Non-invasive BCI-controlled Assistive Devices

Healthy Clinical Trial

Official title:

Non-invasive Brain-computer Interfaces for Control of Assistive Devices

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05183152
• Other study ID #: 2020030073
• Status: Recruiting
• Phase: N/A
• Start date: June 16, 2021
• Est. completion date: December 30, 2023

Study information

• Verified date: December 2022
• Source: University of Texas at Austin
• Contact: Jose del R. Millan, PhD
• Phone: 512-232-8111
• Email: jose.millan[@]austin.utexas.edu
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

A brain-computer interface (BCI) decodes users' behavioral intentions or mental states directly from their brain activity, thus allowing operation of devices without requiring any overt motor action. One major modality for BCI control is based on motor imagery (MI), which is the mental rehearsal of the kinesthetics of a movement without actually performing it. MI-based BCIs translate motor intents into control commands for external devices. A major challenge in such BCIs is differentiating MI patterns corresponding to fine hand movements of the same limb from non-invasive EEG recordings with low spatial resolution since the cortical sources responsible for these movements are overlapping. In this study, the investigators hypothesize that neuromuscular electrical stimulation (NMES) applied contingent to the voluntary activation of the primary motor cortex through MI can help differentiate patterns of activity associated with different hand movements of the same limb by consistently recruiting the separate neural pathways associated with each of the movements within a closed-loop BCI setup. This is expected to be associated with neuroplastic changes at the cortical or corticospinal levels.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 40
• Est. completion date: December 30, 2023
• Est. primary completion date: December 30, 2023
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years to 80 Years

Eligibility

Inclusion Criteria:

• Able-bodied participants:
• good general health
• normal or corrected vision
• no history of neurological/psychiatric disease
• ability to read and understand English (Research Personnel do not speak Spanish)
• Subjects with motor disabilities
• motor deficits due to: unilateral and bilateral stroke / spinal cord injury / motor neuron diseases (i.e. amyotrophic lateral sclerosis, spino-cerebellar ataxia, multiple sclerosis) / muscular diseases (i.e. myopathy) / traumatic or neurological pain / movement disorders (i.e. cerebral palsy) / orthopedic / traumatic brain injury / brain tumors
• normal or corrected vision
• ability to read and understand English (Research Personnel do not speak Spanish)
• ability to provide informed consent

Exclusion Criteria:

• Subjects with motor disabilities
• short attentional spans or cognitive deficits that prevent to remain concentrated during the whole experimental session
• heavy medication affecting the central nervous system (including vigilance)
• concomitant serious illness (e.g., metabolic disorders)
• All participants
• factors hindering EEG/EMG acquisition and FES/tdCS/tACS delivery (e.g., skin infection, wounds, dermatitis, metal implants under electrodes)
• criteria identified in safety guidelines for MRI and TMS, in particular metallic implants

Related Conditions & MeSH terms

• Amyotrophic Lateral Sclerosis
• Brain Injuries
• Brain Injuries, Traumatic
• Disease
• Healthy
• Motor Disorders
• Motor Neuron Disease
• Movement Disorders
• Muscular Diseases
• Spinal Cord Injuries
• Stroke
• Traumatic Brain Injury
• Wounds and Injuries

Intervention

Device:
• NMES-BCI
Electroenceauphalography (EEG) signals will be recorded from subjects as they perform cued tasks for flexing/extending their non-dominant hand. The signals will be processed and classified in real-time using machine learning algorithms to trigger electrical stimulation on the flexors/extensors of the targeted arm contingent to the detection of a subject-specific flexion/extension EEG patterns.
• Visual-BCI
Electroenceauphalography (EEG) signals will be recorded from subjects as they perform cued tasks for flexing/extending their non-dominant hand. The signals will be processed and classified in real-time using machine learning algorithms to control the right/left movement of a bar on a computer screen. The bar feedback is contingent to the detection of a subject-specific flexion/extension EEG patterns.

Locations

Country	Name	City	State
United States	The University of Texas at Austin	Austin	Texas

Sponsors (1)

Lead Sponsor	Collaborator
University of Texas at Austin

Country where clinical trial is conducted

United States, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Change in the BCI command delivery accuracy	The command delivery accuracy reflects the level of control of the subject when using the BCI. It measures the percentage of trials in which the subject-specific classifier that is used to differentiate the different imagined movements could accumulate enough evidence to support the presence of EEG patterns specifically associated with the imagined movement in those trials. The score is 0-100, and the higher the value, the better the outcome.	Difference between the week before versus after each intervention
Primary	Change in fMRI activation for different imagined movements	The clusters of significant activation during MI of different movements would be more separable; The activation associated with different MI tasks would be more discriminable	Difference between the week before versus after each intervention
Secondary	Stability and separability of Motor Imagery features	The features corresponding to different motor imagery tasks become more separable and are more stable at the end of the intervention.	Difference between the week before versus after each intervention
Secondary	Changes in motor-evoked potential amplitude	Continuous measure, the higher the better	Difference between the week before versus after each intervention
Secondary	Changes in electroencephalography functional connectivity	Continuous measure, the more significant changes the better	Difference between the week before versus after each intervention