Swarm Intelligence-Based Improved Adaptive Chirp Mode Decomposition Algorithm for Suppression of Ocular Artifacts from EEG Signal

Electroencephalogram (EEG) signals are mostly contaminated with ocular artifacts (OAs) due to eye movements and eye blinks. These artifacts make the EEG recordings difficult to analyze and diagnose neurological diseases. Therefore, in this work, we propose a hybrid framework that consists of two sta...

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Bibliographic Details
Published in:	IEEE sensors journal Vol. 24; no. 6; pp. 8314 - 8325
Main Authors:	Silpa, Bommala, Hota, Malaya Kumar
Format:	Journal Article
Language:	English
Published:	New York IEEE 15.03.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Adaptive chirp mode decomposition (ACMD) Algorithms Chirp Clustering Continuous wavelet transform continuous wavelet transform (CWT) Continuous wavelet transforms Decomposition electroencephalogram (EEG) Electroencephalography Energy value Eye movements k-means clustering algorithm Mathematical analysis Neurological diseases Noise measurement ocular artifacts (OAs) Optimization Recording Subtraction Swarm intelligence Transforms Waveforms Wavelet transforms
ISSN:	1530-437X, 1558-1748
Online Access:	Get full text
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