A Robust LPC Filtering Method for Time-Resolved Morphology of EEG Activity Analysis

Jin Xu; Mark Davis; Ruairí de Fréin

doi:10.21427/z94d-mw11

A Robust LPC Filtering Method for Time-Resolved Morphology of EEG Activity Analysis

Jin Xu, Mark Davis, Ruairí de Fréin

School of Electrical and Electronic Engineering

Research output: Contribution to conference › Paper › peer-review

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
DOIs	https://doi.org/10.21427/z94d-mw11
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
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

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@conference{04144faf6b784bc9b4b0eedebdf3d180,

title = "A Robust LPC Filtering Method for Time-Resolved Morphology of EEG Activity Analysis",

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.",

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",

author = "Jin Xu and Mark Davis and {de Fr{\'e}in}, Ruair{\'i}",

year = "2020",

doi = "10.21427/z94d-mw11",

language = "English",

note = "26th Annual Conference of the Section of Bioengineering of the Royal Academy of Medicine in Ireland ; Conference date: 17-01-2020 Through 18-01-2020",

}

TY - CONF

T1 - A Robust LPC Filtering Method for Time-Resolved Morphology of EEG Activity Analysis

AU - Xu, Jin

AU - Davis, Mark

AU - de Fréin, Ruairí

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - time-resolved spectral analysis

KW - Linear Prediction Coding (LPC)

KW - EEG activity

KW - spectral dynamic

KW - frequency components

KW - noise

KW - temporal and spectral resolution

KW - Heisenberg-Gabor uncertainty principle

KW - z-plane analysis

KW - poles of the LPC

KW - dominant spectral peaks

KW - temporal variations

KW - noisy EEG signal

U2 - 10.21427/z94d-mw11

DO - 10.21427/z94d-mw11

M3 - Paper

T2 - 26th Annual Conference of the Section of Bioengineering of the Royal Academy of Medicine in Ireland

Y2 - 17 January 2020 through 18 January 2020

ER -

A Robust LPC Filtering Method for Time-Resolved Morphology of EEG Activity Analysis

Abstract

Conference

Keywords

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