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Related Experiment Video

Updated: May 14, 2026

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Using Wavelet Entropy to Demonstrate how Mindfulness Practice Increases Coordination between Irregular Cerebral and Cardiac Activities

Published on: May 10, 2017

Application of wavelet based denoising techniques to rTMS evoked potentials.

Philip Chrapka¹, Hubert de Bruin, Gary Hasey

¹Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S 4K1, Canada. chrapkpk@mcmaster.ca

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|February 1, 2013

Summary

This study introduces a novel wavelet transform method to remove noise from electroencephalogram (EEG) signals during repetitive transcranial magnetic stimulation (rTMS). This technique enhances the analysis of early cortical evoked potentials by reducing stimulus artifacts and muscle activity.

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Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

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Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Repetitive transcranial magnetic stimulation (rTMS) generates significant noise in electroencephalogram (EEG) recordings.
This noise, comprising stimulus artifacts and muscle potentials, obscures early cortical evoked potentials (within 15 ms post-stimulus).
Accurate analysis of early cortical responses is crucial for understanding brain function and rTMS effects.

Purpose of the Study:

To develop and evaluate a new noise removal method for EEG signals acquired during rTMS.
To improve the analysis of early cortical evoked potentials obscured by noise.
To compare the efficacy of different coefficient reduction strategies within the proposed method.

Main Methods:

Utilized the wavelet transform with a fourth-order Daubechies mother wavelet.
Implemented a novel coefficient reduction algorithm based on cortical amplitude thresholds.
Applied and compared two coefficient reduction techniques to EEG data from rTMS studies.

Main Results:

The proposed wavelet transform method effectively reduces stimulus artifacts and muscle noise in EEG signals.
The novel coefficient reduction algorithm demonstrates significant noise suppression.
Comparison of reduction methods highlights differences in artifact removal efficacy.

Conclusions:

The developed wavelet-based method offers a robust solution for cleaning EEG data during rTMS.
This technique facilitates clearer analysis of early cortical evoked potentials.
The findings contribute to more accurate neurophysiological assessments in rTMS research.