 × 8 matrix of characters and commands, and, to avoid the “adjacency-distraction problem” and the “double ﬂash” issues, the sets of nonadjacent elements were pseudo-randomly ﬂashed [102].”
Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review (2020, methodology): “The checkerboard paradigm (CBP) was presented in Townsend et al. (2010) for avoiding the adjacency-distraction problem and double ﬂash issues.”
A comprehensive review of EEG-based brain–computer interface paradigms (2019, unknown): “An innovative checkerboard paradigm (CBP) was introduced in [107].”
Brain Computer Interfaces, a Review (2012, methodology): “In other words, offline simulation and cross-validation can be valuable methods to develop and test new algorithms, but only online analysis can yield solid evidence of BCI system performance [137,212,213].”
Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review (2017, unknown): “For P300based BCI these lines of research have led to, inter varia, the introduction of language models for letter and word prediction [23-25], automatic pause detection [26], the use of faces for flashing symbols [24], the use of random groups or pseudo-random groups flashing instead of row-column ”