Skip to main content
Open Access Publications from the University of California


UCLA Electronic Theses and Dissertations bannerUCLA

Incorporating flash adjacency into the classifier for a language model-based P300 Speller


The P300 speller is a common brain–computer interface (BCI) application designed to allow patients with neuromuscular disorders such as amyotrophic lateral sclerosis (ALS) produce text output through the detection of P300 signals in their electroencephalogram (EEG) signals. The standard P300 speller relies on the detection of signals evoked by visual stimuli, usually consisting of rows and columns highlighted in a grid of characters. Since the visual field is substantially larger than these stimuli, adjacent flashes to the attend characters may cause false positive signals and lead to erroneous output. While previous work has tried to address this issue by limiting the number of adjacent stimuli, no attempts have been made to account for these adjacency false positives in the classifier or utilize information from adjacent flashes to optimize the system. In this study we added a bias to the target character detection based on adjacent flashes and created a new probability model to improve the accuracy and speed of classification. We tested our adjacency classifier in both the standard P300 paradigm (we call it SWLDA method in the following content) and in conjunction with natural language processing. The new algorithm was evaluated offline on a dataset of 69 healthy subjects, which showed increases in speed and accuracy when compared to standard classification methods. On population level, LDA classifier shows a significant improvement, but the improvement for NLP is not significant. However, for some subject in both algorithms the enhancement of information transfer rate is substantial. As for LDA classifier, 57.4% subjects’ peak ITR increases of more than 5 bits per minute, 30.9% subjects’ peak ITR increases more than 10 bits per minutes, and 8.8% subjects’ peak ITR increases more than 15 bits per minutes. As for NLP, of the subjects, 21.7% had peak ITR increases of more than 5 bits per minute, 10.1% subjects’ peak ITR increased more than 10 bits per minutes, and 5.8% subjects’ peak ITR increased more than 15 bits per minutes. Therefore, incorporating adjacent fleshes can potentially provide a better communication system for some users.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View