一种鲁棒脑机接口应用的扩展计算机辅助诊断系统

Xiaojun Yu, Muhammad Zulkifal Aziz, Yiyan Hou, Haopeng Li, Jialin Lv, M. Jamil
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引用次数: 2

摘要

脑电图信号处理是从低信号中破译有意义信息以推动实际应用的关键步骤。本文研究了一种脑电信号处理模型,该模型利用三种脑电信号组合、六种特征提取方法、七种分类算法以及改进的经验傅里叶分解(IEFD)对运动图像(MI)脑电信号进行分析。IEFD的可行性在包含52个参与者的大型GigaDB数据集以及BCI竞赛III数据集IVa和IVb上得到进一步验证。结果表明,结合18电极组合、welch PSD特征和多层感知器分类器,IEFD机制具有鲁棒性,对IVa和IVb受试者的分类准确率分别达到99.52%、99.35%、98.89%、99.52%、100%和93.19%。此外,GigaDB数据集的平均分类准确率、灵敏度、特异性和fl评分分别为83.84%、83.71%、83.98%和83.80%。结果表明,与以往的研究相比,该模型的平均分类准确率提高了16.6%。这些有希望的发现表明,所提出的IEFD方法对MI EEG信号分类具有鲁棒性和适应性,独立于多个数据集的受试者间方差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Extended Computer Aided Diagnosis System for Robust BCI Applications
Electroencephalogram (EEG) signal processing is the pivotal procedure to decipher meaningful information from the lowkey signals to drive practical applications. This paper investigates an EEG signal processing model, which utilizes three EEG electrode combinations, six feature extraction methods, and seven classification algorithms together with an improved empirical Fourier decomposition (IEFD) for motor imagery (MI) EEG signal analysis. The feasibility of IEFD is further validated on a large GigaDB dataset with 52 participants along with the BCI competition III datasets IVa and IVb. Results reveal that IEFD mechanism yields robust classification outcomes when coupled with 18 electrodes combination, welch PSD features, and multilayer perceptron classifier, and the best classification accuracy of 99.52%, 99.35%, 98.89%, 99.52%, 100%, and 93.19% is achieved for dataset IVa and IVb subjects, respectively. Moreover, the GigaDB dataset yields an average classification accuracy, sensitivity, specificity, and fl-score of 83.84%, 83.71%, 83.98%, and 83.80% accordingly. Results compared with previous studies conclude that the proposed model improves the average classification accuracy by 16.6%. Such promising findings conclude that the proposed IEFD method is robust and adaptive for MI EEG signals classification, independent of subject-to-subject variance for multiple datasets.
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