IEEE Trans Biomed Eng. 2020 Oct;67(10):2881-2892. doi: 10.1109/TBME.2020.2972747. Epub 2020 Feb 11.
A challenging task for an electroencephalography (EEG)-based asynchronous brain-computer interface (BCI) is to effectively distinguish between the idle state and the control state while maintaining a short response time and a high accuracy when commands are issued in the control state. This study proposes a novel hybrid asynchronous BCI system based on a combination of steady-state visual evoked potentials (SSVEPs) in the EEG signal and blink-related electrooculography (EOG) signals.
Twelve buttons corresponding to 12 characters are included in the graphical user interface (GUI). These buttons flicker at different fixed frequencies and phases to evoke SSVEPs and are simultaneously highlighted by changing their sizes. The user can select a character by focusing on its frequency-phase stimulus and simultaneously blinking his/her eyes in accordance with its highlighting as his/her EEG and EOG signals are recorded. A multifrequency band-based canonical correlation analysis (CCA) method is applied to the EEG data to detect the evoked SSVEPs, whereas the EOG data are analyzed to identify the user's blinks. Finally, the target character is identified based on the SSVEP and blink detection results.
Ten healthy subjects participated in our experiments and achieved an average information transfer rate (ITR) of 105.52 bits/min, an average accuracy of 95.42%, an average response time of 1.34 s and an average false-positive rate (FPR) of 0.8%.
The proposed BCI generates multiple commands with a high ITR and low FPR.
The hybrid asynchronous BCI has great potential for practical applications in communication and control.
对于基于脑电图（EEG）的异步脑机接口（BCI）来说，一项具有挑战性的任务是在保持较短响应时间的同时，在控制状态下有效地区分空闲状态和控制状态，并在控制状态下发出命令时保持较高的准确性。本研究提出了一种新的基于 EEG 信号中的稳态视觉诱发电位（SSVEP）和眨眼相关的眼电图（EOG）信号的混合异步 BCI 系统。
图形用户界面（GUI）中包含 12 个对应于 12 个字符的按钮。这些按钮以不同的固定频率和相位闪烁以产生 SSVEP，并同时通过改变其大小来突出显示。用户可以通过关注其频率相位刺激并根据其突出显示同时眨眼来选择字符，因为他的 EEG 和 EOG 信号正在被记录。基于多频带的典型相关分析（CCA）方法应用于 EEG 数据以检测诱发的 SSVEP，而 EOG 数据则用于识别用户的眨眼。最后，根据 SSVEP 和眨眼检测结果识别目标字符。
10 名健康受试者参加了我们的实验，平均信息传输率（ITR）为 105.52 位/分钟，平均准确率为 95.42%，平均响应时间为 1.34 秒，平均假阳性率（FPR）为 0.8%。
所提出的 BCI 产生了具有高 ITR 和低 FPR 的多个命令。
混合异步 BCI 在通信和控制中的实际应用具有很大的潜力。
