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.
Highlights
Brainprint authentication is catching attention recently because of their high time resolution, portability and relatively low cost [1]
The experimental results were validated from four different perspectives, i.e. (1) the comparison of amplitude between the original data and the imputed data using the proposed similarity matching method; (2) the comparison of amplitude between the original data and the imputed data using the mean substitution method; (3) the comparison of wavelet phase stability (WPS) between the original data and the imputed data using the similarity matching method; (4) the comparison of WPS between the original data and the imputed data using the mean substitution method
Apart from the evaluation in amplitude, we have evaluated the quality of the imputed data using wavelet phase stability (WPS)
Summary
Brainprint authentication is catching attention recently because of their high time resolution, portability and relatively low cost [1]. Many decent non-clinical grade Electroencephalogram (EEG) acquisition devices have been introduced to the consumer market. This has greatly helped in promoting the EEG research since the data acquisition process is getting simpler and affordable. EEG is a popular non-invasive method which record the electrical activities of the brain on the scalp. EEG is outstanding than the current biometric modalities because EEG signals are hidden in our brain and non-observable physically. Other biometric modalities, such as fingerprint or face, are obtainable physical sensors from the body surface [3]. In order to tackle this issue, a similarity matching imputation method is proposed to deal with the missing values caused by artefact rejection.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
