IEEE Trans Neural Syst Rehabil Eng. 2023;31:3212-3222. doi: 10.1109/TNSRE.2023.3301507. Epub 2023 Aug 10.
Brain-computer interfaces (BCIs) have revolutionized the way humans interact with machines, particularly for patients with severe motor impairments. EEG-based BCIs have limited functionality due to the restricted pool of stimuli that they can distinguish, while those elaborating event-related potentials up to now employ paradigms that require the patient's perception of the eliciting stimulus. In this work, we propose MIRACLE: a novel BCI system that combines functional data analysis and machine-learning techniques to decode patients' minds from the elicited potentials. MIRACLE relies on a hierarchical ensemble classifier recognizing 10 different semantic categories of imagined stimuli. We validated MIRACLE on an extensive dataset collected from 20 volunteers, with both imagined and perceived stimuli, to compare the system performance on the two. Furthermore, we quantify the importance of each EEG channel in the decision-making process of the classifier, which can help reduce the number of electrodes required for data acquisition, enhancing patients' comfort.
脑机接口 (BCI) 改变了人类与机器交互的方式，特别是对于严重运动障碍的患者。基于脑电图的 BCI 由于其可区分的刺激物有限，因此功能有限，而迄今为止，那些详细阐述事件相关电位的 BCI 则采用需要患者感知诱发刺激的范式。在这项工作中，我们提出了 MIRACLE：一种新的 BCI 系统，它结合了功能数据分析和机器学习技术，从诱发的电位中解码患者的思维。MIRACLE 依赖于一个层次化的集成分类器，用于识别想象刺激的 10 种不同语义类别。我们在从 20 名志愿者收集的广泛数据集上验证了 MIRACLE，其中包括想象和感知刺激，以比较两种情况下系统的性能。此外，我们量化了分类器决策过程中每个 EEG 通道的重要性，这有助于减少采集数据所需的电极数量，提高患者的舒适度。
