Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Department of Biomedical Engineering, Dalian University of Technology, Dalian, 116024, China.
Med Biol Eng Comput. 2017 Dec;55(12):2245-2256. doi: 10.1007/s11517-017-1671-5. Epub 2017 Jun 28.
Recently, many studies have been focusing on optimizing the stimulus of an event-related potential (ERP)-based brain-computer interface (BCI). However, little is known about the effectiveness when increasing the stimulus unpredictability. We investigated a new stimulus type of varied geometric pattern where both complexity and unpredictability of the stimulus are increased. The proposed and classical paradigms were compared in within-subject experiments with 16 healthy participants. Results showed that the BCI performance was significantly improved for the proposed paradigm, with an average online written symbol rate increasing by 138% comparing with that of the classical paradigm. Amplitudes of primary ERP components, such as N1, P2a, P2b, N2, were also found to be significantly enhanced with the proposed paradigm. In this paper, a novel ERP BCI paradigm with a new stimulus type of varied geometric pattern is proposed. By jointly increasing the complexity and unpredictability of the stimulus, the performance of an ERP BCI could be considerably improved.
最近，许多研究都集中在优化基于事件相关电位（ERP）的脑机接口（BCI）的刺激。然而，对于增加刺激的不可预测性的有效性知之甚少。我们研究了一种新的刺激类型，即变化的几何图形，这种刺激类型增加了刺激的复杂性和不可预测性。在 16 名健康参与者的个体内实验中，对提出的和经典的范式进行了比较。结果表明，提出的范式显著提高了 BCI 的性能，与经典范式相比，在线书写符号率平均提高了 138%。还发现，提出的范式显著增强了主要 ERP 成分的幅度，如 N1、P2a、P2b、N2。本文提出了一种新的 ERP BCI 范式，采用了一种新的刺激类型，即变化的几何图形。通过共同增加刺激的复杂性和不可预测性，可以显著提高 ERP BCI 的性能。
