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12 April 2004 Nonstationary signal analysis in episodic memory retrieval
Y. G. Ku, Masashi Kawasumi, Masao Saito
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Proceedings Volume 5439, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks II; (2004) https://doi.org/10.1117/12.541295
Event: Defense and Security, 2004, Orlando, Florida, United States
Abstract
The problem of blind source separation from a mixture that has nonstationarity can be seen in signal processing, speech processing, spectral analysis and so on. This study analyzed EEG signal during episodic memory retrieval using ICA and TVAR. This paper proposes a method which combines ICA and TVAR. The signal from the brain not only exhibits the nonstationary behavior, but also contain artifacts. EEG data at the frontal lobe (F3) from the scalp is collected during the episodic memory retrieval task. The method is applied to EEG data for analysis. The artifact (eye movement) is removed by ICA, and a single burst (around 6Hz) is obtained by TVAR, suggesting that the single burst is related to the brain activity during the episodic memory retrieval.
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Y. G. Ku, Masashi Kawasumi, and Masao Saito "Nonstationary signal analysis in episodic memory retrieval", Proc. SPIE 5439, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks II, (12 April 2004); https://doi.org/10.1117/12.541295
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KEYWORDS
Electroencephalography
Independent component analysis
Signal analysis
Brain
Signal processing
Eye
Signal analyzers
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