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Empirical mode decomposition: a method for analyzing neural data

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Title: Empirical mode decomposition: a method for analyzing neural data
Authors: Liang, Hualou1 Hualou.liang@uth.tmc.edu, Bressler, Steven L.2, Desimone, Robert3, Fries, Pascal4
Source: Neurocomputing. Jun2005, Vol. 65-66, p801-807. 7p.
Subjects: Biological neural networks, Cognitive neuroscience, Neurobiology, Cognitive science
Abstract: Abstract: Almost all processes that are quantified in neurobiology are stochastic and nonstationary. Conventional methods that characterize these processes to provide a meaningful and precise description of complex neurobiological phenomenon may be insufficient. Here, we report on the use of the data-driven empirical mode decomposition (EMD) method to study neuronal activity in visual cortical area V4 of macaque monkeys performing a visual spatial attention task. We found that local field potentials were resolved by the EMD into the sum of a set of intrinsic components with different degrees of oscillatory content. High-frequency components were identified as gamma band (35–90Hz) oscillations, whereas low-frequency components in single-trial recordings contributed to the average visual evoked potential (AVEP). Comparison with Fourier analysis showed that EMD may offer better temporal and frequency resolution. The EMD, coupled with instantaneous frequency analysis, may prove to be a vital technique for the analysis of neural data. [Copyright &y& Elsevier]
Copyright of Neurocomputing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
An: 17796178
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  Data: Abstract: Almost all processes that are quantified in neurobiology are stochastic and nonstationary. Conventional methods that characterize these processes to provide a meaningful and precise description of complex neurobiological phenomenon may be insufficient. Here, we report on the use of the data-driven empirical mode decomposition (EMD) method to study neuronal activity in visual cortical area V4 of macaque monkeys performing a visual spatial attention task. We found that local field potentials were resolved by the EMD into the sum of a set of intrinsic components with different degrees of oscillatory content. High-frequency components were identified as gamma band (35–90Hz) oscillations, whereas low-frequency components in single-trial recordings contributed to the average visual evoked potential (AVEP). Comparison with Fourier analysis showed that EMD may offer better temporal and frequency resolution. The EMD, coupled with instantaneous frequency analysis, may prove to be a vital technique for the analysis of neural data. [Copyright &y& Elsevier]
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  Data: <i>Copyright of Neurocomputing is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Jun2005
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              Y: 2005
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