Missing values imputation using similarity matching method for brainprint authentication

Abstract

This paper proposes a similarity matching imputation method to deal with the missing values in electroencephalogram (EEG) signals. EEG signals with rather high amplitude can be considered as noise, normally they will be removed. The occurrence of missing values after this artefact rejection process increases the complexity of computational modelling due to incomplete data input for model training. The fundamental concept of the proposed similarity matching imputation method is founded on the assumption that similar stimulation on a particular subject will acquire comparable EEG signals response over the related EEG channels. Hence, we replaced the missing values using the highest similarity amplitude measure across different trials in this study. Next, wavelet phase stability (WPS) was used to evaluate the performance of the proposed method since WPS portrays better signals information as compared to amplitude measure in this situation. The statistical paired sample t-test was used to validate the performance of the proposed similarity matching imputation method and the preceding mean substitute imputation method. The lower the value of mean difference indicates the better approximation of imputation data towards its original form. The proposed method is able to treat 9.75% more missing value trials, with significantly better imputation value, than the mean substitution method. Continuity of the current study will be focusing on evaluating the robustness of the proposed method in dealing with different rate of missing data.