Han Jin, Xu Minpeng, Wang Yijun, Tang Jiabei, Liu Miao, An Xingwei, Jung Tzyy-Ping, Ming Dong
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:4741-4744. doi: 10.1109/EMBC44109.2020.9175275.
Visual brain-computer interface (BCI) systems have made tremendous process in recent years. It has been demonstrated to perform well in spelling words. However, different from spelling English words in one-dimension sequences, Chinese characters are often written in a two-dimensional structure. Previous studies had never investigated how to use BCI to 'write' but not 'spell' Chinese characters. This study developed an innovative BCI-controlled robot for writing Chinese characters. The BCI system contained 108 commands displayed in a 9*12 array. A pixel-based writing method was proposed to map the starting point and ending point of each stroke of Chinese characters to the array. Connecting the starting and ending points for each stroke can make up any Chinese character. The large command set was encoded by the hybrid P300 and SSVEP features efficiently, in which each output needed only 1s of EEG data. The task-related component analysis was used to decode the combined features. Five subjects participated in this study and achieved an average accuracy of 87.23% and a maximal accuracy of 100%. The corresponding information transfer rate was 56.85 bits/min and 71.10 bits/min, respectively. The BCI-controlled robotic arm could write a Chinese character '' with 16 strokes within 5.7 seconds for the best subject. The demo video can be found at https://www.youtube.com/watch?v=A1w-e2dBGl0. The study results demonstrated that the proposed BCI-controlled robot is efficient for writing ideogram (e.g. Chinese characters) and phonogram (e.g. English letter), leading to broad prospects for real-world applications of BCIs.
视觉脑机接口（BCI）系统近年来取得了巨大进展。它已被证明在拼写单词方面表现出色。然而，与在一维序列中拼写英语单词不同，汉字通常以二维结构书写。以往的研究从未探讨过如何使用脑机接口来“书写”而非“拼写”汉字。本研究开发了一种创新的用于书写汉字的脑机接口控制机器人。该脑机接口系统包含以9×12阵列显示的108个命令。提出了一种基于像素的书写方法，将汉字每个笔画的起点和终点映射到该阵列。连接每个笔画的起点和终点可以组成任何汉字。通过混合P300和稳态视觉诱发电位（SSVEP）特征对大量命令集进行了有效编码，其中每个输出仅需要1秒的脑电图数据。使用任务相关成分分析来解码组合特征。五名受试者参与了本研究，平均准确率达到87.23%，最高准确率为100%。相应的信息传输速率分别为56.85比特/分钟和71.10比特/分钟。对于表现最佳的受试者，脑机接口控制的机械臂能够在5.7秒内写出一个有16笔画的汉字“ ”。演示视频可在https://www.youtube.com/watch?v=A1w-e2dBGl0上找到。研究结果表明，所提出的脑机接口控制机器人在书写表意文字（如汉字）和表音文字（如英语字母）方面是有效的，为脑机接口在现实世界中的应用带来了广阔前景。
