BCI-Based Control for Ankle Exoskeleton T-FLEX: Comparison of Visual and Haptic Feedback With Stroke Survivors
NCT ID: NCT04995367
Last Updated: 2021-08-06
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
NA
5 participants
INTERVENTIONAL
2021-03-15
2021-05-15
Brief Summary
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Detailed Description
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Conditions
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Study Design
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NA
SINGLE_GROUP
TREATMENT
NONE
Study Groups
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Implementation of a BCI system integrated to the T-FLEX lower-limb exoskeleton in post-stroke
The participants will carry out tests for the evaluation of the functionality of the BCI system integrated to the T-FLEX device. The test consists of 1 session that includes four conditional experiments. Real Movement, Continuous Stationary Therapy, Motor Imagery Detection with Visual Stimulation, and Motor Imagery Detection with Tactile Stimulation.
Implementation of a BCI system integrated to the T-FLEX lower-limb exoskeleton in post-stroke patients.
The participants will carry out 4 tasks with a BCI system integrated to the T-FLEX device. The task consists of 1 session that includes 4 conditional experiments: Active ankle movement, passive ankle movement, Motor Imagery Detection with Visual cue, and Motor Imagery Detection with Tactile Stimulation and visual cue.
Interventions
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Implementation of a BCI system integrated to the T-FLEX lower-limb exoskeleton in post-stroke patients.
The participants will carry out 4 tasks with a BCI system integrated to the T-FLEX device. The task consists of 1 session that includes 4 conditional experiments: Active ankle movement, passive ankle movement, Motor Imagery Detection with Visual cue, and Motor Imagery Detection with Tactile Stimulation and visual cue.
Eligibility Criteria
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Inclusion Criteria
* Hemorrhagic or ischemic stroke
* A minimum of six months after the acute infarction/onset of the disease
* Full passive range of motion in lower extremity or at least at neutral position
* Be able to stand freely
* Be able to walk with or without aid for at least 20 meters in less than 2 minutes
Exclusion Criteria
* Epilepsy
* Weight over 100 kg
* No cognitive ability to follow the study instructions
* Pregnancy
* Use of implanted devices
* Instable lower extremity joints or fixed contracture
18 Years
70 Years
ALL
No
Sponsors
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Colombian School of Engineering Julio Garavito
OTHER
Corporación de Rehabilitación Club de Leones Cruz del Sur
OTHER
Responsible Party
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Principal Investigators
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Asterio H Andrade, PhD
Role: STUDY_CHAIR
Rehabilitation Center Club de Leones Cruz del Sur
Locations
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Corporación de Rehabilitación Club de Leones Cruz del Sur
Punta Arenas, Region of Magallanes, Chile
Countries
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References
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Verma R, Arya KN, Sharma P, Garg RK. Understanding gait control in post-stroke: implications for management. J Bodyw Mov Ther. 2012 Jan;16(1):14-21. doi: 10.1016/j.jbmt.2010.12.005. Epub 2010 Dec 30.
Needham DM, Truong AD, Fan E. Technology to enhance physical rehabilitation of critically ill patients. Crit Care Med. 2009 Oct;37(10 Suppl):S436-41. doi: 10.1097/CCM.0b013e3181b6fa29.
Chen G, Chan CK, Guo Z, Yu H. A review of lower extremity assistive robotic exoskeletons in rehabilitation therapy. Crit Rev Biomed Eng. 2013;41(4-5):343-63. doi: 10.1615/critrevbiomedeng.2014010453.
Gomez-Vargas D, Ballen-Moreno F, Barria P, Aguilar R, Azorin JM, Munera M, Cifuentes CA. The Actuation System of the Ankle Exoskeleton T-FLEX: First Use Experimental Validation in People with Stroke. Brain Sci. 2021 Mar 24;11(4):412. doi: 10.3390/brainsci11040412.
He Y, Eguren D, Azorin JM, Grossman RG, Luu TP, Contreras-Vidal JL. Brain-machine interfaces for controlling lower-limb powered robotic systems. J Neural Eng. 2018 Apr;15(2):021004. doi: 10.1088/1741-2552/aaa8c0.
Ortiz M, Ianez E, Contreras-Vidal JL, Azorin JM. Analysis of the EEG Rhythms Based on the Empirical Mode Decomposition During Motor Imagery When Using a Lower-Limb Exoskeleton. A Case Study. Front Neurorobot. 2020 Aug 27;14:48. doi: 10.3389/fnbot.2020.00048. eCollection 2020.
Ma T, Li H, Deng L, Yang H, Lv X, Li P, Li F, Zhang R, Liu T, Yao D, Xu P. The hybrid BCI system for movement control by combining motor imagery and moving onset visual evoked potential. J Neural Eng. 2017 Apr;14(2):026015. doi: 10.1088/1741-2552/aa5d5f. Epub 2017 Feb 1.
Thomas E, Dyson M, Clerc M. An analysis of performance evaluation for motor-imagery based BCI. J Neural Eng. 2013 Jun;10(3):031001. doi: 10.1088/1741-2560/10/3/031001. Epub 2013 May 3.
Ortiz M, Ferrero L, Ianez E, Azorin JM, Contreras-Vidal JL. Sensory Integration in Human Movement: A New Brain-Machine Interface Based on Gamma Band and Attention Level for Controlling a Lower-Limb Exoskeleton. Front Bioeng Biotechnol. 2020 Sep 3;8:735. doi: 10.3389/fbioe.2020.00735. eCollection 2020.
Other Identifiers
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CorporacionRCLCS0005
Identifier Type: -
Identifier Source: org_study_id
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