Non-invasive BCI-controlled Assistive Devices

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

Injuries affecting the central nervous system may disrupt the cortical pathways to muscles causing loss of motor control. Nevertheless, the brain still exhibits sensorimotor rhythms (SMRs) during movement intents or motor imagery (MI), which is the mental rehearsal of the kinesthetics of a movement without actually performing it. Brain-computer interfaces (BCIs) can decode SMRs to control assistive devices and promote functional recovery. Despite rapid advancements in non-invasive BCI systems based on EEG, two persistent challenges remain: First, the instability of SMR patterns due to the non-stationarity of neural signals, which may significantly degrade BCI performance over days and hamper the effectiveness of BCI-based rehabilitation. Second, differentiating MI patterns corresponding to fine hand movements of the same limb is still difficult due to the low spatial resolution of EEG. To address the first challenge, subjects usually learn to elicit reliable SMR and improve BCI control through longitudinal training, so a fundamental question is how to accelerate subject training building upon the SMR neurophysiology. In this study, the investigators hypothesize that conditioning the brain with transcutaneous electrical spinal stimulation, which reportedly induces cortical inhibition, would constrain the neural dynamics and promote focal and strong SMR modulations in subsequent MI-based BCI training sessions - leading to accelerated BCI training. To address the second challenge, 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. The investigators study the neuroplastic changes associated with training with the two stimulation modalities.

Study Overview

• Status: Recruiting
• Conditions: Stroke; Multiple Sclerosis; Healthy; Muscular Diseases; Movement Disorders; Motor Neuron Disease; Traumatic Brain Injury; Spinal Cord Injuries; Motor Disorders
• Intervention / Treatment: Device: Visual Feedback; Device: TESS; Device: NMES Feedback

• Study Type: Interventional
• Enrollment (Estimated): 100
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Jose del R. Millan, PhD; Phone Number: 512-232-8111; Email: jose.millan@austin.utexas.edu
• Study Contact Backup: Name: Hussein Alawieh; Phone Number: 512-373-0535; Email: hussein@utexas.edu

Study Locations

• United States
  • Texas
    • Austin, Texas, United States, 78712
      • Recruiting
      • The University of Texas at Austin
      • Contact: Jose del R. Millan, PhD; Phone Number: 512-232-8111; Email: jose.millan@austin.utexas.edu
      • Contact: Hussein Alawieh; Phone Number: 5123730535; Email: hussein@utexas.edu

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 80 years (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes

Description

Inclusion Criteria:

1. 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)

2. 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
   • ability to provide informed consent

Exclusion Criteria:

1. Subjects with motor disabilities

   • short attentional spans or cognitive deficits that prevent the subject from concentrating during the whole experimental session
   • heavy medication affecting the central nervous system (including vigilance)
   • concomitant serious illness (e.g., metabolic disorders)

2. All participants

   • factors hindering EEG/EMG acquisition and the delivery of non-invasive electrical stimulation (e.g., skin infection, wounds, dermatitis, metal implants under electrodes)
   • criteria identified in safety guidelines for MRI and TMS, in particular metallic implants

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Basic Science
• Allocation: Randomized
• Interventional Model: Factorial Assignment
• Masking: None (Open Label)

Number of Arms

4

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: TESS BCI - Standard MI Task; Transcutaneous Electrical Spinal Stimulation (TESS) is applied for 20 minutes prior to BCI training sessions. Following TESS, BCI training is performed with visual feedback contingent to motor imagery as detected by a closed-loop BCI.	Device: Visual Feedback; Electroencephalography (EEG) - recorded from subjects as they perform cued motor imagery (MI) tasks - are classified in real-time using a subject-specific BCI decoder,. The output classification probability of the decoder is accumulated using exponential smoothing and translated into continuous visual feedback by means of a bar - on a computer screen - that moves to the right or left in response to classification of one or the other MI task.; Device: TESS; Transcutaneous Electrical Spinal Stimulation (TESS) is applied over the C5-C6 spinal segment for 20 minutes at 30Hz with 5kHz carrier frequency.
Active Comparator: Visual BCI - Standard MI Task; Conventional BCI training is performed with visual feedback contingent to the imagination of right versus left hand movements as detected by a closed-loop BCI.	Device: Visual Feedback; Electroencephalography (EEG) - recorded from subjects as they perform cued motor imagery (MI) tasks - are classified in real-time using a subject-specific BCI decoder,. The output classification probability of the decoder is accumulated using exponential smoothing and translated into continuous visual feedback by means of a bar - on a computer screen - that moves to the right or left in response to classification of one or the other MI task.
Experimental: NMES BCI - Difficult MI Task; BCI training is performed with NMES instead of Visual feedback. NMES is delivered over the flexors/extensors of the forearm contingent to the imagination of same-hand wrist and fingers flexion versus extension as detected by a closed-loop BCI.	Device: NMES Feedback; Electroencephalography (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.
Active Comparator: Visual BCI - Difficult MI Task; Conventional BCI training is performed with visual feedback contingent to the imagination of same-hand wrist and fingers flexion versus extension as detected by a closed-loop BCI.	Device: Visual Feedback; Electroencephalography (EEG) - recorded from subjects as they perform cued motor imagery (MI) tasks - are classified in real-time using a subject-specific BCI decoder,. The output classification probability of the decoder is accumulated using exponential smoothing and translated into continuous visual feedback by means of a bar - on a computer screen - that moves to the right or left in response to classification of one or the other MI task.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Change in the BCI command delivery performance	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.	immediately after each intervention session and up to one week after all sessions
Change in the focality and Strength of SMR Modulation	The focality of sensorimotor rhythm modulation is assessed from EEG using event-related desynchorinzation (ERD) and synchronization (ERS) over the motor area. Continuous measure, the higher the better	immediately after each intervention session and up to one week after all sessions

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Stability of Motor Imagery features	The features corresponding to different motor imagery tasks become more stable at the end of the intervention.	immediately after each intervention session and one-day after all sessions
Separability of Motor Imagery features	The features corresponding to different motor imagery tasks become more separable after the intervention.	immediately after each intervention session and one-day after all sessions
Changes in motor-evoked potential amplitude	Continuous measure, the higher the better	immediately after each intervention session and one-day after all sessions
Changes in electroencephalography functional connectivity	Continuous measure, the more significant changes the better	immediately after each intervention session and one-day after all sessions
Change in focality of fMRI activation for different imagined movements	The clusters of significant activation during MI of different movements would be more focal in the associated region of the motor area Continuous measure, the more the better.	immediately after each intervention session and one-day after all sessions
More discriminable fMRI activations for different imagined movements	The activation associated with different MI tasks would be more discriminable from BOLD signals. Continuous measure, the more the better.	immediately after each intervention session and one-day after all sessions

Collaborators and Investigators

• Sponsor: University of Texas at Austin
• Investigators: Principal Investigator: Jose del R. Millan, PhD, The University of Texas at Austin

Study record dates

Study Major Dates

• Study Start (Actual): June 16, 2021
• Primary Completion (Estimated): December 30, 2028
• Study Completion (Estimated): December 30, 2028

Study Registration Dates

• First Submitted: December 20, 2021
• First Submitted That Met QC Criteria: December 20, 2021
• First Posted (Actual): January 10, 2022

Study Record Updates

• Last Update Posted (Actual): May 1, 2026
• Last Update Submitted That Met QC Criteria: April 27, 2026
• Last Verified: April 1, 2026

More Information

Terms related to this study

• Keywords: movement disorders; spinal cord injury; motor deficits; able-bodied, healthy; unilateral and bilateral stroke; motor neuron diseases; muscular diseases (i.e. myopathy); traumatic or neurological pain
• Additional Relevant MeSH Terms: Musculoskeletal Diseases; Cerebrovascular Disorders; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Vascular Diseases; Cardiovascular Diseases; Mental Disorders; Wounds and Injuries; Neuromuscular Diseases; Autoimmune Diseases; Immune System Diseases; Demyelinating Autoimmune Diseases, CNS; Autoimmune Diseases of the Nervous System; Demyelinating Diseases; Neurodegenerative Diseases; Craniocerebral Trauma; Trauma, Nervous System; Spinal Cord Diseases; Brain Injuries; Pathological Conditions, Signs and Symptoms; Signs and Symptoms; Brain Injuries, Traumatic; Motor Disorders; Stroke; Neurologic Manifestations; Multiple Sclerosis; Movement Disorders; Muscular Diseases; Motor Neuron Disease; Spinal Cord Injuries
• Other Study ID Numbers: 2020030073

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Sharing Time Frame: All data will be made available by the online publication date
• IPD Sharing Access Criteria: Data will be placed in public servers for any interested researcher to access it
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: Yes
• product manufactured in and exported from the U.S.: No