Design of a Modified P300 Speller System Based on Prediction by Partial Matching Language Model
Year: 2012
Type: article
Abstract: In recent decades, the field of Brain-Computer Interface (BCI) technologies has been vigorously developed by research groups from all over the world. A BCI system can build a directly pathway between the brain and an external device so that patients with impaired motor activities can be assisted with this system to realize the communication with others. Among varieties of BCI systems, P300 speller is one that has been successfully developed with several advantages such as easy to carry on the experiment and the achievement of relatively better accuracy rate. In this thesis a system is designed to improve the current P300 speller performance based on a language prediction model. The method is implemented with MATLAB simulations in the evaluation of both system accuracy and speedup. The result of the conducted experiments shows the feasibility of our proposed system. To my Dearest Parents
Author: Mengxia Wang
Institution: University of Cincinnati
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2341625903