College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300000, China.
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300000, China.
Sensors (Basel). 2020 Jun 25;20(12):3588. doi: 10.3390/s20123588.
Brain-computer interfaces (BCI) have witnessed a rapid development in recent years. However, the active BCI paradigm is still underdeveloped with a lack of variety. It is imperative to adapt more voluntary mental activities for the active BCI control, which can induce separable electroencephalography (EEG) features. This study aims to demonstrate the brain function of timing prediction, i.e., the expectation of upcoming time intervals, is accessible for BCIs. Eighteen subjects were selected for this study. They were trained to have a precise idea of two sub-second time intervals, i.e., 400 ms and 600 ms, and were asked to measure a time interval of either 400 ms or 600 ms in mind after a cue onset. The EEG features induced by timing prediction were analyzed and classified using the combined discriminative canonical pattern matching and common spatial pattern. It was found that the ERPs in low-frequency (04 Hz) and energy in high-frequency (2060 Hz) were separable for distinct timing predictions. The accuracy reached the highest of 93.75% with an average of 76.45% for the classification of 400 vs. 600 ms timing. This study first demonstrates that the cognitive EEG features induced by timing prediction are detectable and separable, which is feasible to be used in active BCIs controls and can broaden the category of BCIs.
脑-机接口（BCI）近年来发展迅速。然而，主动 BCI 范式仍不够发达，缺乏多样性。必须适应更多的自愿心理活动来进行主动 BCI 控制，从而产生可分离的脑电图（EEG）特征。本研究旨在证明大脑的定时预测功能，即对即将到来的时间间隔的预期，可用于 BCI。本研究选择了 18 名受试者。他们接受了精确的 400ms 和 600ms 两个亚秒时间间隔的训练，并被要求在提示出现后在脑海中测量 400ms 或 600ms 的时间间隔。使用联合判别正则模式匹配和公共空间模式对定时预测引起的 EEG 特征进行分析和分类。结果发现，低频（04Hz）的 ERP 和高频（2060Hz）的能量对于不同的定时预测是可分离的。400ms 与 600ms 定时分类的准确率最高可达 93.75%，平均准确率为 76.45%。本研究首次证明了定时预测引起的认知 EEG 特征是可检测和可分离的，这在主动 BCI 控制中是可行的，并可以拓宽 BCI 的类别。
