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Related Concept Videos

Brain Waves01:23

Brain Waves

Brain waves are electrical signals generated by the neurons in the brain, which are regularly monitored to measure mental activities. Brain waves and their frequency ranges can be measured using an electroencephalogram or EEG. There are four main types of brain waves, each with distinct characteristics:

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Utilizing Electroencephalography Measurements for Comparison of Task-Specific Neural Efficiencies: Spatial Intelligence Tasks
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Comparison of preprocessing techniques for effective cognitive analysis using electroencephalography (EEG).

Keerthika N1, Kiruthika V1

  • 1School of Electronics Engineering, Vellore Institute of Technology, Chennai 600127 Tamil Nadu, India.

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|February 24, 2026
PubMed
Summary

This study compared Electroencephalography (EEG) preprocessing methods for cognitive analysis. TRIM-EEG proved more effective than Variational Decomposition Mode (VMD), enabling accurate differentiation between sports and non-sports groups.

Keywords:
Cognitive abilityElectroencephalography (EEG)Machine learningPreprocessing

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Signal Processing

Background:

  • Electroencephalography (EEG) is crucial for analyzing cognitive processes.
  • Standard EEG preprocessing pipelines can be complex and vary significantly.
  • Optimizing EEG preprocessing is essential for reliable cognitive research, especially when comparing distinct groups like athletes and non-athletes.

Purpose of the Study:

  • To compare two distinct EEG preprocessing pipelines: TRIM-EEG and Variational Decomposition Mode (VMD).
  • To identify the most effective EEG preprocessing strategy for analyzing cognitive differences between sports and non-sports groups.
  • To evaluate the performance of machine learning classifiers on preprocessed EEG data for group differentiation.

Main Methods:

  • Method A (TRIM-EEG): Employed notch filtering, Independent Component Analysis (ICA) for ocular artifact removal, and bandpass filtering.
  • Method B (VMD): Utilized Variational Decomposition Mode (VMD) with parameter optimization via Shuffled Frog Leaping Algorithm (SfLA) and spectral kurtosis.
  • Performance comparison using error metrics, followed by feature analysis with classifiers like Support Vector Machine (SVM) and Random Forest (RF).

Main Results:

  • TRIM-EEG demonstrated superior performance based on error metrics compared to the VMD method.
  • The TRIM-EEG preprocessed data allowed for effective differentiation between sports and non-sports groups.
  • Support Vector Machine (SVM) and Random Forest (RF) classifiers achieved high accuracy (93.33%) in distinguishing the groups.

Conclusions:

  • The TRIM-EEG preprocessing pipeline is a highly effective technique for cognitive analysis using EEG.
  • TRIM-EEG offers a reliable method for preprocessing EEG signals, particularly for studies involving group comparisons.
  • The findings support the use of TRIM-EEG in future cognitive processing research and brain-computer interface applications.