2013Unpublished venueRequires access

EEG time-frequency analysis based on the improved S-transform

Zhang Shaobai, Huang Dandan

Open publisher page 3 citations

Abstract

S-transform, which is a combination of short-time Fourier transform and wavelet transform, has attract intensive interest in recent years as an important tool to investigate non-stationary signal time-frequency distribution. S transform can be self-improved by the EEG characteristics to select a suitable mother wavelet. The improved S-transform will be used to analyze the time-frequency of the EEG characters. A comparison among the Short-time Fourier transform, wavelet transformation and the improved S-transform indicates that improved S-transform gives the best energy distribution in the time-frequency filed.

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What this paper is about

S-transform, which is a combination of short-time Fourier transform and wavelet transform, has attract intensive interest in recent years as an important tool to investigate non-stationary signal time-frequency distribution. S transform can be self-improved by the EEG characteristics to select a suitable mother wavelet. The improved S-transform will be used to analyze the time-frequency of the EEG characters. A comparison among the Short-time Fourier transform, wavelet transformation and the improved S-transform indicates that improved S-transform gives the best energy distribution in the time-frequency filed.

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OpenAlex reports 3 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

S-transform, which is a combination of short-time Fourier transform and wavelet transform, has attract intensive interest in recent years as an important tool to investigate non-stationary signal time-frequency distribution. S transform can be self-improved by the EEG characteristics to select a suitable mother wavelet. The improved S-transform will be used to analyze the time-frequency of the EEG characters. A comparison among the Short-time Fourier transform, wavelet transformation and the improved S-transform indicates that improved S-transform gives the best energy distribution in the time-frequency filed.

Key concepts: Harmonic wavelet transform, S transform, Wavelet transform, Short-time Fourier transform, Constant Q transform, Time–frequency analysis, Continuous wavelet transform, Discrete wavelet transform

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