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首页> 外文会议>International conference on material and manufacturing technology >A Kernel Canonical Correlation Analysis Based Idle-State Detection Method for SSVEP-Based Brain-Computer Interfaces
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A Kernel Canonical Correlation Analysis Based Idle-State Detection Method for SSVEP-Based Brain-Computer Interfaces

机译：基于SSVEP的脑机接口基于核规范相关分析的空闲状态检测方法

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摘要

In this paper, we propose a kernel canonical correlation analysis (KCCA) based idle-state detection method for asynchronous steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems.KCCA method can offer a flexible nonlinear solution to adequately extract nonlinear features of multi-electrode electroencephalogram signals.Based on this method, an ensemble KCCA coefficients feature model is proposed by weighting effectively multi-harmonic information and afterwards a threshold classification strategy for idle-state detection is presented.The weights of the model and optimal threshold are trained by the presented parameters learning scheme. Using our method, offline analysis was performed on 10 subjects with 8 fixed common electrodes.The results showed that the idle state could be detected with 95.9% average accuracy when SSVEP could be determined with 93.8% average accuracy.Further, the analysis verified the effectiveness and significant superiority of our method over other widely used ones.
机译：本文针对基于异步稳态视觉诱发电位（SSVEP）的脑机接口（BCI）系统提出了一种基于核规范相关分析（KCCA）的空闲状态检测方法.KCCA方法可以提供一种灵活的非线性解决方案在此基础上，通过有效地加权多谐波信息，提出了一个整体的KCCA系数特征模型，并提出了一种用于空闲状态检测的阈值分类策略。通过提出的参数学习方案训练模型和最佳阈值。用我们的方法对10个对象进行了离线分析，其中8个固定公共电极固定在10个对象上，结果表明，以93.8％的平均准确度确定SSVEP时，可以以95.9％的平均准确度检测到空闲状态。与其他广泛使用的方法相比，我们的方法具有明显的优势。

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