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Decoding Upper-Limb Motor Imagery from EEG Signals: A Systematic Review of Methods and Applications.

Zhendong Su^1,2, Kok Beng Gan³, Kok Swee Sim⁴

¹Department of Electrical, Electronic & Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, UKM Bangi, Selangor, Malaysia.

Annals of Biomedical Engineering

|June 8, 2026

Summary

This review examines brain-computer interfaces (BCIs) using electroencephalography (EEG) for motor imagery (MI) decoding. It highlights challenges in classifying similar limb movements and explores current methods for improved accuracy in upper-limb BCIs.

Keywords:

Brain–computer interface EEG Motor imagery Signal processing

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Published on: July 26, 2013

Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Brain-computer interfaces (BCIs) show promise in healthcare, industry, and entertainment.
Electroencephalography (EEG)-based motor imagery (MI) is a common BCI paradigm, especially in medical applications.
EEG signals' low signal-to-noise ratio and non-stationarity limit current decoding accuracy, particularly for fine motor distinctions.

Purpose of the Study:

To review upper-limb MI-EEG classification and applications from the last five years.
To provide a comprehensive overview of decoding hand motor imagery from MI-EEG signals.
To examine challenges in practical BCI applications and offer guidance for researchers.

Main Methods:

Systematic investigation of state-of-the-art decoding methods for upper-limb MI-EEG.
Comparative analysis of method performance and underlying assumptions.
Literature review and data extraction from relevant studies.

Main Results:

Current decoding accuracy for upper-limb MI-EEG remains suboptimal, especially for movements involving the same limb.
Various advanced methods exist, but their translation to real-world applications faces limitations.
Significant progress has been made, but challenges persist in achieving high decoding precision.

Conclusions:

Further research is needed to enhance the accuracy and robustness of EEG-based BCIs for motor imagery.
Translating decoding methods into practical applications requires addressing signal quality and non-stationarity issues.
This review provides insights into current trends, challenges, and future directions in upper-limb MI-EEG decoding.