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Abstract：
This paper presents an advanced approach for EEG artifact removal and motor imagery classification using a combination of Four Class Iterative Filtering and Filter Bank Common Spatial Pattern Algorithm with a Modified Deep Neural Network (DNN) classifier. The research aims to enhance the accuracy and reliability of BCI systems by addressing the challenges posed by EEG artifacts and complex motor imagery tasks. The methodology begins by introducing FCIF, a novel technique for ocular artifact removal, utilizing iterative filtering and filter banks. FCIF\'s mathematical formulation allows for effective artifact mitigation, thereby improving the quality of EEG data. In tandem, the FC-FBCSP algorithm is introduced, extending the Filter Bank Common Spatial Pattern approach to handle four-class motor imagery classification. The Modified DNN classifier enhances the discriminatory power of the FC-FBCSP features, optimizing the classification process. The paper showcases a comprehensive experimental setup, featuring the utilization of BCI Competition IV Dataset 2a & 2b. Detailed preprocessing steps, including filtering and feature extraction, are presented with mathematical rigor. Results demonstrate the remarkable artifact removal capabilities of FCIF and the classification prowess of FC-FBCSP combined with the Modified DNN classifier. Comparative analysis highlights the superiority of the proposed approach over baseline methods and the method achieves the mean accuracy of 98.575%.
摘要：
本文提出了一种先进的EEG伪影去除和运动图像分类方法，该方法结合了四类迭代滤波和滤波器组公共空间模式算法以及改进的深度神经网络（DNN）分类器。该研究旨在通过解决EEG伪影和复杂运动成像任务带来的挑战来提高BCI系统的准确性和可靠性。该方法首先引入FCIF，一种新颖的去除眼部伪影的技术，利用迭代滤波和滤波器组。FCIF的数学公式允许有效的伪影缓解，从而提高脑电数据的质量。串联，介绍了FC-FBCSP算法,扩展滤波器组公共空间模式方法来处理四类运动图像分类。改进的DNN分类器增强了FC-FBCSP特征的辨别能力，优化分类过程。本文展示了一个全面的实验装置，以BCI竞赛IV数据集2a和2b的利用为特色。详细的预处理步骤，包括过滤和特征提取，以数学上的严谨性呈现。结果证明了FCIF的显着伪影去除能力以及FC-FBCSP与ModifiedDNN分类器结合的分类能力。对比分析强调了所提出的方法相对于基线方法的优越性，该方法达到了98.575％的平均精度。
