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A Protocol for the Administration of Real-Time fMRI Neurofeedback Training
Published on: August 24, 2017
An efficient feedback active noise control algorithm based on reduced-order linear predictive modeling of FMRI
Govind Kannan1, Ali A Milani, Issa M S Panahi
1University of Texas at Dallas, Richardson, TX 75080-3021, USA. govkan@gmail.com
IEEE Transactions on Bio-Medical Engineering
|December 8, 2010
Summary
Functional magnetic resonance imaging (fMRI) acoustic noise is quasi-periodic. Incorporating past data improves linear prediction accuracy, enabling effective and low-cost active noise control (ANC) for fMRI environments.
Area of Science:
- Medical Imaging
- Acoustics
- Signal Processing
Background:
- fMRI generates quasi-periodic acoustic noise from gradient coil currents.
- Variations in noise waveform are caused by background noise and system drifts.
- Noise periodicity is linked to imaging parameters like slices per second in EPI sequences.
Purpose of the Study:
- To investigate the linear predictability of fMRI acoustic noise.
- To develop a low-complexity linear prediction method for fMRI noise.
- To apply this prediction method to enhance active noise control (ANC) systems.
Main Methods:
- Analyzed the quasi-periodic nature of fMRI acoustic noise.
- Developed a low-order linear predictor incorporating previous noise samples.
- Utilized the predictor to design a feedback ANC system.
Main Results:
- Achieved very high linear prediction accuracy with a low-order predictor by using samples from previous periods.
- Demonstrated the direct impact of noise predictability on ANC system performance.
- Successfully derived an effective and low-cost feedback ANC system based on the developed prediction method.
Conclusions:
- fMRI acoustic noise exhibits high linear predictability, especially when past data is utilized.
- The developed low-complexity linear prediction is crucial for effective feedback ANC systems.
- This approach offers a practical and cost-effective solution for noise cancellation in fMRI settings.
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