Motor imagery (MI) classification is important as the emerging research interest of brain computer interface (BCI) due to its potential about real-world application. Advancing manipulation and control technology of external devices such as robotics, the need of MI for complex and human-like movements is growing. The two most important procedures that influence the performance of MI-BCI are feature extraction and classification. Although there have been recent studies on feature extraction for complex, there is no consensus on the classifier suitable for complex MI. This study aimed to identify the best classifier for complex MI decoding.Electroencephalography (EEG) recordings measured during complex MI, which are hand grasping, spreading, pronation and supination, were used for binary (grasp vs. twist) and quaternary classification. Time domain parameter, which have shown suitability for complex movement decoding in previous works, was used as the EEG feature. Four types of ten machine learning classifiers, which have been applied to MI-BCI, were compared.Shrinkage regularized linear discriminant analysis (SRLDA) exhibited the best classification accuracy in both binary (92.8%) and quaternary (55.2%). In the case of training and testing time, a small amount of time for real-time analysis were needed, except random forest and logistic regression.This study showed that SRLDA is an appropriate classifier for complex MI classification, due to its ability to handle stationary and high dimensionality feature, TDP. The findings suggest that complex MI-BCI could gain more benefit from applying linear and shrinkage regularized model (i.e., SRLDA).