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.
Original language | English |
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Pages (from-to) | 112-120 |
Number of pages | 9 |
Journal | Brain-Computer Interfaces |
Volume | 5 |
Issue number | 4 |
DOIs | |
State | Published - 2 Oct 2018 |
Keywords
- LDA
- P300 speller
- electroencephalography
- signal-to-noise ratio
- symbol selection accuracy
ASJC Scopus subject areas
- Biomedical Engineering
- Human-Computer Interaction
- Behavioral Neuroscience
- Electrical and Electronic Engineering