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arXiv · 1901.03299

An Analysis of the Accuracy of the P300 BCI

Nitzan S. Artzi·Oren Shriki
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Abstract

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.

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BibTeXRIS

Nitzan S. Artzi, Oren Shriki. 2018-12-11. An Analysis of the Accuracy of the P300 BCI. https://doi.org/10.1080/2326263x.2018.1552357

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