Abstract
Brain-Computer Interfaces (BCIs) are promising technologies that control computers by thoughts, and they are used to restore control and communication for severely paralyzed people such as those with amyotrophic lateral sclerosis (ALS). Moreover, BCIs can be appealied to healthy people as well. The P300 speller is one of the BCI applications, which uses the P300: an feature of electroencephalogram (EEG), and it allows users to select letters just by thoughts. The P300 speller presents the user with a matrix containing letters and numbers. The user attends to a character to be communicated and the rows and columns of the matrix are briefly intensified randomly. The intensification of the attended character is served as a rare event in an oddball sequence and it elicits a P300 response. The P300 speller detects the character with the elicited P300 response. However, due to the low signal-to-noise ratio of the P300, signal averaging is often performed, which improves the spelling accuracy but degrades the spelling speed. Predictive text, implemented in the most mobile phones, enables users to spell with fewer key presses; thus, it could improve the spelling speed of the P300 speller. This paper implements a predictive text to the P300 speller, and examines how much the spelling speed for Japanese text is improved. In result, the input time was reduced 21% on an average, 33% at the maximum in the experiments using EEG and th e usability of the proposed system was confirmed.
