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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Vydané v:	IEEE sensors journal Ročník 24; číslo 6; s. 8314 - 8325
Hlavní autori:	Silpa, Bommala, Hota, Malaya Kumar
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 15.03.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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