During the acquisition of electroencephalographic (EEG) signals,data may be missing or corrupted by noise and artifacts.To reconstruct the incomplete data,EEG signals are firstly converted into a three-order tensor (multi-dimensional data) of shape time × channel × trial.Then,the missing data can be efficiently recovered by applying a tensor completion method (TCM).However,there is not a unique way to organize channels and trials in a tensor,and different numbers of channels are available depending on the EEG setting used,which may affect the quality of the tensor completion results.The main goal of this paper is to evaluate the robustness of EEG completion methods with several designed parameters such as the ordering of channels and trials,the number of channels,and the amount of missing data.In this work,the results of completing missing data by several TCMs were compared.To emulate different scenarios of missing data,three different patterns of missing data were designed.Firstly,the amount of missing data on completion effects was analyzed,including the time lengths of missing data and the number of channels or trials affected by missing data.Secondly,the numerical stability of the completion methods was analyzed by shuffling the indices along channels or trials in the EEG data tensor.Finally,the way that the number of electrodes of EEG tensors influences completion effects was assessed by changing the number of channels.Among all the applied TCMs,the simultaneous tensor decomposition and completion (STDC) method achieves the best performance in providing stable results when the amount of missing data or the electrode number of EEG tensors is changed.In other words,STDC proves to be an excellent choice of TCM,since permutations of trials or channels have almost no influence on the complete results.The STDC method can efficiently complete the missing EEG signals.The designed simulations can be regarded as a procedure to validate whether or not a completion method is useful enough to complete EEG signals.