Isaksen Jonas L, Mohebbi Ali, Puthusserypady Sadasivan
Department of Electrical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark.
Laboratory of Experimental Cardiology, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark.
PLoS One. 2017 Sep 13;12(9):e0184785. doi: 10.1371/journal.pone.0184785. eCollection 2017.
In a c-VEP BCI setting, test subjects can have highly varying performances when different pseudorandom sequences are applied as stimulus, and ideally, multiple codes should be supported. On the other hand, repeating the experiment with many different pseudorandom sequences is a laborious process.
This study aimed to suggest an efficient method for choosing the optimal stimulus sequence based on a fast test and simple measures to increase the performance and minimize the time consumption for research trials.
A total of 21 healthy subjects were included in an online wheelchair control task and completed the same task using stimuli based on the m-code, the gold-code, and the Barker-code. Correct/incorrect identification and time consumption were obtained for each identification. Subject-specific templates were characterized and used in a forward-step first-order model to predict the chance of completion and accuracy score.
No specific pseudorandom sequence showed superior accuracy on the group basis. When isolating the individual performances with the highest accuracy, time consumption per identification was not significantly increased. The Accuracy Score aids in predicting what pseudorandom sequence will lead to the best performance using only the templates. The Accuracy Score was higher when the template resembled a delta function the most and when repeated templates were consistent. For completion prediction, only the shape of the template was a significant predictor.
The simple and fast method presented in this study as the Accuracy Score, allows c-VEP based BCI systems to support multiple pseudorandom sequences without increase in trial length. This allows for more personalized BCI systems with better performance to be tested without increased costs.
在基于c-VEP的脑机接口（BCI）环境中，当应用不同的伪随机序列作为刺激时，测试对象的表现可能会有很大差异，理想情况下，应支持多种编码。另一方面，用许多不同的伪随机序列重复实验是一个费力的过程。
本研究旨在提出一种基于快速测试和简单测量来选择最佳刺激序列的有效方法，以提高性能并减少研究试验的时间消耗。
共有21名健康受试者参与在线轮椅控制任务，并使用基于m编码、黄金编码和巴克编码的刺激完成相同任务。每次识别都记录正确/错误识别情况和时间消耗。对受试者特定的模板进行特征化，并用于前向逐步一阶模型，以预测完成的可能性和准确率得分。
在组水平上，没有特定的伪随机序列表现出更高的准确性。当分离出准确率最高的个体表现时，每次识别的时间消耗没有显著增加。准确率得分有助于仅使用模板预测哪种伪随机序列将导致最佳表现。当模板最类似于delta函数且重复模板一致时，准确率得分更高。对于完成预测，只有模板的形状是一个显著的预测因素。
本研究提出的作为准确率得分的简单快速方法，使基于c-VEP的BCI系统能够支持多种伪随机序列，而不会增加试验长度。这使得可以在不增加成本的情况下测试性能更好的更个性化的BCI系统。
