Research Paper | Signal Processing | Bangladesh | Volume 6 Issue 4, April 2017
Time-Frequency Analysis of EEG Signal processing for Artifact Detection
Abstract: EEG is widely used to record the electrical activity of the brain for detecting various kinds of diseases and disorders of the human brain. EEG signals are contaminated with several unwanted artifacts during EEG recording and these artifacts make the analysis of EEG signal difficult by hiding some valuable information. Time-frequency representation of electroencephalogram (EEG) signal provides a source of information that is usually hidden in the Fourier spectrum. The popular methods of short-time Fourier transform, Hilbert Haung transform and the wavelet transform analysis have limitations in representing close frequencies and dealing with fast varying instantaneous frequencies and this is often the nature of EEG signal. The synchrosqueezing transform (SST) is a promising tool to track these resonant frequencies and provide a detailed time-frequency representation. The SST is an extension of the wavelet transform incorporating elements of empirical mode decomposition and frequency reassignment techniques. This new tool produces a well-defined time frequency representation allowing the identification of instantaneous frequencies in EEG signals to highlight individual components. We introduce the SST with applications for EEG signals and produced promising results on synthetic and real examples. . In this paper, different time-frequency distributions are compared with each other with respect to their time and frequency resolution. Several examples are given to illustrated the usefulness of time-frequency analysis in electroencephalography.
Keywords: EEG signal, STFT, HHT, WPT, SST, time-frequency representation
Edition: Volume 6 Issue 4, April 2017,
Pages: 2013 - 2021
How to Cite this Article?
Mst. Jannatul Ferdous, Md. Sujan Ali, "Time-Frequency Analysis of EEG Signal processing for Artifact Detection", International Journal of Science and Research (IJSR), https://www.ijsr.net/get_abstract.php?paper_id=ART20172894, Volume 6 Issue 4, April 2017, 2013 - 2021, #ijsrnet
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