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

## TL;DR

A portable EEG system using machine learning can detect lower-limb motor imagery with high accuracy, offering potential for use in non-specialized settings.

## Contribution

This work demonstrates a portable, single-channel EEG system for lower-limb motor imagery recognition with effective AI-based filtering and classification.

## Key findings

- Combining Savitzky–Golay filtering with a Random Forest classifier achieved 87.36% accuracy in detecting lower-limb motor imagery.
- The prototype system showed strong performance in a controlled lab setting with resting, MI, and movement tasks.
- The system's portability and noise resilience make it suitable for use in non-specialized environments.

## Abstract

Advances in brain–computer interface (BCI) research have explored various strategies for acquiring and processing electroencephalographic (EEG) signals to detect motor imagery (MI) activities. However, the complexity of multichannel clinical systems and processing techniques can limit their accessibility outside specialized centers, where complex setups are not feasible. This paper presents a proof-of-concept prototype of a single-channel EEG acquisition and processing system designed to identify lower-limb motor imagery. The proposed proof-of-concept prototype enables the wireless acquisition of raw EEG values, signal processing using digital filters, and the detection of MI patterns using machine learning algorithms. Experimental validation in a controlled laboratory with participants performing resting, MI, and movement tasks showed that the best performance was obtained by combining Savitzky–Golay filtering with a Random Forest classifier, reaching 87.36% ± 4% accuracy and an F1-score of 87.18% ± 3.8% under five-fold cross-validation. These findings confirm that, despite limited spatial resolution, MI patterns can be detected using appropriate AI-based filtering and classification. The novelty of this work lies in demonstrating that a single-channel, portable EEG prototype can be effectively used for lower-limb MI recognition. The portability and noise resilience achieved with the prototype highlight its potential for research, clinical rehabilitation, and assistive device control in non-specialized environments.