An adaptive noise canceller based on QLMS algorithm for removing EOG artifacts in EEG recordings

In this paper, a novel adaptive noise canceller (ANC) based on the quaternion valued least mean square algorithm (QLMS) is designed in order to remove electrooculography (EOG) artifacts from electroencephalography (EEG) recordings. The measurement real-valued EOG and EEG signals (FP1, FP2, AF3 and AF4) are first modeled as four-dimensional processes in the quaternion domain. The EOG artifacts are then removed from the EEG signals in the quaternion domain by using the ANC based on QLMS algorithm. The quaternion representation of these signals allows us to remove EOG artifacts from all channels at the same time instead of removing the EOG artifacts in each EEG recordings separately. The simulation results support the proposed approach.

[1]  Luca Citi,et al.  Documenting, modelling and exploiting P300 amplitude changes due to variable target delays in Donchin's speller , 2010, Journal of neural engineering.

[2]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[3]  Rahul Kher,et al.  Adaptive filtering based artifact removal from electroencephalogram (EEG) signals , 2016, 2016 International Conference on Communication and Signal Processing (ICCSP).

[4]  Danilo P. Mandic,et al.  Performance Bounds of Quaternion Estimators , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[5]  Danilo P. Mandic,et al.  The Quaternion LMS Algorithm for Adaptive Filtering of Hypercomplex Processes , 2009, IEEE Transactions on Signal Processing.

[6]  Soo-Chang Pei,et al.  Color image processing by using binary quaternion-moment-preserving thresholding technique , 1999, IEEE Trans. Image Process..

[7]  Giovanni Muscato,et al.  Multilayer Perceptrons to Approximate Quaternion Valued Functions , 1997, Neural Networks.

[8]  Danilo P. Mandic,et al.  Quaternion Reproducing Kernel Hilbert Spaces: Existence and Uniqueness Conditions , 2014, IEEE Transactions on Information Theory.

[9]  Giovanni Muscato,et al.  A comparison between HMLP and HRBF for attitude control , 2001, IEEE Trans. Neural Networks.

[10]  Danilo P. Mandic,et al.  A Quaternion Widely Linear Adaptive Filter , 2010, IEEE Transactions on Signal Processing.

[11]  Danilo P. Mandic,et al.  Quaternion-Valued Echo State Networks , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[12]  R. Arumuganathan,et al.  Removal of artifacts from EEG signals using adaptive filter through wavelet transform , 2008, 2008 9th International Conference on Signal Processing.

[13]  Danilo P. Mandic,et al.  Optimization in Quaternion Dynamic Systems: Gradient, Hessian, and Learning Algorithms , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[14]  Danilo P. Mandic,et al.  A Class of Quaternion Kalman Filters , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[15]  G. Wilson,et al.  Removal of ocular artifacts from electro-encephalogram by adaptive filtering , 2004, Medical and Biological Engineering and Computing.

[16]  R. Srinivasan,et al.  Removal of ocular artifacts from EEG using an efficient neural network based adaptive filtering technique , 1999, IEEE Signal Processing Letters.

[17]  Nobuyuki Matsui,et al.  Quaternion neural network with geometrical operators , 2004, J. Intell. Fuzzy Syst..