Study on Performance Metrics for Consideration of Efficiency of the Ocular Artifact Removal Algorithms for EEG Signals

A major challenge related to the removal of Ocular Artifacts (OA) from Electroencephalogram (EEG) signals is the lack of a consensus about one superior standard OA removal algorithm among all, due to unavailability of both natural artifactfree EEG signal and a standard performance evaluation criterion. The present paper discusses the various validated performance criteria often used in research papers for considering the efficiency of the OA removal algorithms. These metrics can be measured in the MATLAB software. In addition to natural EEG signals, artificial or simulated signals are also considered in this study. Some of these performance criteria are commonly used for both real and simulated signals, while others are appropriate only for one of them specifically. However, evaluation of a simulated signal can be easier than a real one because of the availability of artifact-free EEG signals in a simulation study, evaluation using a real signal is more trustworthy than simulated one. Hence, for more reliable and precise efficiency considering of an OA removal algorithm, employing both signals, is recommended. Further, because of the non-stationary nature of real EEG signals, the comparison of an algorithm with others, via implementing them on the same signals, will be meaningful and applicable. The present work will help researchers in recording the efficiency of their algorithms, as well as comparing the performance of their methods with others for reaching a consensus in OA removal.