Mahmoudi Mahmoud, Shamsi Mousa
Faculty of Biomedical Engineering, Sahand University of Technology, Sahand New Town, Tabriz, Iran.
Australas Phys Eng Sci Med. 2018 Dec;41(4):957-972. doi: 10.1007/s13246-018-0691-2. Epub 2018 Oct 18.
The electroencephalogram signals are used to distinguish different motor imagery tasks in brain-computer interfaces. In most studies, in order to classify the EEG signals recorded in a cue-guided BCI paradigm, time segments for feature extraction after the onset of the visual cue were selected manually. In addition, in these studies the authors have selected a single identical time segment for different subjects. The present study emphasized on the inter-individual variability and difference between different motor imagery tasks as the potential source of erroneous results and used mutual information and the subject specific time interval to overcome this problem. More specifically, a new method was proposed to automatically find the best subject specific time intervals for the classification of four-class motor imagery tasks by using MI between the BCI input and output. Moreover, the signal-to-noise ratio was used to calculate the MI values, while the MI values were used as feature selection criteria to select the discriminative features. The time segments and the best discriminative features were found by using training data and used to assess the evaluation data. Furthermore, the CSP algorithm was used to extract signal features. The dataset 2A of BCI competition IV used in this study consisted of four different motor imagery signals, which were obtained from nine different subjects. One Vs One decomposition scheme was used to deal with the multi-class nature of the problem. The MI values showed that the obtained time segments not only varied between different subjects but also varied between different classifiers of different pair of classes. Finally, the results suggested that the proposed method was efficient in classifying multi-class motor imagery signals as compared to other classification strategies proposed by the other studies.
脑电图信号用于脑机接口中区分不同的运动想象任务。在大多数研究中，为了对在提示引导的脑机接口范式中记录的脑电图信号进行分类，视觉提示出现后用于特征提取的时间段是手动选择的。此外，在这些研究中，作者为不同受试者选择了单个相同的时间段。本研究强调个体间的变异性以及不同运动想象任务之间的差异是错误结果的潜在来源，并使用互信息和受试者特定的时间间隔来克服这个问题。更具体地说，提出了一种新方法，通过使用脑机接口输入和输出之间的互信息，自动找到用于四类运动想象任务分类的最佳受试者特定时间间隔。此外，使用信噪比来计算互信息值，而互信息值用作特征选择标准来选择有区分力的特征。通过使用训练数据找到时间段和最佳区分特征，并用于评估测试数据。此外，使用共空间模式（CSP）算法来提取信号特征。本研究中使用的脑机接口竞赛IV的2A数据集由四个不同的运动想象信号组成，这些信号来自九个不同的受试者。采用一对一分解方案来处理问题的多类性质。互信息值表明，获得的时间段不仅在不同受试者之间有所不同，而且在不同类别对的不同分类器之间也有所不同。最后，结果表明，与其他研究提出的其他分类策略相比，所提出的方法在对多类运动想象信号进行分类方面是有效的。
