Samaneh Valipour ¹, G. R. Kulkarni ², A. D. Shaligram ²

Affiliations
1 Department of Electronic Science, Pune University, Pune, Maharashtra, IN
2 Department of Physics, Pune University, Pune, Maharashtra, IN

Abstract

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.

Keywords

EEG, EOG, Ocular Artifact Removal, Performance Metrics

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samaneh valipour ¹, M. Ziaratban ², A. D. Shaligram ³

Affiliations
1 Department of Electronic Science, Pune University, Pune – 411007, Maharashtra, IN
2 Department of Electronic Engineering, Golestan University, Gorgan, Golestan, IN
3 Department of Electronic Science, Pune University, Pune – 411007, Maharashtra, IR

Abstract

One of the most popular techniques in the Ocular Artifact (OA) removal from Electroencephalogram (EEG) signals is Adaptive Filter (AF) with Recursive Least-Squares (RLS) algorithm. The low convergence rate, good tracking, low miss-adjustment, and good stability are expected capabilities of this filter, which depend highly to value of forgetting factor (0<λ<1). As, with a λ very close to one, stability of the filter is increased due to low misadjustment, but its tracking capabilities is reduced and consequently the OA will remained in the EEG signal even after filtering. To preserve stability of the AF-RLS and improve its tracking, a new configuration of two AF-RLS in the wavelet domain is proposed for applying on approximation and detail coefficients. The proposed algorithm is compared with two older AF-RLS in the time domain (AF-T) and Wavelet based AF using approximation coefficients (WAF-A). Simulation results demonstrate the effectiveness of the proposed algorithm in term of the OA removal and preserving background EEG signals. Also some performance criteria such as visual comparison in the time domain, correlation coefficient and artifact to signal ratio are employed as evidence of this achievement. The proposed algorithm can be implemented in the real-time applications due to fast processing speed.

Keywords

Adaptive Filter, EEG, Ocular Artifact, RLS, Wavelet Transform

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