An analysis of the accuracy of the P300 BCI

Nitzan S. Artzi, Oren Shriki

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


The P300 Brain-Computer Interface (BCI) is a well-established communication channel for severely disabled people. The P300 event-related potential is mostly characterized by its amplitude or its area, which correlate with the spelling accuracy of the P300 speller. Here, we introduce a novel approach for estimating the efficiency of this BCI by considering the P300 signal-to-noise ratio (SNR), a parameter that estimates the spatial and temporal noise levels and has a significantly stronger correlation with spelling accuracy. Furthermore, we suggest a Gaussian noise model, which utilizes the P300 event-related potential SNR to predict spelling accuracy under various conditions for LDA-based classification. We demonstrate the utility of this analysis using real data and discuss its potential applications, such as speeding up the process of electrode selection.

Original languageEnglish
Pages (from-to)112-120
Number of pages9
JournalBrain-Computer Interfaces
Issue number4
StatePublished - 2 Oct 2018


  • LDA
  • P300 speller
  • electroencephalography
  • signal-to-noise ratio
  • symbol selection accuracy


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