An EEG-based BCI platform to improve arm reaching ability of chronic stroke patients by means of an operant learning training with a contingent force feedback

Giulia Cisotto, Silvano Pupolin, Marianna Cavinato, Francesco Piccione

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The Brain Computer Interface platform described in this paper was implemented to enhance neuroplasticity of a stroke-damaged brain in order to promote recovery of motor functions like reaching, fundamentally important in a healthy daily life. To this scope a closed-loop between the stroke patients' brain and a robotic arm is established by means of a real-time identification of the cerebral activity related to the movement and its transformation in a force feedback delivered by the robot. In particular, an operant-learning strategy is employed: while patients are performing the motor task they receive a feedback of their neural activity. If the latter agrees with the expected neurophysiological hypothesis, they are helped by the robotic arm in completing the task. The method trains patients to control the modulation of sensorimotor rhythms of their perilesional area and, at the same time, it should induce them to associate that modulation to the reaching movement. In this way, the modification of the neural activity becomes an alternative tool for controlling the impaired reaching ability bypassing the damaged brain area. Preliminary encouraging results were found in both the two first patients recruited in the program.

Original languageEnglish
Pages (from-to)114-134
Number of pages21
JournalInternational Journal of E-Health and Medical Communications
Volume5
Issue number1
DOIs
Publication statusPublished - 2014 Jan 1

Fingerprint

Aptitude
Electroencephalography
Brain
Robotic arms
Stroke
Learning
Feedback
Robotics
Modulation
Brain computer interface
Brain-Computer Interfaces
Neuronal Plasticity
Recovery of Function
Robots
Recovery

Keywords

  • BCI
  • EEG
  • Neuroplasticity
  • Operant-learning
  • Reaching
  • Rehabilitation
  • Stroke

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

Cite this

An EEG-based BCI platform to improve arm reaching ability of chronic stroke patients by means of an operant learning training with a contingent force feedback. / Cisotto, Giulia; Pupolin, Silvano; Cavinato, Marianna; Piccione, Francesco.

In: International Journal of E-Health and Medical Communications, Vol. 5, No. 1, 01.01.2014, p. 114-134.

Research output: Contribution to journalArticle

@article{4463812045ac486b8d156c180fac1a91,
title = "An EEG-based BCI platform to improve arm reaching ability of chronic stroke patients by means of an operant learning training with a contingent force feedback",
abstract = "The Brain Computer Interface platform described in this paper was implemented to enhance neuroplasticity of a stroke-damaged brain in order to promote recovery of motor functions like reaching, fundamentally important in a healthy daily life. To this scope a closed-loop between the stroke patients' brain and a robotic arm is established by means of a real-time identification of the cerebral activity related to the movement and its transformation in a force feedback delivered by the robot. In particular, an operant-learning strategy is employed: while patients are performing the motor task they receive a feedback of their neural activity. If the latter agrees with the expected neurophysiological hypothesis, they are helped by the robotic arm in completing the task. The method trains patients to control the modulation of sensorimotor rhythms of their perilesional area and, at the same time, it should induce them to associate that modulation to the reaching movement. In this way, the modification of the neural activity becomes an alternative tool for controlling the impaired reaching ability bypassing the damaged brain area. Preliminary encouraging results were found in both the two first patients recruited in the program.",
keywords = "BCI, EEG, Neuroplasticity, Operant-learning, Reaching, Rehabilitation, Stroke",
author = "Giulia Cisotto and Silvano Pupolin and Marianna Cavinato and Francesco Piccione",
year = "2014",
month = "1",
day = "1",
doi = "10.4018/ijehmc.2014010107",
language = "English",
volume = "5",
pages = "114--134",
journal = "International Journal of E-Health and Medical Communications",
issn = "1947-315X",
publisher = "IGI Global Publishing",
number = "1",

}

TY - JOUR

T1 - An EEG-based BCI platform to improve arm reaching ability of chronic stroke patients by means of an operant learning training with a contingent force feedback

AU - Cisotto, Giulia

AU - Pupolin, Silvano

AU - Cavinato, Marianna

AU - Piccione, Francesco

PY - 2014/1/1

Y1 - 2014/1/1

N2 - The Brain Computer Interface platform described in this paper was implemented to enhance neuroplasticity of a stroke-damaged brain in order to promote recovery of motor functions like reaching, fundamentally important in a healthy daily life. To this scope a closed-loop between the stroke patients' brain and a robotic arm is established by means of a real-time identification of the cerebral activity related to the movement and its transformation in a force feedback delivered by the robot. In particular, an operant-learning strategy is employed: while patients are performing the motor task they receive a feedback of their neural activity. If the latter agrees with the expected neurophysiological hypothesis, they are helped by the robotic arm in completing the task. The method trains patients to control the modulation of sensorimotor rhythms of their perilesional area and, at the same time, it should induce them to associate that modulation to the reaching movement. In this way, the modification of the neural activity becomes an alternative tool for controlling the impaired reaching ability bypassing the damaged brain area. Preliminary encouraging results were found in both the two first patients recruited in the program.

AB - The Brain Computer Interface platform described in this paper was implemented to enhance neuroplasticity of a stroke-damaged brain in order to promote recovery of motor functions like reaching, fundamentally important in a healthy daily life. To this scope a closed-loop between the stroke patients' brain and a robotic arm is established by means of a real-time identification of the cerebral activity related to the movement and its transformation in a force feedback delivered by the robot. In particular, an operant-learning strategy is employed: while patients are performing the motor task they receive a feedback of their neural activity. If the latter agrees with the expected neurophysiological hypothesis, they are helped by the robotic arm in completing the task. The method trains patients to control the modulation of sensorimotor rhythms of their perilesional area and, at the same time, it should induce them to associate that modulation to the reaching movement. In this way, the modification of the neural activity becomes an alternative tool for controlling the impaired reaching ability bypassing the damaged brain area. Preliminary encouraging results were found in both the two first patients recruited in the program.

KW - BCI

KW - EEG

KW - Neuroplasticity

KW - Operant-learning

KW - Reaching

KW - Rehabilitation

KW - Stroke

UR - http://www.scopus.com/inward/record.url?scp=84927757594&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84927757594&partnerID=8YFLogxK

U2 - 10.4018/ijehmc.2014010107

DO - 10.4018/ijehmc.2014010107

M3 - Article

AN - SCOPUS:84927757594

VL - 5

SP - 114

EP - 134

JO - International Journal of E-Health and Medical Communications

JF - International Journal of E-Health and Medical Communications

SN - 1947-315X

IS - 1

ER -