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Shoulder Flexion Pre-Movement Recognition Through Subject-Specific Brain Regions to Command an Upper Limb

Yunier Prieur-Coloma, Denis Delisle-Rodriguez, Leondry Mayeta-Revilla

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|October 6, 2020

Summary

This study introduces two brain-computer interfaces (BCIs) for recognizing shoulder movements using Electroencephalography (EEG) signals. The developed BCIs effectively identify pre-movement states, potentially aiding neuro-rehabilitation.

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Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Brain-computer interfaces (BCIs) offer potential for motor rehabilitation.
Accurate recognition of pre-movement states is crucial for effective BCI control.
Electroencephalography (EEG) is a non-invasive method for capturing brain activity.

Purpose of the Study:

To develop and evaluate two novel BCIs for shoulder pre-movement recognition.
To compare manual versus automated (NMF) EEG channel selection strategies.
To identify optimal frequency bands for discriminating pre-movement and rest states.

Main Methods:

Two BCI approaches were implemented: manual EEG channel selection and subject-specific selection using Non-negative Matrix Factorization (NMF).
Spatial features were extracted from filtered EEG signals using Riemannian covariance matrices.
Linear Discriminant Analysis (LDA) was employed for classification of pre-movement and rest states.
Experiments were conducted on 21 healthy subjects across various frequency bands.

Main Results:

The automated BCI successfully identified EEG channels over the contralateral limb.
Enhanced pre-movement recognition accuracy was achieved (ACC = 71.39 ± 12.68%, κ = 0.43 ± 0.25%).
The study identified optimal frequency ranges for shoulder pre-movement detection.

Conclusions:

The proposed BCIs demonstrate efficacy in recognizing shoulder pre-movement states.
Automated EEG channel selection based on cortical relevance improves BCI performance.
These findings suggest potential benefits for neuro-rehabilitation applications.