The development of fast and robust brain–computer interface (BCI) systems requires non-complex and efficient computational tools. The modern procedures adopted for this purpose are complex which limits their use in practical applications. In this study, for the first time, and to the best of our knowledge, a successive decomposition index (SDI)-based feature extraction approach is utilized for the classification of motor and mental imagery electroencephalography (EEG) tasks. First of all, the public datasets IVa, IVb, and V from BCI competition III were denoised using multiscale principal analysis (MSPCA), and then a SDI feature was calculated corresponding to each trial of the data. Finally, six benchmark machine learning and neural network classifiers were used to evaluate the performance of the proposed method. All the experiments were performed for motor and mental imagery datasets in binary and multiclass applications using a 10-fold cross-validation method. Furthermore, computerized automatic detection of motor and mental imagery using SDI (CADMMI-SDI) is developed to describe the proposed approach practically. The experimental results suggest that the highest classification accuracy of 97.46% (Dataset IVa), 99.52% (Dataset IVb), and 99.33% (Dataset V) was obtained using feedforward neural network classifier. Moreover, a series of experiments, namely, statistical analysis, channels variation, classifier parameters variation, processed and unprocessed data, and computational complexity, were performed and it was concluded that SDI is robust for noise, and a non-complex and efficient biomarker for the development of fast and accurate motor and mental imagery BCI systems.

中文翻译：

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使用连续分解索引识别两类和多类主题相关任务中的运动和心理意象脑电图。


快速、鲁棒的脑机接口（BCI）系统的开发需要不复杂且高效的计算工具。为此目的采用的现代程序很复杂，这限制了它们在实际应用中的使用。据我们所知，在这项研究中，首次采用基于连续分解指数（SDI）的特征提取方法来对运动和心理意象脑电图（EEG）任务进行分类。首先，使用多尺度主分析（MSPCA）对BCI竞赛III的公共数据集IVa、IVb和V进行去噪，然后计算对应于数据的每次试验的SDI特征。最后，使用六个基准机器学习和神经网络分类器来评估所提出方法的性能。所有实验都是使用 10 倍交叉验证方法在二进制和多类应用程序中针对运动和心理意象数据集进行的。此外，还开发了使用 SDI (CADMMI-SDI) 的计算机自动检测运动和心理意象的方法，以实际描述所提出的方法。实验结果表明，使用前馈神经网络分类器获得了最高的分类准确率，分别为 97.46%（数据集 IVa）、99.52%（数据集 IVb）和 99.33%（数据集 V）。此外，还进行了一系列实验，即统计分析、通道变化、分类器参数变化、处理和未处理的数据以及计算复杂性，得出的结论是SDI对噪声具有鲁棒性，并且是一种不复杂且有效的生物标记物。快速准确的运动和心理意象 BCI 系统的开发。