Non-invasive BCI-controlled Assistive Devices

Official title

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

Overview

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.

Interventions

• 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.
• 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.

Primary outcome measures

• Change in the BCI command delivery performance [Time frame: immediately after each intervention session and up to one week after all sessions]
• Change in the focality and Strength of SMR Modulation [Time frame: immediately after each intervention session and up to one week after all sessions]

Secondary outcome measures (6)

• Stability of Motor Imagery features [Time frame: immediately after each intervention session and one-day after all sessions]
• Separability of Motor Imagery features [Time frame: immediately after each intervention session and one-day after all sessions]
• Changes in motor-evoked potential amplitude [Time frame: immediately after each intervention session and one-day after all sessions]
• Changes in electroencephalography functional connectivity [Time frame: immediately after each intervention session and one-day after all sessions]
• Change in focality of fMRI activation for different imagined movements [Time frame: immediately after each intervention session and one-day after all sessions]
• More discriminable fMRI activations for different imagined movements [Time frame: immediately after each intervention session and one-day after all sessions]

Eligibility criteria

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

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: Yes

Study design

Allocation

Randomized

Model

Factorial

Masking

Open label

Primary purpose

Basic science

Study locations

United States · 1 center

• The University of Texas at Austin — Austin

Identifiers

NCT: NCT05183152 · 2020030073

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗