Department of Electrical Engineering and Computer Science, Seoul National University, Seoul 133-791, Republic of Korea.
Med Biol Eng Comput. 2013 May;51(5):571-9. doi: 10.1007/s11517-012-1026-1. Epub 2013 Jan 17.
To date, most EEG-based brain-computer interface (BCI) studies have focused only on enhancing BCI performance in such areas as classification accuracy and information transfer rate. In practice, however, test-retest reliability of the developed BCI systems must also be considered for use in long-term, daily life applications. One factor that can affect the reliability of BCI systems is the slight displacement of EEG electrode locations that often occurs due to the removal and reattachment of recording electrodes. The aim of this study was to evaluate and compare various feature extraction methods for motor-imagery-based BCI in terms of robustness to slight changes in electrode locations. To this end, EEG signals were recorded from three reference electrodes (Fz, C3, and C4) and from six additional electrodes located close to the reference electrodes with a 1-cm inter-electrode distance. Eight healthy participants underwent 180 trials of left- and right-hand motor imagery tasks. The performance of four different feature extraction methods [power spectral density (PSD), phase locking value (PLV), a combination of PSD and PLV, and cross-correlation (CC)] were evaluated using five-fold cross-validation and linear discriminant analysis, in terms of robustness to electrode location changes as well as regarding absolute classification accuracy. The quantitative evaluation results demonstrated that the use of either PSD- or CC-based features led to higher classification accuracy than the use of PLV-based features, while PSD-based features showed much higher sensitivity to changes in EEG electrode location than CC- or PLV-based features. Our results suggest that CC can be used as a promising feature extraction method in motor-imagery-based BCI studies, since it provides high classification accuracy along with being little affected by slight changes in the EEG electrode locations.
迄今为止，大多数基于脑电图的脑机接口 (BCI) 研究都仅集中于提高分类准确性和信息传输率等方面的 BCI 性能。然而，在实践中，开发的 BCI 系统的测试-重测可靠性也必须考虑到长期、日常生活应用中的使用。影响 BCI 系统可靠性的一个因素是脑电图电极位置的轻微位移，这通常是由于记录电极的移除和重新连接而发生的。本研究的目的是评估和比较基于运动想象的 BCI 的各种特征提取方法在电极位置轻微变化下的稳健性。为此，从三个参考电极（Fz、C3 和 C4）和靠近参考电极的六个具有 1 厘米电极间距离的附加电极记录 EEG 信号。八名健康参与者完成了 180 次左手和右手运动想象任务。使用五折交叉验证和线性判别分析评估了四种不同特征提取方法（功率谱密度 (PSD)、锁相值 (PLV)、PSD 和 PLV 的组合以及互相关 (CC)）的性能，考察了它们对电极位置变化的稳健性以及绝对分类准确性。定量评估结果表明，使用 PSD 或 CC 为基础的特征比使用 PLV 为基础的特征导致更高的分类准确性，而 PSD 为基础的特征对 EEG 电极位置的变化比 CC 或 PLV 为基础的特征更敏感。我们的结果表明，CC 可以作为基于运动想象的 BCI 研究中一种有前途的特征提取方法，因为它在受到 EEG 电极位置轻微变化的影响较小的情况下提供了较高的分类准确性。
