BCI-FIT for ALS

(BCI-FIT Trial)

Overseen By Betts Peters, PhD

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Oregon Health and Science University

Disqualifiers: Unstable medical conditions, Photosensitive seizure, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 12

Duration: 12 weeks

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial aims to improve communication tools for people with severe speech and physical impairments due to neurodegenerative diseases like ALS. Researchers are developing a non-invasive brain-computer interface (BCI) called the Brain Computer Interface Functional Implementation Toolkit (BCI-FIT) to interpret user intent for better typing speed and accuracy. The study tests various configurations and methods, such as language modeling and adaptive signal processing, to enhance the BCI's performance. Ideal participants are adults with significant speech and movement challenges who can attend study visits and use basic communication tools. As an unphased trial, this study offers participants the chance to contribute to pioneering research that could significantly enhance communication for individuals with severe impairments.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications. It is best to discuss this with the trial coordinators or your doctor.

What prior data suggests that this BCI-FIT is safe for adults with severe speech and physical impairments?

Research has shown that brain-computer interfaces (BCIs), such as BCI-FIT, are being used safely by individuals with conditions like ALS (amyotrophic lateral sclerosis). These devices do not require surgery, making them non-invasive and generally easy to use. Studies have found that people with ALS can use BCIs at home and that these devices can be adjusted to meet individual needs. Although specific safety details for BCI-FIT are not provided, research on similar BCIs suggests they are safe, with no major side effects reported. This indicates that BCI-FIT is likely safe for participants. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about the BCI-FIT for ALS trial because it explores innovative ways to enhance communication for people with severe speech and physical impairments. Unlike traditional speech-generating devices, BCI-FIT uses advanced techniques like language modeling, active querying, and adaptive signal modeling to boost typing accuracy and speed. These methods aim to improve user experience by making communication faster and more efficient, which is a significant leap from current options that often rely on less dynamic interfaces. The trial's exploration of multi-modal configurations also offers a fresh approach to tailoring communication tools to individual needs, potentially leading to more personalized and effective solutions.

What evidence suggests that BCI-FIT is effective for improving communication in individuals with severe speech and physical impairments?

Research shows that brain-computer interfaces (BCIs) can help patients with ALS (amyotrophic lateral sclerosis) communicate more effectively. In this trial, participants will experience various BCI-FIT configurations. For the BCI-FIT language modeling arm, studies have found that ALS patients can type as quickly as healthy individuals using visual BCIs, which are faster than other systems. In the active querying techniques arm, BCIs enable people with severe impairments to communicate more accurately and quickly. The multi-modal setup of BCI-FIT, tested in another arm, combines different types of inputs to enhance communication, which has shown promise in other BCI studies. Lastly, the adaptive signal modeling arm allows the system to adjust to the user's needs, improving communication without frequent recalibration, making it more user-friendly and efficient. Overall, these technologies hold promise for enhancing communication in those with severe speech and physical impairments. ^{4 6 7 8 9}

Who Is on the Research Team?

Melanie Fried-Oken, PhD

Principal Investigator

Oregon Health and Science University

Are You a Good Fit for This Trial?

This trial is for adults aged 18-89 with severe speech and physical impairments due to conditions like ALS, muscular dystrophy, or brainstem stroke. They must be able to communicate in English and participate in study visits lasting up to 3 hours. Life expectancy should be over 6 months. Excluded are those who can't tolerate weekly visits, have skin risks from hardware contact, unstable medical conditions, certain implants, or photosensitive seizures.

Inclusion Criteria

"Controls" typically refers to a group of participants in a study who do not receive the treatment being tested, serving as a comparison for the group receiving the treatment.

Controls: Able to read and communicate in English

Participants with severe speech and physical impairment: Able to give informed consent or assent according to IRB approved policy

Exclusion Criteria

You have severe difficulty speaking or moving, or have certain types of implanted medical devices.

Participants with severe speech and physical impairment: Unable to tolerate weekly data collection visits

People with severe difficulty speaking or moving, who may be at risk of skin problems from touching the study equipment.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Baseline

Participants complete baseline copy-spelling sessions with their existing access method to establish stable performance

3-4 weeks

Weekly visits

Treatment

Participants engage in alternating-treatments single-case research design experiments to evaluate BCI-FIT configurations and adaptive techniques

12 weeks

12 data collection sessions (1 session/week)

Follow-up

Participants are monitored for safety and effectiveness after treatment

4 weeks

What Are the Treatments Tested in This Trial?

Interventions

• BCI-FIT

Trial Overview

The BCI-FIT toolkit is being tested which includes adaptive signal modeling and active querying techniques for improving communication through brain-computer interfaces (BCI). It aims to optimize typing speed and accuracy for people with severe speech and physical impairments by using multi-modal signals.

How Is the Trial Designed?

Group I: Language modeling Experimental Treatment 1 Intervention

For this single case research design with alternating treatments, 5 control volunteers and 5 participants with severe speech and physical impairment, each with a control partner for partner input will complete a story retell task with BCI-FIT language modeling features on and with BCI-FIT language modeling features off. Outcome measures are information transfer rate and user experience.

Group II: BCI-FIT multi-modal configuration Experimental Treatment 1 Intervention

For this single case research design with alternating treatments without baseline, 5 participants with severe speech and physical impairment will complete copy spelling tasks with a standard P300 matrix speller layout and with the multi-modal configurations optimized from the BCI-FIT algorithms. Outcome measures are typing accuracy, typing speed and user experience.

Group III: Adaptive signal modeling Experimental Treatment 1 Intervention

For this single case research design with alternating treatments without baseline, 5 participants with severe speech and physical impairment will complete copy spelling tasks with 3 signal adaptive modeling configurations. Outcome measures are typing accuracy, typing speed and user experience.

Group IV: Active querying techniques Experimental Treatment 1 Intervention

For this single case research design with alternating treatments without baseline, 5 control volunteers and 5 participants with severe speech and physical impairment who have AUC scores between 70-80% will complete copy spelling tasks with BCI-FIT active querying technique on and with BCI-FIT active querying technique off. Outcome measures are typing accuracy, typing speed and user experience.

Who Is Running the Clinical Trial?

Oregon Health and Science University

Lead Sponsor

Trials

1,024

Recruited

7,420,000+

Published Research Related to This Trial

The study successfully applied a fourth-order Autoregressive (AR) model to analyze EEG data from ALS patients, achieving a high accuracy of 97.51±0.64, which is crucial for improving brain-computer interface (BCI) performance.

Using P300-based BCI allows ALS patients to communicate and interact despite their motor difficulties, highlighting the potential of non-invasive EEG methods in enhancing the quality of life for individuals with this condition.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Applying Auto-Regressive Model's Yule-Walker Approach to Amyotrophic Lateral Sclerosis (ALS) patients' Data. Sahu, M., Vishwal, S., Usha Srivalli, S., et al.[2020]

The Wadsworth brain-computer interface (BCI) was successfully used by 14 out of 27 patients with advanced ALS for independent communication at home, demonstrating its reliability and usefulness over an 18-month period.

Despite some patients leaving the study due to disease progression or decreased interest, the majority of users found the BCI beneficial for communication, with minimal technical issues and stable quality of life reported.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Independent home use of a brain-computer interface by people with amyotrophic lateral sclerosis. Wolpaw, JR., Bedlack, RS., Reda, DJ., et al.[2023]

A survey of 61 people with ALS revealed that they prioritize a brain-computer interface (BCI) design that achieves at least 90% accuracy in command identification and operates at a speed of 15-19 letters per minute.

Despite the challenges, a significant majority (84%) of participants were open to using an electrode cap, and many were willing to undergo outpatient surgery to obtain a BCI, indicating a strong interest in this technology to improve their quality of life.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

What would brain-computer interface users want? Opinions and priorities of potential users with amyotrophic lateral sclerosis. Huggins, JE., Wren, PA., Gruis, KL.[2021]

The study involved 15 ALS patients and 15 healthy controls, demonstrating that a P300-based Brain Computer Interface (BCI) can effectively assess cognitive functions in ALS patients, correlating well with standard cognitive tests.

Patients reported positive usability experiences with the BCI, and the assessment revealed that ALS patients had slower processing speeds compared to controls, highlighting the potential of this technology for ongoing cognitive evaluation in ALS.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Cognitive assessment in Amyotrophic Lateral Sclerosis by means of P300-Brain Computer Interface: a preliminary study. Poletti, B., Carelli, L., Solca, F., et al.[2018]

In a study involving two participants with late-stage ALS and visual impairments, the modified Shuffle Speller typing interface combined with steady state visual evoked potential (SSVEP) BCI significantly improved typing accuracy, achieving up to 100% accuracy for one participant.

The results suggest that traditional eye tracking systems may not be effective for individuals with severe ALS, highlighting the potential of innovative BCI systems like the Shuffle Speller to enhance communication for this population.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

SSVEP BCI and Eye Tracking Use by Individuals With Late-Stage ALS and Visual Impairments. Peters, B., Bedrick, S., Dudy, S., et al.[2020]

The implanted electrocorticography (ECoG)-based brain-computer interface (BCI) demonstrated stable performance and control over a 36-month period in a patient with late-stage Amyotrophic Lateral Sclerosis (ALS), indicating its long-term efficacy for communication.

Despite a gradual decline in high-frequency power in the motor cortex, the user maintained effective control of the BCI, and the frequency of home use increased, showing successful adoption of the technology for daily communication.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Stability of a chronic implanted brain-computer interface in late-stage amyotrophic lateral sclerosis. Pels, EGM., Aarnoutse, EJ., Leinders, S., et al.[2020]

A large-scale survey of 1918 ALS patients revealed that 77% were interested in brain-machine interfaces (BMIs), with over 60% expressing expectations for various communication and movement supports.

Patients with tracheostomy had high expectations for communication and emergency alarms from BMIs, while those without tracheostomy showed greater interest in movement supports, highlighting the need for tailored BMI solutions based on patient status.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Nationwide survey of 780 Japanese patients with amyotrophic lateral sclerosis: their status and expectations from brain-machine interfaces. Kageyama, Y., He, X., Shimokawa, T., et al.[2021]

A study involving 25 individuals with ALS found that 68% could effectively use a visual P300-based brain-computer interface (BCI) for communication, achieving an average accuracy of 92%, which is sufficient for practical use.

Visual impairments were identified as a major barrier for those who could not use the BCI, suggesting that auditory P300-based BCIs could be a potential alternative for individuals with such disabilities.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Brain-computer interface (BCI) evaluation in people with amyotrophic lateral sclerosis. McCane, LM., Sellers, EW., McFarland, DJ., et al.[2023]

Patients with ALS who have behavioral impairments are less receptive to using brain-computer interfaces (BCIs) for communication, indicating that cognitive and behavioral factors significantly affect acceptance of this technology.

A pilot study revealed that patients' experiences with operating a BCI influenced their opinions on its usefulness, suggesting that user experience should be a key consideration in the design and implementation of assistive devices.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Acceptance of brain-computer interfaces in amyotrophic lateral sclerosis. Geronimo, A., Stephens, HE., Schiff, SJ., et al.[2015]

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