Detection of motor imagery of swallow EEG signals based on the dual-tree complex wavelet transform and adaptive model selection
Detection of motor imagery of swallow EEG signals based on the dual-tree complex wavelet transform and adaptive model selection
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DOI：
10.1088/1741-2560/11/3/035016
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发表时间：
2014-06-01
影响因子：
4
通讯作者：
Ang, Kai Keng
中科院分区：
文献类型：
--
作者：
Yang, Huijuan;Guan, Cuntai;Ang, Kai Keng
目标。手/臂运动表象的检测在中风康复中得到了广泛的研究。本文首先对吞咽运动表象(MI-Sw)和舌突运动表象(MI-TON)的检测进行了研究，试图为卒中后吞咽障碍的康复提供一种新的方法。然后研究从简单但相关的通道(如MI-TON)检测MI-SW，其动机是舌部运动和吞咽之间的激活模式相似，并且与吞咽相比，执行舌部运动时存在更少的运动伪像。接近。基于双树复小波变换的系数，提取新的特征，建立多训练模型，用于检测心电短波。通过自适应地选择训练模型以最大化类间距离与类内距离的比率，利用训练和评估数据的特征，提高了会话到会话的分类精度。主要结果。我们提出的方法对10名健康受试者的MI-Sw和MI-Ton的平均交叉验证(CV)准确率分别为70.89%和73.79%，显著优于现有方法的结果。此外，对于一个卒中患者，MI-Sw和MI-ton的平均CV准确率分别为66.40%和70.24%，证明了从空闲状态可以检测到MI-Sw和MI-ton。此外，使用MI-TON模型对10名健康受试者和1名中风患者进行会话到会话分类的平均准确率分别为72.08%和70%。意义重大。这些结果以及MI-Sw和MI-ton分类精度之间的主观强相关性证明了从MI-Ton模型中检测MI-Sw的可行性。
Objective. Detection of motor imagery of hand/arm has been extensively studied for stroke rehabilitation. This paper firstly investigates the detection of motor imagery of swallow (MI-SW) and motor imagery of tongue protrusion (MI-Ton) in an attempt to find a novel solution for post-stroke dysphagia rehabilitation. Detection of MI-SW from a simple yet relevant modality such as MI-Ton is then investigated, motivated by the similarity in activation patterns between tongue movements and swallowing and there being fewer movement artifacts in performing tongue movements compared to swallowing. Approach. Novel features were extracted based on the coefficients of the dual-tree complex wavelet transform to build multiple training models for detecting MI-SW. The session-to-session classification accuracy was boosted by adaptively selecting the training model to maximize the ratio of between-classes distances versus within-class distances, using features of training and evaluation data. Main results. Our proposed method yielded averaged cross-validation (CV) classification accuracies of 70.89% and 73.79% for MI-SW and MI-Ton for ten healthy subjects, which are significantly better than the results from existing methods. In addition, averaged CV accuracies of 66.40% and 70.24% for MI-SW and MI-Ton were obtained for one stroke patient, demonstrating the detectability of MI-SW and MI-Ton from the idle state. Furthermore, averaged session-to-session classification accuracies of 72.08% and 70% were achieved for ten healthy subjects and one stroke patient using the MI-Ton model. Significance. These results and the subjectwise strong correlations in classification accuracies between MI-SW and MI-Ton demonstrated the feasibility of detecting MI-SW from MI-Ton models.