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Related Experiment Video

Updated: May 14, 2026

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Oracle Upper Bounds on Clean-EEG Recoverability from Single-Channel Decompositions Under EOG/EMG Contamination.

Usman Qamar Shaikh1,2, Anubha Manju Kalra1,2, Andrew Lowe1,3

  • 1Institute of Biomedical Technologies, Auckland University of Technology, Auckland 1010, New Zealand.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

Singular Spectrum Analysis (SSA) offers superior electroencephalogram (EEG) artifact suppression, outperforming other methods in most conditions. This benchmark evaluates decomposition techniques for cleaner EEG signal recovery.

Keywords:
CEEMDANartifact suppressionelectroencephalography (EEG)muscle artifacts (EMG)ocular artifacts (EOG)signal decompositionsingular spectrum analysis (SSA)variational mode decomposition (VMD)wavelet transform (DWT)

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Last Updated: May 14, 2026

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Single-channel EEG artifact suppression is crucial for accurate analysis.
  • Existing methods often rely on signal decomposition, but optimal clean EEG recovery is unclear.
  • An ideal component weighting scenario is not well-defined.

Purpose of the Study:

  • To establish an oracle-based benchmark for evaluating EEG decomposition methods.
  • To characterize the best-case recoverability of clean EEG from common 1-D decomposition families.
  • To isolate representation capacity from component-selection heuristics.

Main Methods:

  • A synthetic benchmark of 4500 single-channel EEG segments was created using EEGdenoiseNet.
  • Clean EEG was mixed with ocular (EOG) and cranial electromyography (EMG) artifacts at varying noise-to-signal ratios (NSRs).
  • Variational Mode Decomposition (VMD), Singular Spectrum Analysis (SSA), Discrete Wavelet Transform (DWT), and CEEMDAN were evaluated using an oracle-based reconstruction.

Main Results:

  • SSA demonstrated the lowest reconstruction error across most tested conditions.
  • DWT performed best in scenarios with milder ocular contamination.
  • VMD performance improved with more modes at a higher computational cost; CEEMDAN showed higher latency.

Conclusions:

  • The benchmark provides decomposition-level upper bounds for EEG artifact suppression under controlled conditions.
  • Results are not indicative of field-ready denoising performance.
  • This benchmark aids in comparing decomposition methods and designing practical component-selection strategies.