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Updated: Sep 4, 2025

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Continuous Decoding of Hand Movement From EEG Signals Using Phase-Based Connectivity Features.

Seyyed Moosa Hosseini¹, Vahid Shalchyan¹

¹Neuroscience and Neuroengineering Research Lab, Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran.

Frontiers in Human Neuroscience

|July 18, 2022

Summary

This summary is machine-generated.

This study shows that using brain connectivity features, like phase-locking value (PLV) and magnitude-squared coherence (MSC), improves the accuracy of decoding continuous hand movements from electroencephalography (EEG) signals for brain-computer interfaces (BCIs). This offers a safer, non-invasive approach for neuroprosthetics.

Keywords:

brain computer interface (BCI)electroencephalography (EEG)magnitude-squared coherence (MSC)multiple linear regression (MLR)phase-locking value (PLV)trajectory decoding

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Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Brain-computer interfaces (BCIs) aim to restore function for individuals with motor disabilities using brain signals.
Non-invasive electroencephalography (EEG) offers a safer alternative to invasive methods for BCI.
Continuous decoding of movement parameters is crucial for practical neuroprosthetic applications.

Purpose of the Study:

To investigate the effectiveness of phase-based brain connectivity features for decoding continuous hand movements from EEG signals.
To compare the performance of connectivity features against amplitude- and phase-based methods in BCI trajectory decoding.

Main Methods:

Collected EEG data from healthy subjects performing a 2D hand movement task.
Extracted phase-based connectivity features: phase-locking value (PLV) and magnitude-squared coherence (MSC).
Utilized multiple linear regression (MLR) with a brute-force channel selection for decoding hand positions.

Main Results:

Regression models using PLV and MSC achieved significant correlations (0.43 and 0.42, respectively) between predicted and actual hand trajectories.
Delta and alpha frequency bands showed the most significant contribution to decoding accuracy.
Connectivity-based models outperformed amplitude- or phase-only feature extraction methods (p < 0.05).

Conclusions:

Phase-based connectivity features (PLV, MSC) are effective for continuous decoding of hand movements using EEG.
Brain connectivity analysis offers a promising avenue for enhancing the accuracy of BCI trajectory decoders.
This non-invasive approach holds potential for improving neuroprosthetic control and restoring lost motor functions.