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Updated: Jun 14, 2026

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

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

Published on: May 13, 2014

Multi-taper transfer function estimation for stimulation artifact removal from neural recordings.

Nick Chernyy¹, Steven J Schiff, Bruce J Gluckman

¹Engineering Science and Mechanics Department, The Pennsylvania State University, State College, PA 16802, USA. nchernyy@psu.edu

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

|January 24, 2009

Summary

This summary is machine-generated.

Simultaneously stimulating and recording neural tissue requires removing stimulation artifacts. This study uses multi-taper spectral estimation to accurately model and subtract these artifacts for improved neural control systems.

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Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Simultaneous neural stimulation and recording are crucial for closed-loop neural control systems.
Electrical stimulation generates artifacts that contaminate neural recordings.
Accurate artifact removal is essential for interpreting neural signals and enabling effective feedback.

Purpose of the Study:

To develop and validate a method for accurately estimating and removing stimulation artifacts in neural recordings.
To improve the fidelity of neural data for applications in neural modulation and control.
To address challenges posed by measurement noise and spectral estimation biases.

Main Methods:

Modeling stimulation artifacts as a linear transfer function of stimulus current.
Employing multi-taper spectral estimation techniques to reduce bias and variance in transfer function computation.
Applying the developed method to neural data from chronically instrumented animals.

Main Results:

Successfully approximated stimulation artifacts using a linear transfer function.
Demonstrated significant reduction in bias and variance of spectral estimation using multi-taper methods.
Validated the transfer function's efficacy in removing stimulation artifacts during low-frequency neural modulation.

Conclusions:

The proposed method effectively removes stimulation artifacts, enhancing the quality of neural recordings.
Multi-taper spectral estimation is a robust technique for accurate artifact modeling in noisy neural data.
This artifact removal technique is vital for advancing feedback-enabled neural control and modulation strategies.