IIT-Unit, Department of Electronics and Information, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy.
Comput Intell Neurosci. 2010;2010:307254. doi: 10.1155/2010/307254. Epub 2010 Feb 11.
Error potentials (ErrPs), that is, alterations of the EEG traces related to the subject perception of erroneous responses, have been suggested to be an elegant way to recognize misinterpreted commands in brain-computer interface (BCI) systems. We implemented a P300-based BCI speller that uses a genetic algorithm (GA) to detect P300s, and added an automatic error-correction system (ECS) based on the single-sweep detection of ErrPs. The developed system was tested on-line on three subjects and here we report preliminary results. In two out of three subjects, the GA provided a good performance in detecting P300 (90% and 60% accuracy with 5 repetitions), and it was possible to detect ErrP with an accuracy (roughly 60%) well above the chance level. In our knowledge, this is the first time that ErrP detection is performed on-line in a P300-based BCI. Preliminary results are encouraging, but further refinements are needed to improve performances.
错误相关电位（ErrPs），即与受试者感知错误反应相关的 EEG 轨迹的改变，被认为是识别脑机接口（BCI）系统中误译命令的一种优雅方式。我们实现了一种基于 P300 的 BCI 拼写器，该拼写器使用遗传算法（GA）来检测 P300，并添加了基于 ErrPs 单次扫描检测的自动纠错系统（ECS）。该开发系统在三位受试者上进行了在线测试，我们在此报告初步结果。在三位受试者中的两位中，GA 在检测 P300 方面表现良好（5 次重复准确率为 90%和 60%），并且可以以远高于随机水平的准确率（大致 60%）检测到 ErrP。据我们所知，这是首次在基于 P300 的 BCI 中在线执行 ErrP 检测。初步结果令人鼓舞，但需要进一步改进以提高性能。
