IEEE Trans Biomed Eng. 2013 Oct;60(10):2839-47. doi: 10.1109/TBME.2013.2265103. Epub 2013 May 30.
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.
通过传统的行/列（RC）P300 范式中的直观方法（增加矩阵大小）来增加通信自由度可能会恶化固有的干扰效应和交互速度。在本文中，我们通过将 3×3 两级矩阵范式与统计语言模型相结合，提出了一种两级预测（TLP）范式。该 TLP 范式通过来自十个健康受试者的离线和在线数据进行评估。与经典的 6×6 RC 相比，TLP 范式诱发的事件相关电位（ERP）明显更大。在在线任务（用 57 个字符正确拼写一个英文句子）中，与 6×6 RC 相比，TLP 的准确率和信息传输率分别提高了 14.45%和 29.29%。使用 TLP 完成任务的时间也减少了 24.61%。相比之下，8×8 RC（6×6 RC 的直观扩展）比经典的 6×6 RC 多消耗 19.18%的时间。此外，还利用统计语言模型以贝叶斯方法提高分类准确率。所提出的贝叶斯融合方法在在线拼写任务的数据上进行了离线测试。结果表明，它在单试 ERP 分类方面具有潜在的改进。
