Evaluation of a P300-Based Brain-Machine Interface for a Robotic Hand-Orthosis Control

Jonathan Delijorge; Omar Mendoza-Montoya; Jose L Gordillo; Ricardo Caraza; Hector R Martinez; Javier M Antelis

doi:10.3389/fnins.2020.589659

Evaluation of a P300-Based Brain-Machine Interface for a Robotic Hand-Orthosis Control

Front Neurosci. 2020 Nov 27:14:589659. doi: 10.3389/fnins.2020.589659. eCollection 2020.

Authors

Jonathan Delijorge¹, Omar Mendoza-Montoya¹, Jose L Gordillo¹, Ricardo Caraza², Hector R Martinez², Javier M Antelis¹

Affiliations

¹ Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Monterrey, Mexico.
² Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Mexico.

Abstract

This work presents the design, implementation, and evaluation of a P300-based brain-machine interface (BMI) developed to control a robotic hand-orthosis. The purpose of this system is to assist patients with amyotrophic lateral sclerosis (ALS) who cannot open and close their hands by themselves. The user of this interface can select one of six targets, which represent the flexion-extension of one finger independently or the movement of the five fingers simultaneously. We tested offline and online our BMI on eighteen healthy subjects (HS) and eight ALS patients. In the offline test, we used the calibration data of each participant recorded in the experimental sessions to estimate the accuracy of the BMI to classify correctly single epochs as target or non-target trials. On average, the system accuracy was 78.7% for target epochs and 85.7% for non-target trials. Additionally, we observed significant P300 responses in the calibration recordings of all the participants, including the ALS patients. For the BMI online test, each subject performed from 6 to 36 attempts of target selections using the interface. In this case, around 46% of the participants obtained 100% of accuracy, and the average online accuracy was 89.83%. The maximum information transfer rate (ITR) observed in the experiments was 52.83 bit/min, whereas that the average ITR was 18.13 bit/min. The contributions of this work are the following. First, we report the development and evaluation of a mind-controlled robotic hand-orthosis for patients with ALS. To our knowledge, this BMI is one of the first P300-based assistive robotic devices with multiple targets evaluated on people with ALS. Second, we provide a database with calibration data and online EEG recordings obtained in the evaluation of our BMI. This data is useful to develop and compare other BMI systems and test the processing pipelines of similar applications.

Keywords: P300; amyotrophic lateral sclerosis; artificial intelligence; brain-machine interface; electroencephalography; evoked potentials; hand-orthosis; signal processing.