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Noise cancelation of epileptic interictal EEG data based on generalized eigenvalue decomposition

Hajipour, S ; Sharif University of Technology | 2012

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  1. Type of Document: Article
  2. DOI: 10.1109/TSP.2012.6256365
  3. Publisher: 2012
  4. Abstract:
  5. Denoising is an important preprocessing stage in some Electroencephalography (EEG) applications such as epileptic source localization. In this paper, we propose a new algorithm for denoising the interictal EEG data. The proposed algorithm is based on Generalized Eigenvalue Decomposition of two covariance matrices of the observations. Since one of these matrices is related to the spike durations, we should estimate the occurrence time of the spike peaks and the exact spike durations. To this end, we propose a spike detection algorithm which is based on the available spike detection methods. The comparison of the results of the proposed algorithm with the results of two well-known ICA algorithms (CoM2 and SOBI) shows that the proposed algorithm denoise data as accurately as these ICA algorithms. The advantage of the proposed technique appears in terms of numerical complexity. The results also show that the proposed algorithm is considerably faster than these ICA algorithms
  6. Keywords:
  7. Denoising ; Electroencephalography (EEG) ; Generalized EigenValue Decomposition (GEVD) ; Interictal spike detection ; Algorithm for denoising ; Covariance matrices ; De-Noise ; De-noising ; Generalized eigenvalue decomposition ; ICA algorithms ; Interictal spike ; Numerical complexity ; Source localization ; Spike detection ; Spike peaks ; Covariance matrix ; Eigenvalues and eigenfunctions ; Electroencephalography ; Electrophysiology ; Signal processing ; Algorithms
  8. Source: 2012 35th International Conference on Telecommunications and Signal Processing, TSP 2012 - Proceedings ; 2012 , Pages 591-595 ; 9781467311182 (ISBN)
  9. URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6256365