Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.
Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, China.
Comput Intell Neurosci. 2022 Jan 30;2022:1038901. doi: 10.1155/2022/1038901. eCollection 2022.
The traditional imagery task for brain-computer interfaces (BCIs) consists of motor imagery (MI) in which subjects are instructed to imagine moving certain parts of their body. This kind of imagery task is difficult for subjects. In this study, we used a less studied yet more easily performed type of mental imagery-visual imagery (VI)-in which subjects are instructed to visualize a picture in their brain to implement a BCI. In this study, 18 subjects were recruited and instructed to observe one of two visual-cued pictures (one was static, while the other was moving) and then imagine the cued picture in each trial. Simultaneously, electroencephalography (EEG) signals were collected. Hilbert-Huang Transform (HHT), autoregressive (AR) models, and a combination of empirical mode decomposition (EMD) and AR were used to extract features, respectively. A support vector machine (SVM) was used to classify the two kinds of VI tasks. The average, highest, and lowest classification accuracies of HHT were 68.14 ± 3.06%, 78.33%, and 53.3%, respectively. The values of the AR model were 56.29 ± 2.73%, 71.67%, and 30%, respectively. The values obtained by the combination of the EMD and the AR model were 78.40 ± 2.07%, 87%, and 48.33%, respectively. The results indicate that multiple VI tasks were separable based on EEG and that the combination of EMD and an AR model used in VI feature extraction was better than an HHT or AR model alone. Our work may provide ideas for the construction of a new online VI-BCI.
传统的脑机接口（BCI）意象任务由运动想象（MI）组成，要求受试者想象移动身体的某些部位。这种想象任务对受试者来说很困难。在这项研究中，我们使用了一种研究较少但更容易执行的心理意象类型——视觉意象（VI）——要求受试者在大脑中想象一张图片来实现 BCI。在这项研究中，招募了 18 名受试者，并要求他们观察两个视觉提示图片中的一个（一个是静态的，另一个是移动的），然后在每个试验中想象提示的图片。同时，采集了脑电图（EEG）信号。分别使用希尔伯特-黄变换（HHT）、自回归（AR）模型和经验模态分解（EMD）与 AR 的组合来提取特征。使用支持向量机（SVM）对两种 VI 任务进行分类。HHT 的平均、最高和最低分类准确率分别为 68.14±3.06%、78.33%和 53.3%。AR 模型的值分别为 56.29±2.73%、71.67%和 30%。EMD 和 AR 模型组合的值分别为 78.40±2.07%、87%和 48.33%。结果表明，基于 EEG 可以分离多种 VI 任务，并且在 VI 特征提取中使用 EMD 和 AR 模型的组合优于单独使用 HHT 或 AR 模型。我们的工作可能为构建新的在线 VI-BCI 提供思路。
