Brain-Computer Interface Paradigms and Signal Processing Strategy for Steady State Visual Evoked Potential

doi:10.7652/xjtuxb201506001

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Brain-Computer Interface Paradigms and Signal Processing Strategy for Steady State Visual Evoked Potential

更新时间：2025-07-09

- Brain-Computer Interface Paradigms and Signal Processing Strategy for Steady State Visual Evoked Potential
- Vol. 49, Issue 6, Pages: 1-7+108(2015)
- 作者机构：
  
  1. 西安交通大学机械工程学院,西安,710049
  2. 西安交通大学机械制造系统工程国家重点实验室,西安,710054
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201506001
  CLC： TP23
- Online First：10 June 2015，
  
  Published：2015
- 稿件说明：
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Brain-Computer Interface Paradigms and Signal Processing Strategy for Steady State Visual Evoked Potential[J]. 2015, 49(6): 1-7+108.
DOI：

Brain-Computer Interface Paradigms and Signal Processing Strategy for Steady State Visual Evoked Potential[J]. 2015, 49(6): 1-7+108. DOI： 10.7652/xjtuxb201506001.

摘要

在概述国内外稳态视觉诱发电位脑机接口技术研究的基础上

针对传统稳态视觉诱发电位(SSVEP)在脑-机接口(BCI)系统应用中存在的问题

在范式设计方面

分别提出了基于牛顿环、高频组合编码和幅值调制的SSVEP的3种BCI范式。针对脑电信号微弱、辨识困难的问题

提出了基于随机共振机制的稳态运动视觉诱发电位增强方法; 针对高频组合编码稳态视觉诱发电位(CCH-SSVEP)新范式响应信号的非平稳、弱信号特征

提出基于改进的希尔伯特黄变换的CCH-SSVEP响应信号处理方法

提高了识别率。在系统应用方面

将牛顿环运动刺激范式与运动场景相结合

设计了场景结合导航技术

相对于传统方法

将刺激目标关联具体的物理位置

导航效率显著提升

将运动场景与刺激目标结合的所见即所得的方式提升了用户预选目标效率以及路线规划能力

同时也有利于用户集中注意力

提高脑电信噪比。最终

将该技术成功地应用于残疾轮椅的脑电导航控制中

取得了令人满意的效果。

Abstract

Following an overview of the recent progress in steady state visual evoked potential(SSVEP)based brain-computer interfaces(BCIs)

three new SSVEP paradigms for the brain-computer interface system are proposed to solve the problems in the traditional SSVEP-BCI

namely steady-state motion visual evoked potentials(SSMVEP)based BCI produced by oscillating Newton's rings

time series combination coding-based high-frequency SSVEP(CCH-SSVEP)

and amplitude modulated visual evoked potential. For identifying weak EEG signals

the enhancement method of steady-state motion visual evoked potential based on stochastic resonance mechanism is adopted. For extracting the time-frequency characteristics of high-frequency time series combination coding-based SSVEPs

the improved Hilbert-Huang transform -based variable frequency EEG feature extraction method is suggested

which facilitates increasing the recognition efficiency of SSVEP. In BCI application

the scene-combined navigational technology via the combination of SSMVEP and motion scene is introduced

where the target stimulus is associated with specific physical location

to improve navigation efficiency and user pre-select target efficiency and to plan the path from the view of “what you see is what you get” in which the movement scene combines with target stimulus to focus and improve EEG SNR for users. The strategy has been applied to intelligent wheelchair's BCI navigation with satisfactory evaluation.

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references

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