西安电子科技大学学报 ›› 2020, Vol. 47 ›› Issue (4): 109-116.doi: 10.19665/j.issn1001-2400.2020.04.015

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深度线性判别分析用于两级脑控字符拼写解码

郭柳君(),张雪英(),陈桂军   

  1. 太原理工大学 信息与计算机学院,山西 太原 030024
  • 收稿日期:2019-12-13 出版日期:2020-08-20 发布日期:2020-08-14
  • 通讯作者: 张雪英
  • 作者简介:郭柳君(1995—),女,太原理工大学硕士研究生,E-mail:383537047@qq.com.
  • 基金资助:
    山西省应用基础研究项目(201701D221117);山西省重点研发计划(社会发展)(201803D31045);山西省高等学校科技创新项目(2019L0189);2019年度研究生教育创新计划项目(2019SY100);山西省回国留学人员科研资助项目(201925)

Deep linear discriminant analysis for two-stage brain-controlled character spelling decoding

GUO Liujun(),ZHANG Xueying(),CHEN Guijun   

  1. College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China
  • Received:2019-12-13 Online:2020-08-20 Published:2020-08-14
  • Contact: Xueying ZHANG

摘要:

为了充分利用视听觉感知通道,实现高效的脑控字符拼写,提出一种基于区域的两级拼写范式。该范式的第一级基于运动视觉诱发电位进行目标区域选择,并引入码分多址方法进行区域编码,以提高其选择速率;第二级基于混合运动视觉诱发电位和听觉P300对目标字符进行编码,充分利用视听觉混合效应,改善目标字符选择的准确率。为了对采集的脑电信号进行有效的目标字符解码,提出一种结合深度线性判别分析的脑电信号分类识别算法。实验结果表明,深度线性判别分析算法在两级脑电信号的分类识别中平均分类准确率分别为61.7%和74%,明显高于传统方法和两种卷积神经网络方法的准确率。因此,该算法可有效地改善视听混合诱发两级脑机接口的指令解码性能。

关键词: 脑机接口, 拼写范式, 运动视觉诱发电位, 深度学习

Abstract:

In order to make full use of visual and auditory perception channels and realize efficient brain-controlled character spelling, a two-level spelling paradigm based on the region is proposed. In the first level of the paradigm, the target region is selected based on the motion-onset visual evoked potential, and the code division multiple access method is introduced to improve the selection rate. In the second level, the target character is encoded based on the hybrid motion-onset visually evoked potential and auditory P300 to make full use of the visual and auditory hybrid effect to improve the accuracy of target character selection. In order to decode the collected EEG signals effectively, a classification recognition algorithm for EEG signals combined with a deep linear discriminant analysis is proposed. Experimental results show that the average classification accuracy of the deep linear discriminant analysis algorithm in the classification recognition of two-level EEG signals is 61.7% and 74%, respectively, which is obviously higher than that of the traditional method and the two convolutional neural network methods. Therefore, the algorithm can effectively improve the decoding performance of the two-level brain computer interface induced by the audio-visual hybrid.

Key words: brain computer interface, spelling paradigm, motion-onset visual evoked potential, deep learning

中图分类号: 

  • TP391