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NCT Number: NCT05183152

Non-invasive BCI-controlled 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.

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Key information

Conditions

Motor Disorders Autoimmune Diseases Autoimmune Diseases of the Nervous System Brain Diseases Brain Injuries Brain Injuries, Traumatic Cardiovascular Diseases Central Nervous System Diseases Cerebrovascular Disorders Craniocerebral Trauma Demyelinating Autoimmune Diseases, CNS Demyelinating Diseases Healthy Immune System Diseases Mental Disorders Motor Neuron Disease Movement Disorders Multiple Sclerosis Muscular Diseases Musculoskeletal Diseases Nervous System Diseases Neurodegenerative Diseases Neurologic Manifestations Neuromuscular Diseases Pathological Conditions, Signs and Symptoms Signs and Symptoms Spinal Cord Diseases Spinal Cord Injuries Stroke Trauma, Nervous System Traumatic Brain Injury Vascular Diseases Wounds and Injuries

Age range

18 year–80 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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
  • ability to provide informed consent

Exclusion criteria

  • 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)
  • 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

Treatment and study plan

NMES Feedback

Device

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.

Visual Feedback

Device

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.

TESS

Device

Transcutaneous Electrical Spinal Stimulation (TESS) is applied over the C5-C6 spinal segment for 20 minutes at 30Hz with 5kHz carrier frequency.

Primary outcomes

  1. Change in the BCI command delivery performance

    Time frame: immediately after each intervention session and up to one week after all sessions

    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.

  2. Change in the focality and Strength of SMR Modulation

    Time frame: immediately after each intervention session and up to one week after all sessions

    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

Secondary outcomes

  1. Stability of Motor Imagery features

    Time frame: immediately after each intervention session and one-day after all sessions

    The features corresponding to different motor imagery tasks become more stable at the end of the intervention.

  2. Separability of Motor Imagery features

    Time frame: immediately after each intervention session and one-day after all sessions

    The features corresponding to different motor imagery tasks become more separable after the intervention.

  3. Changes in motor-evoked potential amplitude

    Time frame: immediately after each intervention session and one-day after all sessions

    Continuous measure, the higher the better

  4. Changes in electroencephalography functional connectivity

    Time frame: immediately after each intervention session and one-day after all sessions

    Continuous measure, the more significant changes the better

  5. Change in focality of fMRI activation for different imagined movements

    Time frame: immediately after each intervention session and one-day after all sessions

    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.

  6. More discriminable fMRI activations for different imagined movements

    Time frame: immediately after each intervention session and one-day after all sessions

    The activation associated with different MI tasks would be more discriminable from BOLD signals.

    Continuous measure, the more the better.

Study contacts

Contact information is provided by the study sponsor or research team.

Hussein Alawieh

CONTACT

[email protected]

512-373-0535

Jose del R. Millan, PhD

CONTACT

[email protected]

512-232-8111

Sponsors and collaborators

Lead sponsor

University of Texas at Austin

Other

Registry information

Official study title

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

Important dates

Study start
2021
Primary completion
2028
Study completion
2028
First posted
Jan 10, 2022
Registry last updated
May 1, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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