Motor imagery electroencephalogram (EEG) is widely employed in brain–computer interface (BCI) systems. As a time–frequency analysis method for nonlinear and non-stationary signals, multivariate empirical mode decomposition (MEMD) and its noise-assisted version (NA-MEMD) has been widely used in the preprocessing step of BCI systems for separating EEG rhythms corresponding to specific brain activities. However, when applied to multichannel EEG signals, MEMD or NA-MEMD often demonstrate low robustness to noise and high computational complexity. To address these issues, we have explored the advantages of our recently proposed fast multivariate empirical mode decomposition (FMEMD) and its noise-assisted version (NA-FMEMD) for analyzing motor imagery data. We emphasize that FMEMD enables a more accurate estimation of EEG frequency information and exhibits a more noise-robust decomposition performance with improved computational efficiency. Comparative analysis with MEMD on simulation data and real-world EEG validates the above assertions. The joint average frequency measure is employed to automatically select intrinsic mode functions that correspond to specific frequency bands. Thus, FMEMD-based classification architecture is proposed. Using FMEMD as a preprocessing algorithm instead of MEMD can improve the classification accuracy by 2.3% on the BCI Competition IV dataset. On the Physiobank Motor/Mental Imagery dataset and BCI Competition IV Dataset 2a, FMEMD-based architecture also attained a comparable performance to complex algorithms. The results indicate that FMEMD proficiently extracts feature information from small benchmark datasets while mitigating dimensionality constraints resulting from computational complexity. Hence, FMEMD or NA-FMEMD can be a powerful time–frequency preprocessing method for BCI.

运动想象脑电图（EEG）广泛应用于脑机接口（BCI）系统。作为一种非线性和非平稳信号的时频分析方法，多元经验模态分解（MEMD）及其噪声辅助版本（NA-MEMD）已广泛应用于BCI系统的预处理步骤中，用于分离对应于特定的大脑活动。然而，当应用于多通道 EEG 信号时，MEMD 或 NA-MEMD 通常表现出对噪声的鲁棒性较低和计算复杂性较高。为了解决这些问题，我们探索了最近提出的快速多元经验模式分解（FMEMD）及其噪声辅助版本（NA-FMEMD）在分析运动想象数据时的优势。我们强调，FMEMD 能够更准确地估计 EEG 频率信息，并表现出更强的抗噪声分解性能和更高的计算效率。MEMD 对模拟数据和真实脑电图的比较分析验证了上述主张。采用联合平均频率测量来自动选择与特定频带相对应的本征模式函数。因此，提出了基于FMEMD的分类架构。使用FMEMD作为预处理算法代替MEMD可以在BCI竞赛IV数据集上将分类精度提高2.3%。在 Physiobank 运动/心理意象数据集和 BCI 竞赛 IV 数据集 2a 上，基于 FMEMD 的架构也获得了与复杂算法相当的性能。结果表明，FMEMD 能够从小型基准数据集中熟练地提取特征信息，同时减轻计算复杂性带来的维度限制。因此，FMEMD 或 NA-FMEMD 可以成为 BCI 的强大时频预处理方法。