NDLI: EEG/ERP Adaptive Noise Canceller Design with Controlled Search Space (CSS) Approach in Cuckoo and Other Optimization Algorithms

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IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) : Volume 13

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EEG/ERP Adaptive Noise Canceller Design with Controlled Search Space (CSS) Approach in Cuckoo and Other Optimization Algorithms

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EEG/ERP Adaptive Noise Canceller Design with Controlled Search Space (CSS) Approach in Cuckoo and Other Optimization Algorithms

Content Provider	ACM Digital Library
Author	Ahirwal, M. K. Kumar, A. Singh, G. K.
Abstract	This paper explores the migration of adaptive filtering with swarm intelligence/evolutionary techniques employed in the field of electroencephalogram/event-related potential noise cancellation or extraction. A new approach is proposed in the form of controlled search space to stabilize the randomness of swarm intelligence techniques especially for the EEG signal. Swarm-based algorithms such as Particles Swarm Optimization, Artificial Bee Colony, and Cuckoo Optimization Algorithm with their variants are implemented to design optimized adaptive noise canceler. The proposed controlled search space technique is tested on each of the swarm intelligence techniques and is found to be more accurate and powerful. Adaptive noise canceler with traditional algorithms such as least-mean-square, normalized least-mean-square, and recursive least-mean-square algorithms are also implemented to compare the results. ERP signals such as simulated visual evoked potential, real visual evoked potential, and real sensorimotor evoked potential are used, due to their physiological importance in various EEG studies. Average computational time and shape measures of evolutionary techniques are observed 8.21E-01 sec and 1.73E-01, respectively. Though, traditional algorithms take negligible time consumption, but are unable to offer good shape preservation of ERP, noticed as average computational time and shape measure difference, 1.41E-02 sec and 2.60E+00, respectively.
Starting Page	1491
Ending Page	1504
Page Count	14
File Format	PDF
ISSN	15455963
DOI	10.1109/TCBB.2013.119
Volume Number	10
Issue Number	6
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-11-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Adaptive noise canceler, EEG/ERP, COA, PSO, ABC, evolutionary techniques
Content Type	Text
Resource Type	Article
Subject	Genetics Biotechnology Applied Mathematics

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EEG/ERP Adaptive Noise Canceller Design with Controlled Search Space (CSS) Approach in Cuckoo and Other Optimization Algorithms

Adaptive noise canceller based on PSO algorithm

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Analysis and testing of PSO variants through application in EEG/ERP adaptive filtering approach

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IIR system identification using particle swarm optimization with constriction factor and inertia weight approach

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EEG/ERP Adaptive Noise Canceller Design with Controlled Search Space (CSS) Approach in Cuckoo and Other Optimization Algorithms

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