Incorporating advanced language models into the P300 speller using particle filtering.
Journal:
Journal of neural engineering
Published Date:
Jun 10, 2015
Abstract
OBJECTIVE: The P300 speller is a common brain-computer interface (BCI) application designed to communicate language by detecting event related potentials in a subject's electroencephalogram signal. Information about the structure of natural language can be valuable for BCI communication, but attempts to use this information have thus far been limited to rudimentary n-gram models. While more sophisticated language models are prevalent in natural language processing literature, current BCI analysis methods based on dynamic programming cannot handle their complexity.
Authors
Keywords
Algorithms
Brain Mapping
Brain-Computer Interfaces
Communication Devices for People with Disabilities
Computer Simulation
Electroencephalography
Event-Related Potentials, P300
Humans
Machine Learning
Models, Statistical
Natural Language Processing
Pattern Recognition, Automated
Reproducibility of Results
Sensitivity and Specificity
Signal Processing, Computer-Assisted
Task Performance and Analysis
Word Processing