NDLI: Performance prediction of adaptive filters for EEG signal

Content Provider	IET Digital Library
Author	Ahirwal, Mitul Kumar Kumar, Anil Singh, Girish Kumar Londhe, Narendra Digambar
Abstract	A methodology for predicting the performance of algorithm in terms of fidelity parameters is introduced. Algorithm performance is measured in terms of space and time complexities required to solve a particular problem. The proposed approach correlates the performance with input data and output results. Initial theory of performance model generation is based on polynomial regressing and curve fitting of least mean square (LMS) algorithm for adaptive filtering. Testing and validation of the generated model has been done through the coefficient of determination parameters calculated over interpolation and extrapolation results. Other measures like adjusted R 2, sum of square error and mean square error are also reported. Overall study is summarised in form of algorithm, and is tested with LMS and recursive LMS algorithm of adaptive filtering applied over electroencephalography/event related potentials (EEG/ERP) noise removal to build up relation between input signal-to-noise ratio (SNR) and output fidelity parameters (like output SNR and correlation (r)).
Starting Page	525
Ending Page	531
Page Count	7
ISSN	17518822
Volume Number	11
e-ISSN	17518830
Issue Number	Issue 5, Aug (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-smt/11/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-smt.2016.0254
Journal	IET Science, Measurement & Technology
Publisher Date	2017-01-20
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Filter Adaptive Filtering Bioelectric Signal Biology And Medical Computing Computational Complexity Curve Fitting Digital Signal Processing EEG Signal EEG-ERP Noise Removal Electrodiagnostics And Other Electrical Measurement Technique Electroencephalography Electroencephalography-event Related Potential Noise Removal Extrapolation Filtering Method in Signal Processing Interpolation Interpolation And Function Approximation Least Mean Square Error Algorithm Least Mean Squares Method Medical Signal Processing Numerical Analysis Numerical Approximation And Analysis Polynomial Polynomial Regression Prediction Theory Probability Theory Recursive Filter Recursive LMS Algorithm Regression Analysis Signal Denoising Signal to Noise Ratio SNR Space Complexity Statistics Stochastic Linearised SCUC Time Complexity
Content Type	Text
Resource Type	Article
Subject	Atomic and Molecular Physics, and Optics Electrical and Electronic Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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