To provide speech prostheses for individuals with severe communication impairments, we investigated a classification method for brain computer interfaces (BCIs) using silent speech. Event-related potentials (ERPs) were recorded using scalp electrodes when five subjects imagined the vocalization of Japanese vowels, /a/, /i/, /u/, /e/, and /o/ in order and in random order, while the subjects remained silent and immobilized. For actualization, we tried to apply relevance vector machine (RVM) and RVM with Gaussian kernel (RVM-G) instead of support vector machine with Gaussian kernel (SVM-G) to reduce the calculation cost in the use of 19 channels, common special patterns (CSPs) filtering, and adaptive collection (AC). Results show that using RVM-G instead of SVM-G reduced the ratio of the number of efficient vectors to the number of training data from 97% to 55%. At this time, the averaged classification accuracies (CAs) using SVM-G and RVM-G were, respectively, 77% and 79%, showing no degradation. However, the calculation cost was more than that using SVM-G because RVM-G necessitates high calculation costs for optimization. Furthermore, results show that CAs using RVM-G were weaker than SVM-G when the training data were few. Additionally, results showed that nonlinear classification was necessary for silent speech classification. This paper serves as a beginning of feasibility study for speech prostheses using an imagined voice. Although classification for silent speech presents great potential, many feasibility problems remain. 
Classification of silent speech using support vector machine and relevance vector machine