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Enhancing LDA-based discrimination of left and right hand motor imagery: Outperforming the winner of BCI Competition II

机译：增强基于LDA的左右手运动图像识别：优于BCI竞赛第二名

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Due to the potential applications of Brain-Computer Interfaces (BCI), like producing rehabilitation systems for disabled people, many researches have been aimed at minimizing the error of BCI systems. In this paper, we used left and right hand motor imagery EEG data provided by Graz University of Technology for the BCI Competition II. We attempted to achieve a better misclassification rate while selecting less features compared with various former reported researches on this dataset. We used linear discriminant analysis (LDA) as the classifier due to its low computational cost and previously reported promising results. Furthermore, we investigated what features have major impacts on local or global minimization of the misclassification rate. Also, we briefly assessed the effect of changing window length on the misclassification rate. In this paper first, a set of various statistical, spectral, wavelet-based, connectivity, and chaotic features was extracted from EEG data. Subsequently, an LDA-based wrapper Sequential Forward Selection (SFS) scheme was used for selecting optimum subset of features for each data window. Finally, data windows were classified by LDA. We achieved less misclassification rate using less features compared with previous LDA-based researches and the winner of BCI competition II on the same dataset. Also, the absolute mean of the third-level wavelet detail coefficients (related to μ-band) and the skewness were the two features that together yielded the best local discrimination results.
机译：由于脑机接口（BCI）的潜在应用，例如为残疾人士生产康复系统，因此许多研究旨在使BCI系统的错误最小化。在本文中，我们使用了格拉茨科技大学为BCI竞赛II提供的左右手运动图像EEG数据。与以前在该数据集上进行的各种研究相比，我们试图在选择较少的特征的同时实现更好的误分类率。我们使用线性判别分析（LDA）作为分类器，这是因为其计算成本低，并且先前报告的结果令人鼓舞。此外，我们调查了哪些功能对误分类率的本地或全球最小化有重大影响。此外，我们简要评估了更改窗口长度对误分类率的影响。首先，本文从EEG数据中提取了一组各种统计，光谱，基于小波，连通性和混沌的特征。随后，基于LDA的包装程序顺序前向选择（SFS）方案用于为每个数据窗口选择特征的最佳子集。最后，数据窗口由LDA分类。与以前的基于LDA的研究以及BCI竞赛II的获胜者相比，我们在同一数据集上使用较少的功能实现了较少的误分类率。同样，第三级小波细节系数的绝对平均值（与μ带有关）和偏度是一起产生最佳局部判别结果的两个特征。

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