Removal of Eye Blink Artifacts From EEG Signals Using Sparsity

Abstract

Neural activities recorded using electroencephalography (EEG) are mostly contaminated with eye blink (EB) artifact. This results in undesired activation of brain-computer interface (BCI) systems. Hence, removal of EB artifact is an important issue in EEG signal analysis. Of late, several artifact removal methods have been reported in the literature and they are based on independent component analysis (ICA), thresholding, wavelet transformation, etc. These methods are computationally expensive and result in information loss which makes them unsuitable for online BCI system development. To address the above problems, we have investigated sparsity-based EB artifact removal methods. Two sparsity-based techniques namely morphological component analysis (MCA) and K-SVD-based artifact removal method have been evaluated in our work. MCA-based algorithm exploits the morphological characteristics of EEG and EB using predefined Dirac and discrete cosine transform (DCT) dictionaries. Next, in K-SVD-based algorithm an overcomplete dictionary is learned from the EEG data itself and is designed to model EB characteristics. To substantiate the efficacy of the two algorithms, we have carried out our experiments with both synthetic and real EEG data. We observe that the K-SVD algorithm, which uses a learned dictionary, delivers superior performance for suppressing EB artifacts when compared to MCA technique. Finally, the results of both the techniques are compared with the recent state-of-the-art FORCe method. We demonstrate that the proposed sparsity-based algorithms perform equal to the state-of-the-art technique. It is shown that without using any computationally expensive algorithms, only with the use of over-complete dictionaries the proposed sparsity-based algorithms eliminate EB artifacts accurately from the EEG signals.

Automated eye blink artefact removal from EEG using support vector machine and autoencoder

“…SAR is used to measure the amount of artefact removed from the corrupted signal after the application of the proposed method. SAR between the artefact free signal and corrupted signal x is measured aswhere denotes the standard deviation [17].…”

“…Recently Sreeja et al . [17] proposed two sparsity‐based techniques, namely morphological component analysis (MCA) and K‐singular value decomposition (K‐SVD). The MCA‐based method depends on the choice of appropriate dictionaries (basis functions) for the separation of the signal.…”

“…where C(x, x′) denotes the covariance between x and x′[17].2.3.5 Mutual information (MI):MI is a measure of the relevant information between the corrupted signal (x) and the artefact free signal (x′). Mathematically, it is defined as and p x′ are the marginal probability density function of x and x′, respectively.…”

Modified Support Vector Machine for Detecting Stress Level Using EEG Signals

“…In [23], two sparsity based techniques are used to remove the eye blink artifacts, namely, Morphological Component Analysis (MCA) and K-SVD. Results of both algorithms were correlated with the developed FORCe method.…”

Singular Value Decomposition in Embedded Systems Based on ARM Cortex-M Architecture

“…The method was firstly established by Beltrami and Jordan [1] and successively generalized by Autonne [2], Eckart and Young [3]. Since then SVD has successfully been applied on a huge number of different application fields, such as biomedical signal processing [4][5][6][7][8][9][10], image processing [11][12][13][14][15][16][17][18][19], Kalman filtering [20][21][22], array signal processing [23], dynamic networks [24], speech processing [25], simultaneous localization and mapping (SLAM) systems [26], and variable digital filter design [27], to cite just a few.…”