Non-invasive BCI-controlled Assistive Devices

Part of paid clinical trials in Austin, Texas.

Sponsor
University of Texas at Austin
Study ID
NCT05183152
Status
Recruiting
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Conditions

  • Healthy
  • Motor Disorders
  • Motor Neuron Disease
  • Movement Disorders
  • Multiple Sclerosis
  • Muscular Diseases
  • Spinal Cord Injuries
  • Stroke
  • Traumatic Brain Injury

Eligibility Criteria

Sex
ALL
Age
18 Years - 80 Years
Healthy Volunteers
Accepted

Interventions

  • 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.

Study Details

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.

Key Dates

Start date
Jun 16, 2021
Status verified
Apr 2026
Primary completion
Dec 30, 2028
Completion
Dec 30, 2028

Study Design

Enrollment
100 participants (estimated)
Allocation
RANDOMIZED
Intervention model
FACTORIAL
Primary purpose
BASIC_SCIENCE

Arms

  • 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.
  • 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.
  • 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.
  • 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.

Primary Outcome Measure

Change in the BCI command delivery performance [ Time Frame: immediately after each intervention session and up to one week after all sessions ]

Central Contacts

Locations (1)

FacilityCityStateZIPSite coordinators
The University of Texas at AustinAustinTexas78712
Jose del R. Millan, PhD
[email protected]
512-232-8111
Hussein Alawieh
[email protected]
5123730535

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