基于自适应无参经验小波变换和选择集成分类模型的运动想象

运动想象模式识别率的提高对脑机接口(BCI)技术的应用具有重要意义,本文采用自适应无参经验小波变换(APEWT)和选择集成分类模型相结合的方法提高脑电(EEG)信号的分类识别准确率.首先,通过APEWT将EEG信号分解成不同的模态;然后,使用最优模态重构后的信号计算其能量谱(ES)特征,使用最优模态分量计算其边际谱(MS)特征;最后,将不同时间段的ES特征和不同频段的MS特征输入到构建的选择集成分类模型中,从而得到其分类结果,并将该方法与其他4种组合方法进行比较.实验结果表明,本文方法具有较好分类准确率和实时性,其平均分类正确率高于其他4种方法,同时较近期使用相同数据的文献也有优势.本文为在线运动想象类BCI的应用提供了新的方法和思路.

Motor imagery based on adaptive parameterless empirical wavelet transform and selective integrated classification

Improving recognition rate of motor imagery (MI)-related electroencephalography (EEG) is of great importance for numerous brain computer interface (BCI) applications. However, the performance of a typical BCI system greatly relies on the effectiveness of the extracted features from raw EEG signals and the ability of the classifier to correctly identify different MI patterns. Therefore, in this paper, a new recognition method based on adaptive parameterless empirical wavelet transform (APEWT) and selective integrated classification model is proposed to enhance the classification accuracy of MI-related EEG signal. First, the APEWT is used to decompose EEG signals from different MI patterns into several intrinsic mode functions (IMFs), each of which contains different rhythm information over different frequency bands. Then several related modes are optimally selected based on the correlation coefficients calculated between each IMF component and the original signal to reconstruct EEG signals. Next, in order to further extract useful pattern information from both the time domain and frequency domain, the energy spectrum features of multiple time segments from the reconstructed signals and marginal spectrum features of different frequency bands corresponding to those selected modes are investigated, respectively. Finally, the extracted multiple features from time domain and frequency domain are input into the selective integrated classification model to build an MI recognition system. The selective integrated classification model consists of several extreme learning machines (ELMs) as the basic classifiers, different weights are assigned, respectively, to ELM basic classifiers based on their corresponding classification performances, and several basic ELM classifiers with good performances are selected to construct the final integrated model. The proposed method is evaluated on two public datasets, including BCI Competition Ⅱ dataset Ⅲ and BCI Competition IV dataset 2 b, and is compared with four different combination methods where different features in time domain or frequency domain in the feature extraction stage and different ELMs based classification models are considered. Experimental results demonstrate that the proposed method outperformed four combination methods and the existing methods recently reported in the literature using the same datasets in terms of classification accuracy and area under the ROC curve receiver operating characteristic metric. Specifically, our proposed method achieves the highest average classification accuracy (89.95%) in the compared methods, which indicates its better classification performance and generalization capability. In addition, the proposed method exhibits high computational efficiency, thus providing a new solution for online recognition of MI-related BCI and having the potential to be embedded in a practical system for controlling an external device.