Brain-Computer Interface Device for Severe Neurological Disorders

(BRAVO Trial)

Overseen by Karunesh Ganguly, MD, PhD

Age: 18+

Sex: Any

Travel: May Be Covered

Time Reimbursement: Varies

Trial Phase: Academic

Recruiting

Sponsor: Karunesh Ganguly

Disqualifiers: Dementia, Active depression, Substance abuse, others

No Placebo Group

Approved in 1 Jurisdiction

Trial Summary

What is the purpose of this trial?

This trial is testing a method that uses sensors on the brain to help people with severe neurological disorders control devices and speak. The sensors pick up brain signals and translate them into actions or speech. This could help those who struggle with movement and communication due to their condition.

Will I have to stop taking my current medications?

The trial information does not specify if you need to stop taking your current medications. It's best to discuss this with the trial coordinators.

What data supports the effectiveness of the treatment PMT/Blackrock Combination Device for severe neurological disorders?

Research shows that brain-machine interfaces (BMIs), like the PMT/Blackrock Combination Device, have made significant progress in helping patients with severe neurological impairments regain some neural functions. Studies have demonstrated that BMIs can enable people with movement and communication disorders to control assistive devices directly with their brain signals, improving their quality of life. ^{1 2 3 4 5}

What safety data exists for the Brain-Computer Interface Device for Severe Neurological Disorders?

The BrainGate feasibility study, the largest and longest-running clinical trial of an implanted brain-computer interface, reports safety results but acknowledges that the long-term safety of these devices in humans is still unknown. Additionally, a study involving a neural interface in patients with Parkinson's disease found no serious surgical complications, suggesting a favorable safety profile in the short term. ^{3 5 6 7 8}

How is the PMT/Blackrock Combination Device treatment different from other treatments for severe neurological disorders?

The PMT/Blackrock Combination Device is unique because it uses a brain-machine interface (BMI) to directly connect the brain to external devices, allowing real-time control and communication for patients with severe neurological disorders. This approach is different from traditional treatments as it involves implanting electrodes on the brain's surface to capture brain signals, which are then translated into actions, potentially restoring neural functions and improving quality of life. ^{1 2 9 10 11}

Research Team

Karunesh Ganguly, MD, PhD

Principal Investigator

University of California, San Francisco

Eligibility Criteria

Adults over 21 with severe limitations in upper limb use due to conditions like stroke, ALS, MS, or spinal cord injury. They must have significant disability and be at least one year post-symptom onset for strokes or injuries. Participants need to live within two hours of UCSF and cannot be pregnant, have certain mental health issues, substance abuse history, major organ failure, prior brain surgery or seizures.

Inclusion Criteria

I need help with daily activities due to my disability.

I have limited use of my arms due to a neurological condition.

It has been over a year since my stroke or spinal cord injury.

Exclusion Criteria

Inability to comply with study follow-up visits

I have a history of seizures.

My immune system is weak.

Trial Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Training and Assessment

Participants undergo training and assessment of their ability to control a complex robotic system using ECoG signals

6 months

Follow-up

Participants are monitored for safety and effectiveness after treatment

Up to 6 years

Treatment Details

Interventions

• PMT/Blackrock Combination Device (Brain-Machine Interface)

Trial Overview The trial is testing a device combining PMT/Blackrock technology that uses brain signals (ECoG) for controlling motor and speech devices in those severely affected by neurological disorders.

Participant Groups

Group I: Electrocorticography-based brain computer interface Experimental Treatment 1 Intervention

Who Is Running the Clinical Trial?

Karunesh Ganguly

Lead Sponsor

Trials

1

Recruited

3+

University of California, San Francisco

Lead Sponsor

Trials

2,636

Recruited

19,080,000+

Suresh Gunasekaran

University of California, San Francisco

Chief Executive Officer since 2022

MBA from Southern Methodist University

Dr. Lukejohn Day

University of California, San Francisco

Chief Medical Officer

MD from Stanford University School of Medicine

National Institute on Deafness and Other Communication Disorders (NIDCD)

Collaborator

Trials

377

Recruited

190,000+

Joshua M. Levy

National Institute on Deafness and Other Communication Disorders (NIDCD)

Chief Medical Officer

MD, MPH, MS

Debara L. Tucci

National Institute on Deafness and Other Communication Disorders (NIDCD)

Chief Executive Officer since 2019

MD, MS, MBA

Findings from Research

Brain-machine interface (BMI) technology has advanced significantly over the past decade and is nearing clinical application to help restore neural functions in patients with severe neurological impairments.

In addition to its therapeutic potential, BMI serves as a valuable tool for studying brain signal processing and understanding neural dysfunction, which could lead to new treatments for various neurological disorders.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

[Brain-machine interface (BMI) - application to neurological disorders]. Yoshimine, T., Yanagisawa, T., Hirata, M.[2019]

The Brain Machine Interface (BMI) system, specifically the ECoG-BMI, aims to improve the quality of life for paralyzed patients by interpreting brain activity and controlling devices accordingly.

The developed device features a high-density electrode array for effective ECoG signal recording, a compact circuit board for signal processing, and a wireless power supply, demonstrating a significant advancement in the integration and functionality of BMI technology.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Development of an implantable wireless ECoG 128ch recording device for clinical brain machine interface. Matsushita, K., Hirata, M., Suzuki, T., et al.[2020]

Invasive Brain-Machine Interface (BMI) technology has shown promise in allowing individuals with severe movement and communication disorders to directly control assistive devices, as demonstrated in recent pilot clinical studies.

These studies have sparked interest among healthcare providers and patients, highlighting the potential of BMI technology to improve the quality of life for people with disabilities, while also addressing the challenges and future directions for its clinical application.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Cortical neuroprosthetics from a clinical perspective. Tsu, AP., Burish, MJ., GodLove, J., et al.[2021]

Brain-computer interfaces (BCIs) have the potential to significantly improve the quality of life for individuals with neurological conditions by enabling communication and interaction with their environment.

The paper discusses the principles and challenges of implementing BCIs in neurorehabilitation, highlighting the need for effective clinical translation to ensure these technologies can be successfully integrated into patient care.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Brain-Computer Interfaces in Neurorecovery and Neurorehabilitation. Young, MJ., Lin, DJ., Hochberg, LR.[2023]

A first-generation, totally implantable neural interface was successfully used in 5 patients with Parkinson's disease, allowing for both continuous therapeutic stimulation and recording of brain activity over a 1-year period without serious complications.

The device demonstrated the feasibility of acquiring chronic multisite brain signals, which can enhance our understanding of neural networks and potentially improve future neurostimulation therapies.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Chronic multisite brain recordings from a totally implantable bidirectional neural interface: experience in 5 patients with Parkinson's disease. Swann, NC., de Hemptinne, C., Miocinovic, S., et al.[2019]

The BrainGate feasibility study, involving 14 adults with quadriparesis, demonstrated that the implanted microelectrode arrays had a low rate of serious adverse events (SAEs) over an average implantation duration of 872 days, with no device-related deaths or permanent disabilities.

The most common adverse event was skin irritation, and overall, the safety profile of the BrainGate Neural Interface system is comparable to other chronically implanted medical devices, suggesting a favorable risk/benefit ratio for further research.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Interim Safety Profile From the Feasibility Study of the BrainGate Neural Interface System. Rubin, DB., Ajiboye, AB., Barefoot, L., et al.[2023]

The study identifies six core risk domains associated with implantable brain-computer interfaces (BCIs), including safety, cognitive impairment, and privacy concerns, which are crucial for informed consent in clinical applications.

Additionally, the research highlights three further risk domains—identity, agency, and stigma—that are often overlooked in consent processes, emphasizing the need for comprehensive discussions about the implications of BCI technology with potential participants.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Informed Consent in Implantable BCI Research: Identifying Risks and Exploring Meaning. Klein, E.[2018]

Invasive brain-machine interfaces (BMIs) have made significant advancements over the past two decades, particularly in their ability to record high-resolution neural signals, which is crucial for restoring motor functions in paralyzed individuals.

The transition of BMI technology from successful animal research to clinical trials shows promise in helping people regain lost motor abilities, highlighting both the potential benefits and the challenges that lie ahead in this field.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Invasive Brain Machine Interface System. Jin, Y., Chen, J., Zhang, S., et al.[2020]

The developed Brain-Computer Interface (BCI) system achieved an impressive average classification accuracy of 91.3% and an Information Transfer Rate (ITR) of 2.2 commands per minute, demonstrating its effectiveness in translating EEG signals into commands.

Using a simple classification technique (Nearest Neighbour Approach), the BCI system elicited strong P300 responses, showing that intuitive graphical user interfaces can lead to competitive performance compared to more complex systems.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Importance of Graphical User Interface in the design of P300 based Brain-Computer Interface systems. Ratcliffe, L., Puthusserypady, S.[2021]

The 'neurocommunicator' brain-machine interface developed for patients with severe motor deficits can accurately identify messages from EEG data with over 95% accuracy, allowing for communication at a rate of about 32 bits per minute.

The system combines a portable EEG recorder, a high-speed decoding algorithm, and a hierarchical message generation system, but further technical improvements are needed for effective use in home settings for patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

[Development of a cognitive BMI "neurocommunicator" as a communication aid of patients with severe motor deficits]. Hasegawa, RP.[2019]

Brain-Machine Interface (BMI) technology allows users to control devices like robotic arms and tablets using their brain activity, showing promise for helping individuals with motor disabilities.

Regular use of BMI can enhance brain plasticity, potentially leading to improved neurological recovery and functional reorganization of the sensorimotor nervous system in patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

[Brain-Machine Interface and Neuro-Rehabilitation]. Ushiba, J.[2019]

References

1. Japan pubmed.ncbi.nlm.nih.gov

[Brain-machine interface (BMI) - application to neurological disorders]. [2019]

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

Development of an implantable wireless ECoG 128ch recording device for clinical brain machine interface. [2020]

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

Cortical neuroprosthetics from a clinical perspective. [2021]

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

Brain-Computer Interfaces in Neurorecovery and Neurorehabilitation. [2023]

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

Chronic multisite brain recordings from a totally implantable bidirectional neural interface: experience in 5 patients with Parkinson's disease. [2019]

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

Interim Safety Profile From the Feasibility Study of the BrainGate Neural Interface System. [2023]

7. England pubmed.ncbi.nlm.nih.gov

Informed Consent in Implantable BCI Research: Identifying Risks and Exploring Meaning. [2018]

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

Invasive Brain Machine Interface System. [2020]

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

Importance of Graphical User Interface in the design of P300 based Brain-Computer Interface systems. [2021]

10. Japan pubmed.ncbi.nlm.nih.gov

[Development of a cognitive BMI "neurocommunicator" as a communication aid of patients with severe motor deficits]. [2019]

11. Japan pubmed.ncbi.nlm.nih.gov

[Brain-Machine Interface and Neuro-Rehabilitation]. [2019]