Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin 300072, PR China.
Clin Neurophysiol. 2012 Sep;123(9):1779-88. doi: 10.1016/j.clinph.2012.02.071. Epub 2012 Mar 28.
The main goal of this study was to develop a novel spatial filtering method for better extracting the feature information underlying the event-related de-synchronisation and synchronisation (ERD/ERS) during complex motor imagery of lower limb action.
The algorithm used a wavelet packet-based independent component analysis (WPICA) method to extract the ERD/ERS patterns in different frequency bands. Time-frequency decomposition in the wavelet packet domain was designed to avoid the statistical correlation between different electroencephalographic (EEG) rhythms. The subband-specific principal components were extracted after independent component analysis and projected back to the time-frequency domain of corresponding electrodes for better fitting the varying EEG spatial distributions.
The present method was tested with the EEG data from 10 human subjects performing three complex mental tasks (i.e., imagery standing up, imagery left/right foot movement combined with homolateral hand movement). A classification rate of about 80% was achieved using the WPICA-based technique, which is better than the traditional ICA method with the rate of 72.30% and the non-spatial filtering condition of 68.34%.
We developed a novel spatial filtering method based on WPICA to extract the ERD/ERS patterns in different frequency bands. The overall performance of this algorithm was better than that of the conventional methods.
The current method promised to provide an effective way for ERD/ERS patterns recognition and thus could improve the pattern classification performance of complex mental tasks from scalp EEGs.
本研究的主要目的是开发一种新的空间滤波方法，以更好地提取下肢动作复杂运动想象过程中事件相关去同步和同步（ERD/ERS）的特征信息。
该算法使用基于小波包独立成分分析（WPICA）的方法提取不同频带中的 ERD/ERS 模式。在小波包域中进行时频分解，以避免不同脑电图（EEG）节律之间的统计相关性。在独立成分分析后提取子带特定的主成分，并将其投影回相应电极的时频域，以更好地拟合不断变化的 EEG 空间分布。
本方法使用 10 名人类受试者执行三种复杂心理任务（即想象站立、想象左右脚运动结合同侧手运动）的 EEG 数据进行了测试。基于 WPICA 的技术可实现约 80%的分类率，优于传统 ICA 方法的 72.30%和非空间滤波条件的 68.34%。
我们开发了一种基于 WPICA 的新的空间滤波方法，以提取不同频带中的 ERD/ERS 模式。该算法的整体性能优于传统方法。
该方法有望为 ERD/ERS 模式识别提供一种有效途径，从而可以提高头皮 EEG 复杂心理任务的模式分类性能。
