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Automatic Selection of Control Features for Electroencephalography-Based Brain-Computer Interface Assisted Motor
Emma Colamarino1,2, Floriana Pichiorri3, Jlenia Toppi4,3
1Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Via Ariosto 25, 00185, Rome, Italy. emma.colamarino@uniroma1.it.
Brain Topography
|January 19, 2022
Summary
A new algorithm, GUIDER, automatically selects electroencephalogram features for Brain-Computer Interfaces (BCIs) in stroke rehabilitation. This approach matches expert neurophysiologist performance, potentially aiding therapists in BCI use.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Rehabilitation Science
Background:
- Brain-Computer Interfaces (BCIs) utilizing sensorimotor rhythms show promise for upper limb motor rehabilitation post-stroke.
- Effective BCI performance relies on selecting appropriate electroencephalogram (EEG) features reflecting sensorimotor activation.
- Current feature selection often requires expert neurophysiologist input, limiting broader clinical adoption.
Purpose of the Study:
- To introduce and evaluate GUIDER, a novel algorithm for automatic EEG feature selection in BCI-based stroke rehabilitation.
- To assess GUIDER's ability to integrate neurophysiological knowledge and rehabilitative principles into feature selection.
- To compare the performance of GUIDER with manual feature selection by expert neurophysiologists.
Main Methods:
- Development of the GUIDER algorithm for automated EEG feature selection.
- Testing GUIDER on an EEG dataset from 13 subacute stroke participants undergoing BCI rehabilitation.
- Comparative analysis of feature selection and classification performance between GUIDER and expert manual selection.
Main Results:
- The GUIDER algorithm achieved performance comparable to expert neurophysiologists in both feature selection and classification tasks.
- Preliminary findings indicate that automated feature selection can reliably replicate expert choices.
- The algorithm demonstrated potential for supporting non-expert users like therapists and clinicians.
Conclusions:
- The GUIDER algorithm offers a reproducible and automated approach to EEG feature selection for BCI rehabilitation.
- This tool can potentially empower clinicians and therapists, facilitating wider implementation of BCI-based stroke recovery.
- GUIDER may enhance the accessibility and effectiveness of BCI technology in clinical settings.
