Chu Chenxi, Luo Jingjing, Tian Xiwei, Han Xiangke, Guo Shijie
Institute of Artificial Intelligence (AI) and Robotics, Academy for Engineering and Technology, Fudan University, as well as Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai, China.
Guanghua Lingang Engineering Application and Technology R&D (Shanghai) Co., Ltd., Shanghai, China.
Front Hum Neurosci. 2021 Apr 14;15:641357. doi: 10.3389/fnhum.2021.641357. eCollection 2021.
This paper proposed a novel tactile-stimuli P300 paradigm for Brain-Computer Interface (BCI), which potentially targeted at people with less learning ability or difficulty in maintaining attention. The new paradigm using only two types of stimuli was designed, and different targets were distinguished by frequency and spatial information. The classification algorithm was developed by introducing filters for frequency bands selection and conducting optimization with common spatial pattern (CSP) on the tactile evoked EEG signals. It features a combination of spatial and frequency information, with the spatial information distinguishing the sites of stimuli and frequency information identifying target stimuli and disturbances. We investigated both electrical stimuli and vibration stimuli, in which only one target site was stimulated in each block. The results demonstrated an average accuracy of 94.88% for electrical stimuli and 95.21% for vibration stimuli, respectively.
本文提出了一种用于脑机接口（BCI）的新型触觉刺激P300范式，该范式潜在地针对学习能力较弱或难以保持注意力的人群。设计了仅使用两种刺激类型的新范式，通过频率和空间信息区分不同目标。通过引入用于频段选择的滤波器并对触觉诱发的脑电信号进行共同空间模式（CSP）优化，开发了分类算法。它具有空间和频率信息相结合的特点，空间信息区分刺激部位，频率信息识别目标刺激和干扰。我们研究了电刺激和振动刺激，每个区块仅刺激一个目标部位。结果表明，电刺激的平均准确率分别为94.88%，振动刺激的平均准确率为95.21%。
