Hybrid Multi-Channel EEG Filtering Method for Ocular and Muscular Artifact Removal Based on the 3D Spline Interpolation Technique

Abstract

The present work develops a novel hybrid method for ocular and muscular artifact removal from electroencephalography (EEG) signals, EFICA-TQWT. It is a combination of efficient fast independent component analysis (EFICA) method with the tunable Q-factor wavelet transform (TQWT). The main contribution of this paper is to apply the 3D interpolation method in the filtering system. Three EEG datasets are used in this work, two healthy and one epileptic. The choice of subjects for each dataset is made with the help of an expert in physiology. The selection criterion adopted is the presence of muscular and ocular artifacts in the processed recordings. First, a noisy channel automatic classification is performed by the support vector machine (SVM) with radial basis function in order to delete the signal(s) corresponding to the noisiest channel(s) from each EEG recording. The results of the automatic classification by the SVM were compared with those found by the expert’s classification. An accuracy of 97.45%, a sensitivity of 86.66% and a 100% specificity are provided by the SVM classification. The hybrid method of artifact removal will be applied on the rest of the EEG channels of international 10/20 system for each subject. Then, a reconstruction of the eliminated channel signal(s) will be performed in order to obtain a well-filtered signal. The proposed filtering process is evaluated by calculating the mean squared error (MSE) and the signal to noise ratio (SNR). Both for the healthy and pathological EEG datasets, a comparative study of the proposed method (EFICA-TQWT) and other filtering techniques (Fast-ICA, DWT, TQWT and EFICA) is generated. The EFICA-TQWT method gave the best results with a minimum of MSE and a maximum of SNR, more particularly in the case of the application of the 3D interpolation method. Besides, in order to optimize the computing time of the proposed system, a parallel implementation of this filtering system is developed based on graphical processing units using compute unified device architecture.

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“…Defective channels are identified and mitigated through interpolation [37]. The interpolated EEG signal ζ(t) can be obtained as follows:…”

EEG artifacts detection and removal techniques for brain computer interface applications: a systematic review

“…Devulapalli et al [99] introduced a hybrid method firefly-Levenberg-Marquardt (FLM) with adaptive filter for optimization of EMG, ECG, EOG artifacts and demonstrated that this method is effective in removal of ocular artifact. Abidi et al [100] has shown a hybrid method for removal of muscle and ocular artifacts for multi-channel EEG with efficient fast independent component analysis (EFICA) and tunable Q-factor wavelet transform (TQWT) with reduced mean square error. Chen et al [101] proposed variational mode decomposition (VMD) with CCA for removal of muscle artifact and demonstrated that it is superior method compared to the available methods.…”

“…Currently, artifact detection or removal is also addressed using machine learning and deep learning models considering it as hybrid artifact removal methods. SVM combined with other artifact removal technique is the most widely used hybrid method as indicated in [27,68,97,100] , [135−138]. Extreme learning machine algorithm using regression model is proposed in [139] for reducing the cardiac artifact of single channel EEG.…”

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