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Updated: Jan 13, 2026

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Lower-Limb Motor Imagery Recognition Prototype Based on EEG Acquisition, Filtering, and Machine Learning-Based

Sonia Rocío Moreno-Castelblanco1, Manuel Andrés Vélez-Guerrero1, Mauro Callejas-Cuervo1

  • 1Software Research Group, Universidad Pedagógica y Tecnológica de Colombia, Tunja 150002, Colombia.

Sensors (Basel, Switzerland)
|October 29, 2025
PubMed
Summary
This summary is machine-generated.

A novel single-channel electroencephalography (EEG) system effectively detects lower-limb motor imagery (MI) using machine learning. This portable prototype shows potential for accessible rehabilitation and assistive device control.

Keywords:
Brain–Computer Interface (BCI)ButterworthRandom ForestSavitzky–Golayartificial intelligenceelectroencephalography (EEG)filteringlower-limbmachine learningmotor imagery (MI)pattern detectionsignal processing

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Brain-computer interface (BCI) research utilizes electroencephalography (EEG) to detect motor imagery (MI).
  • Complex multichannel EEG systems limit accessibility in non-specialized settings.
  • There is a need for portable and user-friendly systems for MI detection.

Purpose of the Study:

  • To develop and validate a single-channel EEG prototype for detecting lower-limb motor imagery.
  • To assess the feasibility of using a portable EEG system in environments outside specialized centers.
  • To evaluate machine learning algorithms for classifying MI patterns from single-channel EEG data.

Main Methods:

  • A wireless, single-channel EEG acquisition system was designed.
  • Raw EEG signals were processed using digital filters, specifically Savitzky-Golay filtering.
  • Machine learning algorithms, including Random Forest, were employed for MI pattern detection.
  • Experimental validation involved participants performing resting, MI, and movement tasks in a controlled laboratory setting.
  • Performance was evaluated using accuracy and F1-score with five-fold cross-validation.

Main Results:

  • The combined Savitzky-Golay filtering and Random Forest classifier achieved the highest performance.
  • The system demonstrated an accuracy of 87.36% ± 4% and an F1-score of 87.18% ± 3.8%.
  • These results confirm the detectability of MI patterns even with limited spatial resolution from a single channel.

Conclusions:

  • A single-channel, portable EEG prototype can effectively recognize lower-limb motor imagery.
  • The developed system offers portability and noise resilience, suitable for non-specialized environments.
  • The prototype holds potential for applications in research, clinical rehabilitation, and assistive device control.