BCI-Controlled Devices for Motor Disorders

Overseen By Jose del R. Millan, PhD

Age: 18+

Sex: Any

Travel: May Be Covered

Time Reimbursement: Varies

Trial Phase: Academic

Recruiting

Sponsor: University of Texas at Austin

Must not be taking: CNS affecting

Disqualifiers: Cognitive deficits, Metabolic disorders, others

No Placebo Group

Approved in 2 Jurisdictions

Trial Summary

What is the purpose of this trial?

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.

Will I have to stop taking my current medications?

The trial does not specify if you need to stop taking your current medications, but it excludes participants on heavy medication affecting the central nervous system. It's best to discuss your specific medications with the trial team.

What data supports the effectiveness of the treatment Non-invasive BCI-controlled Assistive Devices for motor disorders?

Research shows that non-invasive brain-computer interfaces (BCIs) can help people with severe disabilities control devices using their brain signals. Studies have found that these BCIs can provide movement control comparable to more invasive methods, suggesting they could be effective for people with motor disorders. ^{1 2 3 4 5}

Is it safe to use non-invasive BCI-controlled assistive devices for motor disorders?

The research on non-invasive BCI-controlled assistive devices shows that they are generally safe for use in humans, including those with severe disabilities and neurological disorders. However, improvements are needed for daily life use, and there are concerns about the regulatory safeguards for these devices. ^{2 6 7 8 9}

How is the treatment of non-invasive BCI-controlled assistive devices different from other treatments for motor disorders?

Non-invasive BCI-controlled assistive devices are unique because they allow people with motor disorders to control external devices using their brain activity, without the need for surgery or invasive procedures. This treatment uses EEG (a method to record brain activity) to interpret mental commands, offering a nonintrusive and user-friendly alternative to traditional therapies that often rely on physical movement or invasive techniques. ^{6 10 11 12 13}

Research Team

Jose del R. Millan, PhD

Principal Investigator

The University of Texas at Austin

Eligibility Criteria

This trial is for people with certain motor disabilities (like stroke, spinal cord injury, or muscular diseases) and healthy individuals with normal vision. Participants must understand English and be able to consent. Those with serious illnesses, attention/cognitive issues preventing focus during sessions, heavy central nervous system medication, or conditions affecting EEG/EMG data collection can't join.

Inclusion Criteria

Ability to provide informed consent

Able-bodied participants in good general health with normal or corrected vision and no history of neurological/psychiatric disease

Ability to read and understand English (Research Personnel do not speak Spanish)

Exclusion Criteria

You have another serious health condition, like a metabolic disorder.

You have trouble moving and paying attention for the entire study session.

You are taking strong medication that affects your brain and alertness.

Trial Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Training

Participants undergo BCI training with transcutaneous electrical spinal stimulation and neuromuscular electrical stimulation to improve motor imagery control.

8 weeks

Weekly sessions

Follow-up

Participants are monitored for changes in BCI command delivery performance and SMR modulation strength.

1 week

1 visit (in-person)

Long-term Follow-up

Participants are assessed for stability and separability of motor imagery features and changes in fMRI activations.

1 day after all sessions

Treatment Details

Interventions

• Non-invasive BCI-controlled Assistive Devices (Behavioural Intervention)

Trial Overview The study tests brain-computer interfaces (BCI) that read brain activity to control devices without movement. It focuses on whether neuromuscular electrical stimulation (NMES), when paired with motor imagery-based BCI, improves the differentiation of hand movement patterns in the same limb.

Participant Groups

Group I: TESS BCI - Standard MI Task Experimental Treatment 2 Interventions

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.

Group II: NMES BCI - Difficult MI Task Experimental Treatment 1 Intervention

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.

Group III: Visual BCI - Standard MI Task Active Control 1 Intervention

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.

Group IV: Visual BCI - Difficult MI Task Active Control 1 Intervention

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.

Who Is Running the Clinical Trial?

University of Texas at Austin

Lead Sponsor

Trials

387

Recruited

86,100+

Findings from Research

Brain-computer interfaces (BCIs) have advanced significantly over the past few decades, leading to the creation of faster and more reliable technologies that help individuals with severe disabilities control external devices using their brain activity.

These developments in BCIs represent a promising area of research and application, enhancing the quality of life for people with disabilities by providing them with new ways to interact with their environment.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Brain⁻Computer Interfaces for Human Augmentation. Valeriani, D., Cinel, C., Poli, R.[2020]

The integration of brain-computer interface (BCI) technology into the QualiWORLD assistive device showed high effectiveness in communication and internet tasks among four users with severe disabilities, indicating its potential for enhancing user interaction.

Despite high user satisfaction and performance, challenges such as the EEG-cap and low speed were identified, suggesting that further improvements are necessary for practical daily use.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A brain-computer interface as input channel for a standard assistive technology software. Zickler, C., Riccio, A., Leotta, F., et al.[2017]

A noninvasive brain-computer interface (BCI) using scalp-recorded brain signals can provide multidimensional movement control for individuals with severe motor disabilities, comparable to invasive BCIs used in monkeys.

The adaptive algorithm in this noninvasive BCI enhances users' ability to control movements by focusing on the brain signals they can best manage, suggesting a safe and effective alternative for operating robotic arms or neuroprostheses without surgery.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Wolpaw, JR., McFarland, DJ.[2023]

The recoveriX system is a novel brain-computer interface designed specifically for stroke patients, aiming to enhance therapy by monitoring motor imagery in real-time.

Initial results from a single patient indicate that recoveriX can provide closed-loop feedback, potentially improving compliance and effectiveness of rehabilitation therapies for stroke recovery.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

recoveriX: a new BCI-based technology for persons with stroke. Irimia, D., Sabathiel, N., Ortner, R., et al.[2020]

A chronically implanted ECoG brain-computer interface (BCI) allowed a participant with ALS to achieve a median spelling rate of 10.2 characters per minute using a click-decoding method, demonstrating effective communication capabilities.

The click decoder maintained robust performance over 90 days without retraining, even after being trained on a small dataset of less than 44 minutes, indicating its long-term stability and potential for practical use in communication for individuals with movement and speech impairments.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A click-based electrocorticographic brain-computer interface enables long-term high-performance switch-scan spelling. Crone, N., Candrea, D., Shah, S., et al.[2023]

The study introduces a novel dynamic and self-adaptive algorithm (DSAA) that improves the accuracy of brain-computer interfaces (BCIs) by 8% compared to existing methods, based on experiments with 17 subjects.

DSAA effectively selects the best feature extraction and classification algorithms for each individual during both training and online testing, enhancing the system's performance even without reference labels for trials.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A dynamic and self-adaptive classification algorithm for motor imagery EEG signals. Belwafi, K., Gannouni, S., Aboalsamh, H., et al.[2020]

Implantable Brain-Computer Interface (BCI) devices are undergoing clinical trials in the U.S., with potential introduction to Canada in the near future, highlighting the advancements in this technology.

The article argues that BCIs should not be approved for investigational testing in Canada until stronger regulatory safeguards are established to protect research subjects, indicating concerns about safety and oversight.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Brain-Computer Interface devices: risks and Canadian regulations. Alpert, S.[2008]

Non-invasive brain-computer interfaces (BCIs) have the potential to significantly improve communication for patients with severe disabilities, such as those with locked-in syndrome, but they are not yet commonly used in daily life despite some successful individual cases.

Recent advancements in hybrid BCIs, which combine different paradigms and signals, may enhance performance and communication speed, addressing issues like BCI illiteracy and offering more effective solutions for patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Single-paradigm and hybrid brain computing interfaces and their use by disabled patients. de Neeling, M., Van Hulle, MM.[2020]

A new brain-computer interface (BCI) method using non-invasive EEG signals showed high reliability in classifying motor intentions without requiring extensive training, achieving over 90% accuracy in healthy volunteers and around 80% in patients with neurological disorders.

The study demonstrated that EEG beta band activity, particularly from the sensorimotor area, is effective for distinguishing between motor execution and motor imagery, making this BCI method promising for clinical applications.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A high performance sensorimotor beta rhythm-based brain-computer interface associated with human natural motor behavior. Bai, O., Lin, P., Vorbach, S., et al.[2022]

A new capacitive EEG electrode, which does not require gel or direct contact with the scalp, showed high performance in brain-computer interface (BCI) systems, achieving an accuracy of 95.2% for visual tasks and 82.6% for auditory tasks in tests with five subjects.

The capacitive electrode demonstrated feasibility for practical use in BCI applications, suggesting it could provide a more user-friendly and non-intrusive alternative to traditional wet electrodes, which require more complex setup and cleanup.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Brain-computer interfaces using capacitive measurement of visual or auditory steady-state responses. Baek, HJ., Kim, HS., Heo, J., et al.[2013]

In a study involving 18 healthy untrained subjects, those who practiced distinguishing between a motor imagery task and mental relaxation had better control over a brain-computer interface (BCI) compared to those who only practiced motor imagery tasks.

The findings suggest that effective BCI training protocols should be tailored to individual subjects, emphasizing the importance of selecting easily distinguishable mental tasks to enhance EEG control and user satisfaction.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

[Training protocol evaluation of a brain-computer interface: mental tasks proposal]. Ron-Angevin, R., Díaz-Estrella, A.[2015]

Brain-computer interface (BCI) technology can help individuals with neurological disorders and disabilities by enabling them to communicate and control devices using only their thoughts.

In laboratory experiments, BCIs have successfully allowed patients to perform tasks by imagining movements, showcasing their potential for functional support in real-world applications.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Step Closer to Mind Control for Everyday Life. Bates, M.[2021]

Noninvasive brain-computer interfaces (BCIs) using EEG have successfully enabled communication for paralyzed and locked-in patients, demonstrating their potential for clinical applications.

While invasive BCIs in animals have shown promise for precise motor control, it remains unclear if they provide superior benefits over noninvasive methods for restoring voluntary movement in humans.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Physiological regulation of thinking: brain-computer interface (BCI) research. Birbaumer, N., Weber, C., Neuper, C., et al.[2006]

References

1. Switzerland pubmed.ncbi.nlm.nih.gov

Brain⁻Computer Interfaces for Human Augmentation. [2020]

2. United States pubmed.ncbi.nlm.nih.gov

A brain-computer interface as input channel for a standard assistive technology software. [2017]

3. United States pubmed.ncbi.nlm.nih.gov

Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. [2023]

4. United States pubmed.ncbi.nlm.nih.gov

recoveriX: a new BCI-based technology for persons with stroke. [2020]

5. United States pubmed.ncbi.nlm.nih.gov

A click-based electrocorticographic brain-computer interface enables long-term high-performance switch-scan spelling. [2023]

6. Netherlands pubmed.ncbi.nlm.nih.gov

A dynamic and self-adaptive classification algorithm for motor imagery EEG signals. [2020]

7. United States pubmed.ncbi.nlm.nih.gov

Brain-Computer Interface devices: risks and Canadian regulations. [2008]

8. England pubmed.ncbi.nlm.nih.gov

Single-paradigm and hybrid brain computing interfaces and their use by disabled patients. [2020]

9. England pubmed.ncbi.nlm.nih.gov

A high performance sensorimotor beta rhythm-based brain-computer interface associated with human natural motor behavior. [2022]

10. England pubmed.ncbi.nlm.nih.gov

Brain-computer interfaces using capacitive measurement of visual or auditory steady-state responses. [2013]

11. Spain pubmed.ncbi.nlm.nih.gov

[Training protocol evaluation of a brain-computer interface: mental tasks proposal]. [2015]

12. United States pubmed.ncbi.nlm.nih.gov

A Step Closer to Mind Control for Everyday Life. [2021]

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Physiological regulation of thinking: brain-computer interface (BCI) research. [2006]