Language-Model Assisted Brain Computer Interface for Typing: A Comparison of Matrix and Rapid Serial Visual Presentation.
Journal:
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Published Date:
Mar 11, 2015
Abstract
Noninvasive electroencephalography (EEG)-based brain-computer interfaces (BCIs) popularly utilize event-related potential (ERP) for intent detection. Specifically, for EEG-based BCI typing systems, different symbol presentation paradigms have been utilized to induce ERPs. In this manuscript, through an experimental study, we assess the speed, recorded signal quality, and system accuracy of a language-model-assisted BCI typing system using three different presentation paradigms: a 4 × 7 matrix paradigm of a 28-character alphabet with row-column presentation (RCP) and single-character presentation (SCP), and rapid serial visual presentation (RSVP) of the same. Our analyses show that signal quality and classification accuracy are comparable between the two visual stimulus presentation paradigms. In addition, we observe that while the matrix-based paradigm can be generally employed with lower inter-trial-interval (ITI) values, the best presentation paradigm and ITI value configuration is user dependent. This potentially warrants offering both presentation paradigms and variable ITI options to users of BCI typing systems.
Authors
Keywords
Adult
Algorithms
Brain-Computer Interfaces
Communication Devices for People with Disabilities
Electroencephalography
Evoked Potentials, Visual
Female
Humans
Language
Machine Learning
Male
Models, Neurological
Natural Language Processing
Pattern Recognition, Automated
Photic Stimulation
Reproducibility of Results
Sensitivity and Specificity
Task Performance and Analysis
Word Processing