A wrapped time-frequency combined selection in the source domain. (March 2020) - Li, Ming-ai; Wang, Yi-fan; Zhu, Xiao-qing; Yang, Jin-fu
Highlights: A novel Wrapped Time-Frequency combined Selection in the Source Domain (WTFS-SD) is proposed for decoding the complex MI-tasks effectively. MI-EEG can be transformed into a mass of dipole source estimators by WMNE, which maps out latent time-frequency information in the source domain. The time-frequency analysis in the source domain can fully fit subject-based sensorimotor rhythm, yielding exact time-frequency selection. By using CSP based sub-band feature extraction and fusion, WTFS-SD generates at least 5.37% improvement of accuracy relative to other methods. A statistical analysis shows WTFS-SD achieves excellent consistency and significance with a superior mean Kappa score of 0.8627. Abstract: The selection of time segment and frequency band always play a vital role in the decoding of Motor Imagery Tasks (MI-tasks), especially for the feature extraction of MI-Electroencephalographic (MI-EEG). The excavation of valuable and discriminative feature information needs to be based on the reliable time-frequency analysis, which is the foremost precondition for feature engineering. However, relying on the high temporal resolution of MI-EEG, traditional feature extraction methods can only conduct the time-frequency analysis according to the superficial neurophysiological rhythm of EEG in the sensor domain. And more detailed time-frequency characteristics could hardly be embodied in a few channels of MI-EEG signals, which leads to a coarse selection of time-frequency interval and the resulted lower decoding effect. Therefore, a neurophysiology-based technique is needed for performing more exact time-frequency analysis. Based on the advanced EEG Source Imaging, a Wrapped Time-Frequency combined Selection in the Source Domain, which is denoted as WTFS-SD, is proposed for decoding the MI-tasks by applying Weighted Minimum Norm Estimate and CSP based sub-band feature extraction in this paper. Abundant comparative experiments are conducted on the BCI2000 system dataset with six subjects, and the results show that the proposed methods can select subject-specific optimal frequency band and TOI, which yields the highest average classification rate of 93.14% by 9-fold cross-validation at the same chance level as well as a superior mean kappa coefficient of 0.8627 across all subjects compared to other prevalent methods. This study will enhance the decoding of complex MI-tasks and be helpful for the development of intelligent BCI system.