title: A two-level predictive event-related potential-based brain-computer interface.
abstract: 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 3×3 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 6×6 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 6×6 RC. Time to complete the task is also decreased by 24.61% using TLP. In sharp contrast, an 8×8 RC (naive extension of the 6×6 RC) consumed 19.18% more time than the classical 6×6 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.
authors.forename: Yaming
authors.initials: Y
authors.lastname: Xu
authors.forename: Yoshikazu
authors.initials: Y
authors.lastname: Nakajima
mesh_terms: D000328:Adult; D000465:Algorithms; D062207:Brain-Computer Interfaces; D003627:Data Interpretation, Statistical; D018913:Event-Related Potentials, P300 / Q000502:physiology*; D005071:Evoked Potentials / Q000502:physiology*; D005260:Female; D006801:Humans; D066298:In Vitro Techniques; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D014793:Visual Cortex / Q000502:physiology*; D014796:Visual Perception / Q000502:physiology*