Uncorrelated Multiway Discriminant Analysis for Motor Imagery EEG Classification

Uncorrelated Multiway Discriminant Analysis for Motor Imagery EEG Classification
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运动想象脑电图分类的不相关多路判别分析

DOI:
10.1142/s0129065715500136
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发表时间:
2015-05
影响因子:
8
通讯作者:
Zhang, Liqing
Zhang, Liqing
中科院分区:
计算机科学2区
文献类型:
--
作者:
Liu, Ye;Zhao, Qibin;Zhang, Liqing

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基于运动想象的脑机接口(BCI)训练已被证明是人脑与外部设备之间的有效通信系统。基于BCI的系统的一个实际问题是如何从模糊的头皮脑电图(EEG)中正确有效地识别和提取特定于主题的特征,并将这些特征转化为设备命令以控制外部设备。在实际的基于BCI的应用中,我们通常预先定义与大脑活动相关的频段和通道配置。然而,在实际应用中,由于不同主体之间的个体差异,稳定的配置通常会失去效果。在这项研究中,提出了一种基于张量的鲁棒方法,用于从张量表示的脑电图数据中进行多路判别子空间提取,该方法在运动想象脑电图分类中表现良好,无需先验的神经生理学知识(如通道配置和活动频带)。空间-频谱-时间域中的运动想象脑电图模式是直接从多维脑电图检测到的,这可以提供对潜在皮层活动模式的见解。我们对著名的 BCI 竞赛 III 的基准数据集以及健康受试者和中风患者的自我获取数据进行了广泛的实验比较。实验结果表明所提出的方法比当代方法具有优越的性能。
Motor imagery-based brain-computer interfaces (BCIs) training has been proved to be an effective communication system between human brain and external devices. A practical problem in BCI-based systems is how to correctly and efficiently identify and extract subject-specific features from the blurred scalp electroencephalography (EEG) and translate those features into device commands in order to control external devices. In real BCI-based applications, we usually define frequency bands and channels configuration that related to brain activities beforehand. However, a steady configuration usually loses effects due to individual variability among different subjects in practical applications. In this study, a robust tensor-based method is proposed for a multiway discriminative subspace extraction from tensor-represented EEG data, which performs well in motor imagery EEG classification without the prior neurophysiologic knowledge like channels configuration and active frequency bands. Motor imagery EEG patterns in spatial-spectral-temporal domain are detected directly from the multidimensional EEG, which may provide insights to the underlying cortical activity patterns. Extensive experiment comparisons have been performed on a benchmark dataset from the famous BCI competition III as well as self-acquired data from healthy subjects and stroke patients. The experimental results demonstrate the superior performance of the proposed method over the contemporary methods.
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