IEEE Trans Neural Syst Rehabil Eng. 2022;30:465-475. doi: 10.1109/TNSRE.2021.3125386. Epub 2022 Mar 8.
With the development of the brain-computer interface (BCI) community, motor imagery-based BCI system using electroencephalogram (EEG) has attracted increasing attention because of its portability and low cost. Concerning the multi-channel EEG, the frequency component is one of the most critical features. However, insufficient extraction hinders the development and application of MI-BCIs. To deeply mine the frequency information, we proposed a method called tensor-based frequency feature combination (TFFC). It combined tensor-to-vector projection (TVP), fast fourier transform (FFT), common spatial pattern (CSP) and feature fusion to construct a new feature set. With two datasets, we used different classifiers to compare TFFC with the state-of-the-art feature extraction methods. The experimental results showed that our proposed TFFC could robustly improve the classification accuracy of about 5% ( ). Moreover, visualization analysis implied that the TFFC was a generalization of CSP and Filter Bank CSP (FBCSP). Also, a complementarity between weighted narrowband features (wNBFs) and broadband features (BBFs) was observed from the averaged fusion ratio. This article certificates the importance of frequency information in the MI-BCI system and provides a new direction for designing a feature set of MI-EEG.
随着脑-机接口（BCI）领域的发展，基于运动想象的脑电（EEG）BCI 系统因其便携性和低成本而受到越来越多的关注。对于多通道 EEG，频率成分是最关键的特征之一。然而，由于提取不足，限制了 MI-BCI 的发展和应用。为了深入挖掘频率信息，我们提出了一种称为张量频域特征组合（TFFC）的方法。它结合张量到向量投影（TVP）、快速傅里叶变换（FFT）、共空间模式（CSP）和特征融合，构建了一个新的特征集。在两个数据集上，我们使用不同的分类器将 TFFC 与最先进的特征提取方法进行比较。实验结果表明，我们提出的 TFFC 可以稳健地提高约 5%的分类精度（）。此外，可视化分析表明，TFFC 是 CSP 和滤波组 CSP（FBCSP）的推广。从平均融合比中还观察到加权窄带特征（wNBFs）和宽带特征（BBFs）之间的互补性。本文证明了频率信息在 MI-BCI 系统中的重要性，并为 MI-EEG 的特征集设计提供了新的方向。
