Semi-simulation Experiments for Quantifying the Performance of SSVEP-based BCI after Reducing Artifacts from Trapezius Muscles
Semi-simulation Experiments for Quantifying the Performance of SSVEP-based BCI after Reducing Artifacts from Trapezius Muscles
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DOI：
10.1109/embc.2018.8513180
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
S. Kanoga;M. Nakanishi;A. Murai;M. Tada;Atsunori Kanemura
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作者：
S. Kanoga;M. Nakanishi;A. Murai;M. Tada;Atsunori Kanemura
肌肉伪影经常污染脑电图（EEG）并恶化脑机接口（BCI）的性能。虽然有许多伪影减少技术，但大多数研究都集中在它们的减少能力（即重建误差）上，而没有评估它们对BCI性能的影响。本研究旨在评估基于稳态视觉诱发电位（SSVEPs）的BCI场景中最先进的肌肉伪影减少技术的性能。性能进行了评估的基础上，半模拟设置使用的基准数据集的SSVEP人工污染的肌肉伪影获得的肌束。实验结果表明，将伪影减少方法与基于任务相关成分分析的分类算法相结合，可以提高分类精度。有趣的是，使重建误差最小化的伪影减少设置，即精心恢复真实EEG波形，与使分类性能最大化的伪影减少设置不一致。结果表明，伪影减少方法应进行调整，以最大限度地提高性能的脑机接口。
Muscular artifacts often contaminate electroencephalograms (EEGs) and deteriorate the performance of brain–computer interfaces (BCIs). Although many artifact reduction techniques are available, most of the studies have focused on their reduction ability (i.e. reconstruction errors), and it has been missing to evaluate their effect on the performance of BCIs. This study aims at evaluating the performance of a state-of-the-art muscular artifact reduction technique on a scenario of a steady-state visual evoked potentials (SSVEPs)based BCI. The performance was evaluated based on a semisimulation setting using a benchmark dataset of SSVEPs artificially contaminated by muscular artifacts acquired from the trapezius. Our results showed that combining the artifact reduction method and the classification algorithm based on the task-related component analysis gained improved classification accuracy. Interestingly, the artifact reduction setting minimizing the reconstruction errors, i.e. elaborately recovering the true EEG waveforms, was inconsistent to the one maximizing the classification performance. The results suggest that artifact reduction methods should be tuned so as to tomaximize performance of BCIs.