Abstract
This paper introduces a new time-resolved spectral analysis method based on Linear Prediction Coding (LPC) method that is particularly suited to the study of the dynamics of EEG (Electroencephalogram) activity. The spectral dynamic of EEG signals can be challenging to analyse as they contain multiple frequency components and are often heavily corrupted by noise. Furthermore, the temporal and spectral resolution that can be achieved is limited by the Heisenberg-Gabor uncertainty principle [1]. The method described here is based on a z-plane analysis of the poles of the LPC which allows us to identify and estimate the frequency of the dominant spectral peaks. We demonstrate how this method can be used to track the temporal variations of the various frequency components in a noisy EEG signal.
Original language | English |
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DOIs | |
Publication status | Published - 2020 |
Event | 26th Annual Conference of the Section of Bioengineering of the Royal Academy of Medicine in Ireland - Duration: 17 Jan 2020 → 18 Jan 2020 |
Conference
Conference | 26th Annual Conference of the Section of Bioengineering of the Royal Academy of Medicine in Ireland |
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Period | 17/01/20 → 18/01/20 |
Keywords
- time-resolved spectral analysis
- Linear Prediction Coding (LPC)
- EEG activity
- spectral dynamic
- frequency components
- noise
- temporal and spectral resolution
- Heisenberg-Gabor uncertainty principle
- z-plane analysis
- poles of the LPC
- dominant spectral peaks
- temporal variations
- noisy EEG signal