A flexible analytic wavelet transform based approach for motor-imagery tasks classification in BCI applications
A flexible analytic wavelet transform based approach for motor-imagery tasks classification in BCI applications
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DOI：
10.1016/j.cmpb.2020.105325
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发表时间：
2020-04-01
影响因子：
6.1
通讯作者：
Siuly, Siuly
中科院分区：
文献类型：
--
作者：
Chaudhary, Shalu;Taran, Sachin;Siuly, Siuly
背景和目的：基于运动想象的脑机接口是一种新兴的支持系统，它可以帮助残疾人在没有任何外界帮助的情况下与真实的世界进行交流。它是用户和计算机之间的另一种通信渠道。脑电图（EEG）记录证明是一个合适的选择，在脑机接口系统中成像MI任务，因为它提供了一个非侵入性的方式来完成任务。BCI系统的可靠性依赖于不同的MI Tasks.Methods的评估效率：本工作提出了一种新的方法，不同的MI任务的分类的基础上，使用灵活的解析小波变换（FAWT）技术的EEG信号。FAWT将脑电信号分解成子带，并从子带中提取基于时间矩的特征。特征归一化被应用于最小化分类器的偏差性质。基于FAWT的特征被用作多个分类器的输入。结果：基于子空间k近邻（kNN）分类器的子带特征在多个分类器上进行了测试，得到了最佳性能参数。对于第四SB获得的参数的最佳结果为准确性99.33%，灵敏度99%，特异性99.6%，F1-Score 0.9925和Kappa值0.9865。其他子带也取得了显着的结果，使用子空间KNN classifier.Conclusions：所提出的工作探讨了实用的FAWT为基础的功能的分类RH和RF MI任务EEG信号。建议的工作突出了多个分类器的分类MI任务的有效性。所提出的方法表现出更好的性能相比，最先进的方法。因此，有可能实现用于控制轮椅，机械臂等的BCI系统。（C）2020 Elsevier B. V.保留所有权利。
Background and Objective: Motor Imagery (MI) based Brain-Computer-Interface (BCI) is a rising support system that can assist disabled people to communicate with the real world, without any external help. It serves as an alternative communication channel between the user and computer. Electroencephalogram (EEG) recordings prove to be an appropriate choice for imaging MI tasks in a BCI system as it provides a non-invasive way for completing the task. The reliability of a BCI system confides on the efficiency of the assessment of different MI tasks.Methods: The present work proposes a new approach for the classification of distinct MI tasks based on EEG signals using the flexible analytic wavelet transform (FAWT) technique. The FAWT decomposes the EEG signal into sub-bands and temporal moment-based features are extracted from the sub-bands. Feature normalization is applied to minimize the bias nature of classifier. The FAWT-based features are utilized as inputs to multiple classifiers. Ensemble learning method based Subspace k-Nearest Neighbour (kNN) classifier is established as the best and robust classifier for the distinction of the right hand (RH) and right foot (RF) MI tasks.Results: The sub-band (SB) wise features are tested on multiple classifiers and best performance parameters are obtained using the ensemble method based subspace kNN classifier. The best results of parameters are obtained for fourth SB as accuracy 99.33%, sensitivity 99%, specificity 99.6%, Fl-Score 0.9925, and kappa value 0.9865. The other sub-bands are also attained significant results using subspace KNN classifier.Conclusions: The proposed work explores the utility of FAWT based features for the classification of RH and RF MI tasks EEG signals. The suggested work highlights the effectiveness of multiple classifiers for classification MI-tasks. The proposed method shows better performance in comparison to state-of-arts methods. Thus, the potential to implement a BCI system for controlling wheelchairs, robotic arms, etc. (C) 2020 Elsevier B.V. All rights reserved.