期刊
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
卷 60, 期 10, 页码 2839-2847出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2013.2265103
关键词
Bayesian fusion; brain-computer interface (BCI); P300; statistical language model
资金
- Grants-in-Aid for Scientific Research [25280104] Funding Source: KAKEN
Increasing the freedom of communication using conventional row/column (RC) P300 paradigm by naive way (increasing matrix size) may deteriorate inherent distraction effect and interaction speed. In this paper, we propose a two-level predictive (TLP) paradigm by integrating a 3 x 3 two-level matrix paradigm with a statistical language model. The TLP paradigm is evaluated using offline and online data from ten healthy subjects. Significantly larger event-related potentials (ERPs) are evoked by the TLP paradigm compared with the classical 6 x 6 RC. During an online task (correctly spell an English sentence with 57 characters), accuracy and information transfer rate for the TLP are increased by 14.45% and 29.29%, respectively, when compared with the 6 x 6 RC. Time to complete the task is also decreased by 24.61% using TLP. In sharp contrast, an 8 x 8 RC (naive extension of the 6 x 6 RC) consumed 19.18% more time than the classical 6 x 6 RC. Furthermore, the statistical language model is also exploited to improve classification accuracy in a Bayesian approach. The proposed Bayesian fusion method is tested offline on data from the online spelling tasks. The results show its potential improvement on single-trial ERP classification.
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