Low-Cost Robotic Guide Based on a Motor Imagery Brain-Computer Interface for Arm Assisted Rehabilitation

Overview

Journal Int J Environ Res Public Health

Publisher MDPI

Specialties Environmental Health
Public Health

Date 2020 Jan 25

PMID 31973155

Citations 5

Authors

Eduardo Quiles

Ferran Suay

Gemma Candela

Nayibe Chio

Manuel Jimenez

Leandro Alvarez-Kurogi

Affiliations

Soon will be listed here.

Abstract

Motor imagery has been suggested as an efficient alternative to improve the rehabilitation process of affected limbs. In this study, a low-cost robotic guide is implemented so that linear position can be controlled via the user's motor imagination of movement intention. The patient can use this device to move the arm attached to the guide according to their own intentions. The first objective of this study was to check the feasibility and safety of the designed robotic guide controlled via a motor imagery (MI)-based brain-computer interface (MI-BCI) in healthy individuals, with the ultimate aim to apply it to rehabilitation patients. The second objective was to determine which are the most convenient MI strategies to control the different assisted rehabilitation arm movements. The results of this study show a better performance when the BCI task is controlled with an action-action MI strategy versus an action-relaxation one. No statistically significant difference was found between the two action-action MI strategies.

Citing Articles

EEG-Based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review.

Saibene A, Caglioni M, Corchs S, Gasparini F Sensors (Basel). 2023; 23(5).

PMID: 36905004 PMC: 10007053. DOI: 10.3390/s23052798.

Cross-Platform Implementation of an SSVEP-Based BCI for the Control of a 6-DOF Robotic Arm.

Quiles E, Dadone J, Chio N, Garcia E Sensors (Basel). 2022; 22(13).

PMID: 35808498 PMC: 9269816. DOI: 10.3390/s22135000.

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain-Computer Interfaces: A Systematic Literature Review.

Jamil N, Belkacem A, Ouhbi S, Lakas A Sensors (Basel). 2021; 21(14).

PMID: 34300492 PMC: 8309653. DOI: 10.3390/s21144754.

Evaluating the Effect of Stimuli Color and Frequency on SSVEP.

Duart X, Quiles E, Suay F, Chio N, Garcia E, Morant F Sensors (Basel). 2020; 21(1).

PMID: 33375441 PMC: 7796402. DOI: 10.3390/s21010117.

A BCI System Based on Motor Imagery for Assisting People with Motor Deficiencies in the Limbs.

Attallah O, Abougharbia J, Tamazin M, Nasser A Brain Sci. 2020; 10(11).

PMID: 33212777 PMC: 7697603. DOI: 10.3390/brainsci10110864.

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