Connected Autonomous Intelligent Systems Laboratory, Department of Computer and Network Engineering, College of IT (CIT), United Arab Emirates University (UAEU), Al Ain 15551, United Arab Emirates.
Sensors (Basel). 2023 May 25;23(11):5051. doi: 10.3390/s23115051.
The use of Riemannian geometry decoding algorithms in classifying electroencephalography-based motor-imagery brain-computer interfaces (BCIs) trials is relatively new and promises to outperform the current state-of-the-art methods by overcoming the noise and nonstationarity of electroencephalography signals. However, the related literature shows high classification accuracy on only relatively small BCI datasets. The aim of this paper is to provide a study of the performance of a novel implementation of the Riemannian geometry decoding algorithm using large BCI datasets. In this study, we apply several Riemannian geometry decoding algorithms on a large offline dataset using four adaptation strategies: baseline, rebias, supervised, and unsupervised. Each of these adaptation strategies is applied in motor execution and motor imagery for both scenarios 64 electrodes and 29 electrodes. The dataset is composed of four-class bilateral and unilateral motor imagery and motor execution of 109 subjects. We run several classification experiments and the results show that the best classification accuracy is obtained for the scenario where the baseline minimum distance to Riemannian mean has been used. The mean accuracy values up to 81.5% for motor execution, and up to 76.4% for motor imagery. The accurate classification of EEG trials helps to realize successful BCI applications that allow effective control of devices.
使用黎曼几何解码算法对基于脑电图的运动想象脑机接口 (BCI) 试验进行分类是相对较新的方法，它有望通过克服脑电图信号的噪声和非平稳性来超越当前的最先进方法。然而，相关文献仅在相对较小的 BCI 数据集上显示出较高的分类准确性。本文的目的是研究使用大型 BCI 数据集对新实现的黎曼几何解码算法的性能进行研究。在这项研究中，我们使用四种自适应策略（基线、重新调整、监督和无监督）在一个大型离线数据集上应用了几种黎曼几何解码算法：基线、重新调整、监督和无监督。这些自适应策略中的每一种都应用于运动执行和运动想象两种情况下的 64 个电极和 29 个电极。该数据集由 109 名受试者的四分类双侧和单侧运动想象和运动执行组成。我们进行了多次分类实验，结果表明，在使用基线最小黎曼均值距离的情况下，获得了最佳的分类准确性。运动执行的平均准确率高达 81.5%，运动想象的平均准确率高达 76.4%。对 EEG 试验的准确分类有助于实现成功的 BCI 应用，从而实现对设备的有效控制。
