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Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients
Published on: September 1, 2023
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A multiple session dataset of NIRS recordings from stroke patients controlling brain-computer interface.
Mikhail R Isaev1, Olesya A Mokienko1,2, Roman Kh Lyukmanov2
1Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, Butlerova St., 5A, Moscow, 117485, Russia.
Scientific Data
|October 25, 2024
Summary
This study offers an open dataset of over 50 hours of near-infrared spectroscopy (NIRS) data from 15 stroke patients undergoing brain-computer interface (BCI) training. The dataset aids in developing and evaluating NIRS signal processing for cerebrovascular accident recovery.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Rehabilitation Science
Background:
- Stroke, or cerebrovascular accidents, significantly impact motor function.
- Brain-computer interfaces (BCIs) offer potential for motor rehabilitation.
- Near-infrared spectroscopy (NIRS) is a non-invasive neuroimaging technique for monitoring brain activity.
Purpose of the Study:
- To present a comprehensive, open-access dataset of NIRS recordings from stroke patients.
- To facilitate the development and validation of NIRS-based signal processing and analysis techniques for BCI applications in stroke rehabilitation.
Main Methods:
- Collected over 50 hours of NIRS data from 15 stroke patients.
- Patients performed motor imagery tasks to control a BCI with visual feedback.
- Recorded experimental data, patient demographics, clinical scores (ARAT, Fugl-Meyer), and online BCI performance.
Main Results:
- A substantial dataset comprising 237 motor imagery BCI sessions is now publicly available.
- Includes detailed patient profiles, clinical assessments, and real-time BCI performance metrics.
- Preliminary analysis of hemodynamic responses is provided.
Conclusions:
- The presented NIRS dataset is a valuable resource for researchers in neurorehabilitation.
- It enables the evaluation of advanced signal processing algorithms for stroke patients using BCIs.
- This resource can accelerate the translation of NIRS-BCI technology into clinical practice.
