Objective: Motor Imagery (MI) serves as a crucial experimental paradigm within the realm of Brain Computer Interfaces (BCIs), aiming to decoding motor intentions from electroencephalogram (EEG) signals. Method: Drawing inspiration from Riemannian geometry and Cross-Frequency Coupling (CFC), this paper introduces a novel approach termed Riemann Tangent Space Mapping using Dichotomous Filter Bank with Convolutional Neural Network (DFBRTS) to enhance the representation quality and decoding capability pertaining to MI features. DFBRTS first initiates the process by meticulously filtering EEG signals through a Dichotomous Filter Bank, structured in the fashion of a complete binary tree. Subsequently, it employs Riemann Tangent Space Mapping to extract salient EEG signal features within each sub-band. Finally, a lightweight convolutional neural network is employed for further feature extraction and classification, operating under the joint supervision of cross-entropy and center loss. To validate the efficacy, extensive experiments were conducted using DFBRTS on two well-established benchmark datasets: the BCI competition IV 2a (BCIC-IV-2a) dataset and the OpenBMI dataset. The performance of DFBRTS was benchmarked against several state-of-the-art MI decoding methods, alongside other Riemannian geometry-based MI decoding approaches. Results: DFBRTS significantly outperforms other MI decoding algorithms on both datasets, achieving a remarkable classification accuracy of 78.16% for four-class and 71.58% for two-class hold-out classification, as compared to the existing benchmarks.

翻译：目的：运动想象（MI）是脑机接口（BCI）领域的重要实验范式，旨在从脑电图（EEG）信号中解码运动意图。方法：受黎曼几何与跨频耦合（CFC）启发，本文提出一种新方法——采用二分滤波器组与卷积神经网络的黎曼切空间映射（DFBRTS），以提升MI特征的表征质量与解码能力。DFBRTS首先通过按完全二叉树结构组织的二分滤波器组对EEG信号进行精细滤波；随后，利用黎曼切空间映射提取每个子带内的显著EEG信号特征；最后，采用轻量级卷积神经网络在交叉熵损失与中心损失的联合监督下进行特征提取与分类。为验证有效性，我们在两个公认的基准数据集——BCI竞赛IV 2a（BCIC-IV-2a）数据集与OpenBMI数据集上，开展了基于DFBRTS的广泛实验。将DFBRTS的性能与多种最先进的MI解码方法及其他基于黎曼几何的MI解码方法进行对比。结果：在两个数据集上，DFBRTS均显著优于其他MI解码算法，在四类留出分类中达到78.16%的卓越分类准确率，在两类留出分类中达到71.58%的准确率，优于现有基准方法。