Arpaia Pasquale, Donnarumma Francesco, Esposito Antonio, Parvis Marco
Department of Electrical Engineering and Information Technology (DIETI), Universita' degli Studi di Napoli Federico II, Naples, Italy.
Augmented Reality for Health Monitoring Laboratory (ARHeMLab), Italy.
Int J Neural Syst. 2021 Mar;31(3):2150003. doi: 10.1142/S0129065721500039. Epub 2020 Dec 22.
A method for selecting electroencephalographic (EEG) signals in motor imagery-based brain-computer interfaces (MI-BCI) is proposed for enhancing the online interoperability and portability of BCI systems, as well as user comfort. The attempt is also to reduce variability and noise of MI-BCI, which could be affected by a large number of EEG channels. The relation between selected channels and MI-BCI performance is therefore analyzed. The proposed method is able to select acquisition channels common to all subjects, while achieving a performance compatible with the use of all the channels. Results are reported with reference to a standard benchmark dataset, the BCI competition IV dataset 2a. They prove that a performance compatible with the best state-of-the-art approaches can be achieved, while adopting a significantly smaller number of channels, both in two and in four tasks classification. In particular, classification accuracy is about 77-83% in binary classification with down to 6 EEG channels, and above 60% for the four-classes case when 10 channels are employed. This gives a contribution in optimizing the EEG measurement while developing non-invasive and wearable MI-based brain-computer interfaces.
提出了一种在基于运动想象的脑机接口（MI-BCI）中选择脑电图（EEG）信号的方法，以提高BCI系统的在线互操作性和便携性，以及用户舒适度。该方法还试图减少MI-BCI的变异性和噪声，因为MI-BCI可能会受到大量EEG通道的影响。因此，分析了所选通道与MI-BCI性能之间的关系。所提出的方法能够选择所有受试者共有的采集通道，同时实现与使用所有通道时相当的性能。参考标准基准数据集BCI竞赛IV数据集2a报告了结果。结果表明，在二分类和四分类任务中，采用显著更少数量的通道，也能实现与最佳现有技术方法相当的性能。特别是，在二分类中，使用低至6个EEG通道时，分类准确率约为77%-83%；在四分类情况下，使用10个通道时，准确率高于60%。这为开发非侵入性和可穿戴的基于运动想象的脑机接口时优化EEG测量做出了贡献。
