基于可穿戴式脑磁图的视觉混合脑机接口研究
收稿日期: 2024-02-02
网络出版日期: 2024-03-11
基金资助
季华实验室项目——新一代可穿戴脑磁图仪研制(X190131TD190)
Study of Visual Hybrid Brain-Computer Interface Based on Wearable Magnetoencephalogram
Received date: 2024-02-02
Online published: 2024-03-11
新兴的可穿戴式脑磁图技术为脑机接口（BCI）提供高质量数据奠定了基础．为探究可穿戴式脑磁图应用于视觉混合BCI上的可行性，本文基于稳态视觉诱发场（SSVEF）和Alpha波设计了SSVEF-Alpha混合BCI，并在不同分类模型上进行了性能对比．结果表明，基于用户依赖（UD）的训练方法，混合BCI 6分类平均分类准确率为(93.29±1.69)%，信息传输速率可达86.81 bits/min，且使用短数据长度进行用户独立（UI）的训练方法比免训练的方法更具优越性．本研究验证了视觉混合BCI的有效性，为进一步开发设计可穿戴式脑磁图的BCI应用产品提供参考范例．
王晨旭 , 郭旭 , 王慧 , 张欣 , 常严 , 郭清乾 , 胡涛 , 冯晓宇 , 杨晓冬 . 基于可穿戴式脑磁图的视觉混合脑机接口研究[J]. 波谱学杂志, 2024 , 41(4) : 405 -417 . DOI: 10.11938/cjmr20243096
The emerging wearable magnetoencephalography technology lays the foundation for brain-computer interface to provide high-quality data. To explore the feasibility of applying wearable magnetoencephalography in visual hybrid brain-computer interface, a SSVEF-Alpha hybrid brain-computer interface is designed based on steady-state visual evoked field and Alpha wave, and the performance is compared with different classification models. The results show that based on the user-dependent training method, the average classification accuracy of hybrid brain-computer interface is (93.29±1.69)%, the information transmission rate can reach 86.81 bits/min. And the user-independent training method with short data length shows superiority over the training-free method. This study verifies the effectiveness of visual hybrid brain-computer interface and provides a reference example for further development and design of brain-computer interface products of wearable magnetoencephalography.
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