University of Rijeka, Faculty of Engineering, Vukovarska 58, HR-51000 Rijeka, Croatia.
Center for Artificial Intelligence and Cybersecurity, University of Rijeka, R. Matejcic 2, HR-51000 Rijeka, Croatia.
Sensors (Basel). 2023 May 25;23(11):5064. doi: 10.3390/s23115064.
Motor imagery (MI) is a technique of imagining the performance of a motor task without actually using the muscles. When employed in a brain-computer interface (BCI) supported by electroencephalographic (EEG) sensors, it can be used as a successful method of human-computer interaction. In this paper, the performance of six different classifiers, namely linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF), and three classifiers from the family of convolutional neural networks (CNN), is evaluated using EEG MI datasets. The study investigates the effectiveness of these classifiers on MI, guided by a static visual cue, dynamic visual guidance, and a combination of dynamic visual and vibrotactile (somatosensory) guidance. The effect of filtering passband during data preprocessing was also investigated. The results show that the ResNet-based CNN significantly outperforms the competing classifiers on both vibrotactile and visually guided data when detecting different directions of MI. Preprocessing the data using low-frequency signal features proves to be a better solution to achieve higher classification accuracy. It has also been shown that vibrotactile guidance has a significant impact on classification accuracy, with the associated improvement particularly evident for architecturally simpler classifiers. These findings have important implications for the development of EEG-based BCIs, as they provide valuable insight into the suitability of different classifiers for different contexts of use.
运动想象（MI）是一种无需实际使用肌肉即可想象运动任务执行的技术。当它被用于基于脑电图（EEG）传感器的脑机接口（BCI）中时，它可以作为一种成功的人机交互方法。在本文中，我们评估了六种不同分类器的性能，即线性判别分析（LDA）、支持向量机（SVM）、随机森林（RF）和卷积神经网络（CNN）家族中的三个分类器，使用 EEG MI 数据集。该研究通过静态视觉提示、动态视觉指导以及动态视觉和振动触觉（体感）指导的组合，探讨了这些分类器在 MI 中的有效性。还研究了数据预处理过程中滤波通带的影响。结果表明，基于 ResNet 的 CNN 在检测不同方向的 MI 时，在振动触觉和视觉引导数据上的表现明显优于竞争分类器。使用低频信号特征预处理数据被证明是实现更高分类准确性的更好解决方案。此外，振动触觉指导对分类准确性有重大影响，相关改进对于架构更简单的分类器尤为明显。这些发现对基于 EEG 的 BCI 的发展具有重要意义，因为它们为不同的使用场景下不同分类器的适用性提供了有价值的见解。
