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Related Experiment Videos

Constrained RLS algorithm for narrow band interference rejection from EEG signal during CES.

O Sezer1, M Ferdjallah

  • 1Dept. of Electr. & Comput. Eng., Tennessee Univ., Knoxville, TN, USA.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|February 3, 2007
PubMed
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This study introduces a novel constrained recursive least-squares (CRLS) adaptive filter to effectively remove narrow-band noise from electroencephalographic (EEG) signals during cranial electrical stimulation (CES). The CRLS filter uniquely adapts both zeros and poles for improved signal filtering.

Area of Science:

  • Biomedical Signal Processing
  • Digital Signal Processing
  • Neuroscience Instrumentation

Background:

  • Narrow-band interference noise is a significant challenge in processing biomedical signals like electroencephalography (EEG).
  • Cranial electrical stimulation (CES) can introduce specific narrow-band white Gaussian noise into EEG recordings.
  • Existing adaptive filters struggle to efficiently adapt to changing noise characteristics.

Purpose of the Study:

  • To develop and evaluate a novel adaptive band-rejection filter for removing double narrow-band noise from EEG signals.
  • To improve the filtering performance during cranial electrical stimulation (CES) procedures.
  • To introduce a constrained recursive least-squares (CRLS) algorithm that allows true adaptation of filter zeros and poles.

Main Methods:

Related Experiment Videos

  • Design of multiple adaptive Infinite Impulse Response (IIR) digital band-rejection filters using pole-zero placement on the unit circle.
  • Cascading N second-order band-rejection filters to achieve a higher-order filter (2N).
  • Utilizing a unique second-order filter structure and convoluting coefficients for filter design.
  • Updating filter coefficients via a constrained recursive least-squares (CRLS) algorithm.

Main Results:

  • The proposed CRLS multiple adaptive HR band-rejection filter demonstrates true adaptation of both zeros and poles.
  • Effective filtering of double narrow-band white Gaussian noise from EEG signals during CES.
  • Improved signal quality compared to existing RLS-based adaptive filters.

Conclusions:

  • The CRLS adaptive filter provides a robust solution for narrow-band noise removal in EEG signals.
  • This method enhances the accuracy of EEG analysis, particularly in the presence of interference from CES.
  • The true adaptation of zeros and poles offers superior performance in dynamic noise environments.