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Sensors 2017, 17(5), 989; doi:10.3390/s17050989

Analysis of Gamma-Band Activity from Human EEG Using Empirical Mode Decomposition

Departamento de Electrónica, Grupo de Ingeniería Biomédica, Universidad de Alcalá, Alcalá de Henares 28801, Spain
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Academic Editor: Wee Ser
Received: 2 February 2017 / Revised: 25 April 2017 / Accepted: 26 April 2017 / Published: 29 April 2017
(This article belongs to the Section Biosensors)
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Abstract

The purpose of this paper is to determine whether gamma-band activity detection is improved when a filter, based on empirical mode decomposition (EMD), is added to the pre-processing block of single-channel electroencephalography (EEG) signals. EMD decomposes the original signal into a finite number of intrinsic mode functions (IMFs). EEGs from 25 control subjects were registered in basal and motor activity (hand movements) using only one EEG channel. Over the basic signal, IMF signals are computed. Gamma-band activity is computed using power spectrum density in the 30–60 Hz range. Event-related synchronization (ERS) was defined as the ratio of motor and basal activity. To evaluate the performance of the new EMD based method, ERS was computed from the basic and IMF signals. The ERS obtained using IMFs improves, from 31.00% to 73.86%, on the original ERS for the right hand, and from 22.17% to 47.69% for the left hand. As EEG processing is improved, the clinical applications of gamma-band activity will expand. View Full-Text
Keywords: electroencephalography; gamma-band activity; motor area; motor tasks; empirical mode decomposition; event-related synchronization; power spectral density electroencephalography; gamma-band activity; motor area; motor tasks; empirical mode decomposition; event-related synchronization; power spectral density
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MDPI and ACS Style

Amo, C.; de Santiago, L.; Barea, R.; López-Dorado, A.; Boquete, L. Analysis of Gamma-Band Activity from Human EEG Using Empirical Mode Decomposition. Sensors 2017, 17, 989.

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