Graduate Institute of Mechatronic Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Department of Mechanical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Sensors (Basel). 2017 Jul 3;17(7):1557. doi: 10.3390/s17071557.
Motor imagery is based on the volitional modulation of sensorimotor rhythms (SMRs); however, the sensorimotor processes in patients with amyotrophic lateral sclerosis (ALS) are impaired, leading to degenerated motor imagery ability. Thus, motor imagery classification in ALS patients has been considered challenging in the brain-computer interface (BCI) community. In this study, we address this critical issue by introducing the Grassberger-Procaccia and Higuchi's methods to estimate the fractal dimensions (GPFD and HFD, respectively) of the electroencephalography (EEG) signals from ALS patients. Moreover, a Fisher's criterion-based channel selection strategy is proposed to automatically determine the best patient-dependent channel configuration from 30 EEG recording sites. An EEG data collection paradigm is designed to collect the EEG signal of resting state and the imagination of three movements, including right hand grasping (RH), left hand grasping (LH), and left foot stepping (LF). Five late-stage ALS patients without receiving any SMR training participated in this study. Experimental results show that the proposed GPFD feature is not only superior to the previously-used SMR features (mu and beta band powers of EEG from sensorimotor cortex) but also better than HFD. The accuracies achieved by the SMR features are not satisfactory (all lower than 80%) in all binary classification tasks, including RH imagery vs. resting, LH imagery vs. resting, and LF imagery vs. resting. For the discrimination between RH imagery and resting, the average accuracies of GPFD in 30-channel (without channel selection) and top-five-channel configurations are 95.25% and 93.50%, respectively. When using only one channel (the best channel among the 30), a high accuracy of 91.00% can still be achieved by the GPFD feature and a linear discriminant analysis (LDA) classifier. The results also demonstrate that the proposed Fisher's criterion-based channel selection is capable of removing a large amount of redundant and noisy EEG channels. The proposed GPFD feature extraction combined with the channel selection strategy can be used as the basis for further developing high-accuracy and high-usability motor imagery BCI systems from which the patients with ALS can really benefit.
运动想象基于运动感觉节律（SMR）的随意调节；然而，肌萎缩侧索硬化症（ALS）患者的运动感觉过程受损，导致运动想象能力下降。因此，脑机接口（BCI）社区认为 ALS 患者的运动想象分类具有挑战性。在这项研究中，我们通过引入 Grassberger-Procaccia 和 Higuchi 的方法来估计 ALS 患者脑电图（EEG）信号的分形维数（分别为 GPFD 和 HFD）来解决这个关键问题。此外，还提出了一种基于 Fisher 准则的通道选择策略，以自动确定来自 30 个 EEG 记录部位的最佳患者相关通道配置。设计了一个 EEG 数据采集范式来采集静息状态和三种运动想象的 EEG 信号，包括右手抓握（RH）、左手抓握（LH）和左脚踩踏（LF）。五名未接受任何 SMR 训练的晚期 ALS 患者参加了这项研究。实验结果表明，所提出的 GPFD 特征不仅优于以前使用的 SMR 特征（感觉运动皮层 EEG 的 mu 和 beta 波段功率），而且优于 HFD。在所有二进制分类任务中，包括 RH 想象与静息、LH 想象与静息、LF 想象与静息，SMR 特征的准确率都不令人满意（均低于 80%）。对于 RH 想象与静息的区分，在 30 通道（无通道选择）和前五通道配置下，GPFD 的平均准确率分别为 95.25%和 93.50%。当仅使用一个通道（30 个通道中的最佳通道）时，GPFD 特征和线性判别分析（LDA）分类器仍可达到 91.00%的高精度。结果还表明，所提出的基于 Fisher 准则的通道选择能够去除大量冗余和噪声 EEG 通道。所提出的 GPFD 特征提取与通道选择策略相结合，可作为进一步开发高精度、高可用性运动想象 BCI 系统的基础，使 ALS 患者从中真正受益。
