Tecnologico de Monterrey, School of Engineering and Science, Monterrey, NL 64849, Mexico.
Sensors (Basel). 2020 Dec 16;20(24):7198. doi: 10.3390/s20247198.
The P300 paradigm is one of the most promising techniques for its robustness and reliability in Brain-Computer Interface (BCI) applications, but it is not exempt from shortcomings. The present work studied single-trial classification effectiveness in distinguishing between target and non-target responses considering two conditions of visual stimulation and the variation of the number of symbols presented to the user in a single-option visual frame. In addition, we also investigated the relationship between the classification results of target and non-target events when training and testing the machine-learning model with datasets containing different stimulation conditions and different number of symbols. To this end, we designed a P300 experimental protocol considering, as conditions of stimulation: the color highlighting or the superimposing of a cartoon face and from four to nine options. These experiments were carried out with 19 healthy subjects in 3 sessions. The results showed that the Event-Related Potentials (ERP) responses and the classification accuracy are stronger with cartoon faces as stimulus type and similar irrespective of the amount of options. In addition, the classification performance is reduced when using datasets with different type of stimulus, but it is similar when using datasets with different the number of symbols. These results have a special connotation for the design of systems, in which it is intended to elicit higher levels of evoked potentials and, at the same time, optimize training time.
P300 范式是脑机接口（BCI）应用中最有前途的技术之一，因其稳健性和可靠性而备受关注，但它并非没有缺点。本研究考虑了两种视觉刺激条件和单个选项视觉框架中呈现给用户的符号数量的变化，研究了在区分目标和非目标响应中单试分类的有效性。此外，我们还研究了当使用包含不同刺激条件和不同符号数量的数据集训练和测试机器学习模型时，目标和非目标事件的分类结果之间的关系。为此，我们设计了一个 P300 实验方案，考虑了以下刺激条件：颜色突出显示或叠加卡通面孔，以及从四个到九个选项。这些实验在三个会话中进行，共有 19 名健康受试者参加。结果表明，使用卡通面孔作为刺激类型时，事件相关电位（ERP）响应和分类准确性更强，而与选项数量无关。此外，当使用具有不同刺激类型的数据集时，分类性能会降低，但当使用具有不同符号数量的数据集时，分类性能相似。这些结果对于系统的设计具有特殊的意义，因为系统旨在引发更高水平的诱发电位，同时优化训练时间。
