Artifacts Removal using Dragonfly Levenberg Marquardt-Based Learning Algorithm from Electroencephalogram Signal

: Electroencephalogram (EEG) is the recording of the electrical activity of the brain. The waveforms that are recorded from the brain regions show the cortical activity. The integration of EEG signals with other bio-signals is known as artifacts. Some of the artifacts are Electrooculogram (EOG), Electrocardiogram (ECG), and Electromyogram (EMG). The artifacts removed from the EEG signal are very challenging in medical. This paper presents the Dragonfly Levenberg Marquardt (DrLM) optimization-based Neural Network (NN) to remove the artifacts from EEG. Initially, the EEG signal is subjected to adaptive filter for determining the optimal weights based on Dragonfly Algorithm (DA) and LM. These two approaches are hybridized and given to the NN to identify the weights. At last, the artifacts are removed from the EEG signal. The performance of DrLM-NN is evaluated in terms of SNR, MSE, and RMSE. The proposed artifact removal method achieves the maximum SNR of 45.67, minimal MSE of 2982, and minimal RMSE of 1.11 that indicates its superiority.

[1]  Alexander Bertrand,et al.  A generic EEG artifact removal algorithm based on the multi-channel Wiener filter , 2018, Journal of neural engineering.

[2]  Abdullah Al Mamun,et al.  Automatic EEG Artifact Removal Techniques by Detecting Influential Independent Components , 2017, IEEE Transactions on Emerging Topics in Computational Intelligence.

[3]  Chen Peng,et al.  Model in Frequency-Domain Identification of a Fast Steering Mirror System Based on Levenberg-Marquardt Algorithm , 2017, 2017 2nd International Conference on Cybernetics, Robotics and Control (CRC).

[4]  F. D. V. Fallani,et al.  Surrogate-Based Artifact Removal From Single-Channel EEG , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[5]  Don M. Tucker,et al.  An improved artifacts removal method for high dimensional EEG , 2016, Journal of Neuroscience Methods.

[6]  Seyedali Mirjalili,et al.  Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems , 2015, Neural Computing and Applications.

[7]  Kaspar Anton Schindler,et al.  PureEEG: Automatic EEG artifact removal for epilepsy monitoring , 2014, Neurophysiologie Clinique/Clinical Neurophysiology.

[8]  Arjon Turnip,et al.  Removal artifacts from EEG signal using independent component analysis and principal component analysis , 2014, 2014 2nd International Conference on Technology, Informatics, Management, Engineering & Environment.

[9]  Mohammad Ali Badamchizadeh,et al.  Artifacts removal in EEG signal using a new neural network enhanced adaptive filter , 2013, Neurocomputing.

[10]  Jiang Li,et al.  EOG artifact removal using a wavelet neural network , 2012, Neurocomputing.

[11]  Vijander Singh,et al.  New Approaches for Image Compression Using Neural Network , 2011 .

[12]  Guilherme De A. Barreto,et al.  Long-term time series prediction with the NARX network: An empirical evaluation , 2008, Neurocomputing.

[13]  Dezhong Yao,et al.  Removal of the ocular artifacts from EEG data using a cascaded spatio-temporal processing , 2006, Comput. Methods Programs Biomed..

[14]  T. Sejnowski,et al.  Removing electroencephalographic artifacts by blind source separation. , 2000, Psychophysiology.

[15]  S. G. Kahalekar,et al.  Artifacts removal from EEG signal: FLM optimization-based learning algorithm for neural network-enhanced adaptive filtering , 2017 .

[16]  Dwi Esti Kusumandari,et al.  A Comparison of EEG Processing Methods to Improve the Performance of BCI , 2013, SiPS 2013.

[17]  K. Hong,et al.  CLASSIFYING MENTAL ACTIVITIES FROM EEG-P 300 SIGNALS USING ADAPTIVE NEURAL NETWORKS , 2012 .

[18]  G. Geetha,et al.  Artifact Removal from EEG using Spatially Constrained Independent Component Analysis and Wavelet Denoising with Otsu's Thresholding Technique , 2012 .