Enhanced Fast Fully Adaptive Beamformer for Localizing Brain Short Time Activities

Talesh Jafadideh, Alireza; Mohammadzadeh Asl, Babak

doi:10.22041/ijbme.2017.72842.1269

Document Type : Full Research Paper

Authors

¹ Ph.D Student, Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran

² Associate Professor, Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran

https://doi.org/10.22041/ijbme.2017.72842.1269

Abstract

Minimum variance beamformer (MVB) and its extensions are most widely used techniques in brain source localization due to their high spatial resolution. Unfortunately, beacause of using data covariance matrix, these methods often fail when the number of samples of the recorded data sequences is small in comparison to the number of electrodes. This condition is particularly relevant when measuring evoked potentials. For solving this problem, Fast Fully Adaptive (FFA) algorithm was developed a few years ago. This method is a multistage adaptive processing technique drawing its inspiration from the butterfly structure of the Fast Fourier Transform (FFT) and decreasing the data requirement significantly. Unfortunately, the high sensitivity of FFA to data partitioning sequences and also its low performance in low SNRs pose a doubt on using it as a reliable localizer for short time brain activities. In this paper, a preprocessing step is proposed to enhance the FFA method. In this step, the brain is divided into separate areas, the components of each area are determined, the data is projected to each area using components of that area. After that, FFA is applied to the projected data. The performance of the enhanced FFA is compared with FFA method by using simulated ERP and real ERF data. In all simulations, enhanced FFA shows the better performance in terms of localization error (enhancement about 2-10 mm) and spread radius (enhancement about 4-9 mm). In addition, the proposed method for real ERF data shows accurate localization result with the most concentrated power spectrum, compared to FFA approach. It is noteworthy that enhanced FFA offers less sensitivity to data partitioning sequences. Emprical results illustrate that enhanced FFA can be implemented as a reliable method for localizing brain short time activities.

Keywords

Main Subjects

Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing

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Iranian Journal of Biomedical Engineering

Enhanced Fast Fully Adaptive Beamformer for Localizing Brain Short Time Activities

References

References

Volume 10, Issue 4
January 2017
Pages 347-359

Enhanced Fast Fully Adaptive Beamformer for Localizing Brain Short Time Activities

References

References

Volume 10, Issue 4January 2017Pages 347-359

Volume 10, Issue 4
January 2017
Pages 347-359