基于脑电信号的视觉脑机接口检测性能分析

2023 International Conference on Electronics, Information, and Communication (ICEIC) Pub Date : 2023-02-05 DOI:10.1109/ICEIC57457.2023.10049806

Kibae Lee, Ghun Hyeok Ko, C. Lee, Yoon-Sang Jeong

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引用次数: 0

摘要

脑机接口(bci)为人与计算机的交流提供了可能。事件相关电位(Event related potential, ERP)和稳态视觉诱发电位(steady-state visual evoked potential, SSVEP)研究主要通过简单图像来观察大脑对视觉刺激的反应特征。在本文中，我们根据静止图像中目标的存在或不存在，给出了一个Visual BCI数据集，并分析了各种特征提取和分类算法的性能。在各种实验中，基于Takens延迟嵌入的方差均值比(VMR)的平均准确率在69.23 ~ 76.40%之间。

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Detection Performance Analysis based on EEG Signal for Visual BCI

The brain computer interfaces (BCIs) offer a possibility of communication for people with computer. Event related potential (ERP) and steady-state visual evoked potential (SSVEP) studies using simple images were mainly conducted to observe brain response characteristics to visual stimuli. In this paper, we present a Visual BCI dataset according to the presence or absence of a target in a still image and analyze the performance of various feature extraction and classification algorithms. Throughout various experiments, the proposed variance and mean ratio (VMR) based on Takens' delay embedding showed the best average accuracy from 69.23 to 76.40%.

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2023 International Conference on Electronics, Information, and Communication (ICEIC)

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