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.
Highlights
This study described how intrinsic mode functions (IMFs) are effective in assessing Gamma-band activity (GBA) as activity in motor tasks is improved
This study described how IMFs are effective in assessing GBA as a complement to Fourier-based methods
The ratio of motor and basal activity obtained using IMFs improved, from 31.00% to 73.86% of the original value, for the right hand, and 22.17% to 47.69% for improved, from 31.00% to 73.86% of the original value, for the right hand, and 22.17% to 47.69% for the left
Summary
An electroencephalogram (EEG) represents the electrical activity of the brain, recorded by placing several electrodes on the scalp. Semiautomatic detection of artifacts (eye blinks, muscle contraction, etc.) methods improve the quality of analysis results of EEG signals [3,4,5]. Signal processing methods, such as extraction of power spectral density (PSD) [6], wavelet analysis [7], independent component analysis (ICA) [8], and local mean decomposition [9], are used to improve recording quality and to analyze data. Automatic classification methods are implemented [10,11,12,13]
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