EEG/ERP Adaptive Noise Canceller Design with Controlled Search Space (CSS) Approach in Cuckoo and Other Optimization Algorithms
作者： M.K. Ahirwal , Anil Kumar , G.K. Singh
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摘要: This paper explores the migration of adaptive filtering with swarm intelligence/evolutionary techniques employed in field electroencephalogram/event-related potential noise cancellation or extraction. A new approach is proposed form controlled search space to stabilize randomness intelligence especially for EEG signal. Swarm-based algorithms such as Particles Swarm Optimization, Artificial Bee Colony, and Cuckoo Optimization Algorithm their variants are implemented design optimized canceler. The technique tested on each found be more accurate powerful. Adaptive canceler traditional least-mean-square, normalized recursive least-mean-square also compare results. ERP signals simulated visual evoked potential, real sensorimotor used, due physiological importance various studies. Average computational time shape measures evolutionary observed 8.21E-01 sec 1.73E-01, respectively. Though, take negligible consumption, but unable offer good preservation ERP, noticed average measure difference, 1.41E-02 2.60E+00,
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