Motor imagery (MI) classification using electroencephalography (EEG) signals is essential for advancing brain-computer interfaces (BCIs). Traditional EEG channel selection methods often face limitations, such as dependency on single-objective criteria and susceptibility to local optima. To address these challenges, this work proposes a multi-objective optimisation framework that employs non-dominated sorting genetic algorithm, multiple-objective particle swarm optimisation, and a multi-objective evolutionary algorithm based on decomposition. Our approach effectively balances spatial relevance, using a Gaussian kernel, and functional discriminability, which assesses intratrial task-related desynchronisation, thereby improving performance. We evaluated this framework on four EEG datasets: Physionet, OpenBMI, HighGamma, and BCIIV-2A. The proposed approach successfully identifies compact, relevant channel subsets concentrated around sensorimotor cortex regions linked to MI activity, addressing the prevalent challenges of dimensionality and complexity inherent to traditional techniques. Furthermore, the framework achieved classification performance of 87%, 71%, 75%, and 65% on the Physionet, OpenBMI, HighGamma, and BCIIV-2A datasets, respectively. By outperforming existing single-objective and accuracy-based methods, and those relying on fixed subsets, these findings demonstrate that this new multi-objective optimisation framework can enhance MI-based BCI performance while facilitating compact channel configurations with reduced computational complexity, making them better suited for wearable, portable, and real-time BCI applications.

翻译：运动想象脑电图信号分类是推进脑机接口发展的关键。传统脑电图通道选择方法常面临局限性，例如依赖单目标准则且易陷入局部最优。为解决这些挑战，本文提出一种多目标优化框架，采用基于非支配排序遗传算法、多目标粒子群优化以及基于分解的多目标进化算法。该方法通过高斯核函数平衡空间相关性，并利用评估试验内任务相关去同步的功能区分性，从而有效提升性能。我们在四个脑电图数据集（Physionet、OpenBMI、HighGamma 和 BCIIV-2A）上评估了该框架。所提方法成功识别了集中于与运动想象活动相关的感觉运动皮层区域周围的紧凑相关通道子集，应对了传统方法固有的维度和复杂性问题。此外，该框架在 Physionet、OpenBMI、HighGamma 和 BCIIV-2A 数据集上分别实现了 87%、71%、75% 和 65% 的分类性能。通过超越现有的单目标和基于准确率的方法以及依赖固定子集的方法，这些结果表明，这种新型多目标优化框架能够增强基于运动想象的脑机接口性能，同时促进紧凑的通道配置并降低计算复杂度，使其更适用于可穿戴、便携式和实时脑机接口应用。