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Updated: Sep 18, 2025

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EEG-Driven Arm Movement Decoding: Combining Connectivity and Amplitude Features for Enhanced Brain-Computer Interface

Hamidreza Darvishi1, Ahmadreza Mohammadi1, Mohammad Hossein Maghami2

  • 1Department of Cognitive Psychology, Institute for Cognitive Science Studies (ICSS), Tehran 16583-44575, Iran.

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|June 26, 2025
PubMed
Summary
This summary is machine-generated.

Combining amplitude and phase-based brain signal features significantly improves brain-computer interface (BCI) accuracy for decoding arm movements. This enhanced BCI approach offers better control for neuroprosthetic systems.

Keywords:
ReliefFbrain–computer interfaceelectroencephalographyelectromyographyfeature selectionfilter bank common spatial patternsmovement decodingneural networkphase-locking value

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Brain-computer interfaces (BCIs) translate electroencephalography (EEG) signals into control commands, aiding individuals with motor impairments.
  • Traditional BCIs often analyze only amplitude or simple connectivity, overlooking the dynamic, multi-faceted nature of brain activity during movement.

Purpose of the Study:

  • To investigate if fusing amplitude-based (FBCSP) and phase-based (PLV) EEG features enhances movement decoding accuracy.
  • To identify optimal feature subsets and frequency bands for improved BCI performance.

Main Methods:

  • EEG signals from ten healthy subjects performing arm movements were recorded.
  • Amplitude (FBCSP) and phase-locking value (PLV) features were extracted and fused.
  • The ReliefF algorithm performed feature selection, followed by a feedforward neural network for decoding.

Main Results:

  • The fused feature approach achieved high accuracy: Pearson correlation 0.829, R-squared 0.675, and RMSE 0.579.
  • Significant contributions were observed from both FBCSP and PLV features, particularly in the 4-8 Hz and 24-28 Hz bands.
  • Data-driven feature selection further optimized the decoding model.

Conclusions:

  • Fusion of amplitude and phase-based EEG features, combined with informed feature selection, substantially improves arm movement decoding.
  • This advanced BCI methodology holds promise for developing more robust and effective neuroprosthetic control systems.