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Channel Selection for Optimal EEG Measurement in Motor Imagery-Based Brain-Computer Interfaces
International Journal of Neural Systems ( IF 8 ) Pub Date : 2020-11-20 , DOI: 10.1142/s0129065721500039
Pasquale Arpaia 1, 2 , Francesco Donnarumma 2, 3 , Antonio Esposito 2, 4 , Marco Parvis 2, 4
Affiliation  

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.

中文翻译:

基于运动图像的脑机接口中最佳 EEG 测量的通道选择

为提高脑机接口系统的在线互操作性和便携性以及用户舒适度,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。尝试还减少可能受大量 EEG 通道影响的 MI-BCI 的可变性和噪声。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,同时实现与所有通道的使用兼容的性能。参考标准基准数据集 BCI 竞赛 IV 数据集 2a 报告结果。他们证明,可以实现与最先进的方法兼容的性能,同时采用显着较少数量的通道,在两个和四个任务分类中。特别是,在低至 6 个 EEG 通道的二元分类中,分类准确率约为 77-83%,而在使用 10 个通道的四类情况下,分类准确率在 60% 以上。这有助于优化脑电图测量,同时开发非侵入性和可穿戴的基于 MI 的脑机接口。
更新日期:2020-11-20
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