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

55 Participants Needed

Trial Summary

What is the purpose of this trial?

This project adds to non-invasive BCIs for communication for adults with severe speech and physical impairments due to neurodegenerative diseases. Researchers will optimize \& adapt BCI signal acquisition, signal processing, natural language processing, \& clinical implementation. BCI-FIT relies on active inference and transfer learning to customize a completely adaptive intent estimation classifier to each user's multi-modality signals simultaneously. 3 specific aims are: 1. develop \& evaluate methods for on-line \& robust adaptation of multi-modal signal models to infer user intent; 2. develop \& evaluate methods for efficient user intent inference through active querying, and 3. integrate partner \& environment-supported language interaction \& letter/word supplementation as input modality. The same 4 dependent variables are measured in each SA: typing speed, typing accuracy, information transfer rate (ITR), \& user experience (UX) feedback. Four alternating-treatments single case experimental research designs will test hypotheses about optimizing user performance and technology performance for each aim.Tasks include copy-spelling with BCI-FIT to explore the effects of multi-modal access method configurations (SA1.3a), adaptive signal modeling (SA1.3b), \& active querying (SA2.2), and story retell to examine the effects of language model enhancements. Five people with SSPI will be recruited for each study. Control participants will be recruited for experiments in SA2.2 and SA3.4. Study hypotheses are: (SA1.3a) A customized BCI-FIT configuration based on multi-modal input will improve typing accuracy on a copy-spelling task compared to the standard P300 matrix speller. (SA1.3b) Adaptive signal modeling will allow people with SSPI to typing accurately during a copy-spelling task with BCI-FIT without training a new model before each use. (SA2.2) Either of two methods of adaptive querying will improve BCI-FIT typing accuracy for users with mediocre AUC scores. (SA3.4) Language model enhancements, including a combination of partner and environmental input and word completion during typing, will improve typing performance with BCI-FIT, as measured by ITR during a story-retell task. Optimized recommendations for a multi-modal BCI for each end user will be established, based on an innovative combination of clinical expertise, user feedback, customized multi-modal sensor fusion, and reinforcement learning.

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 data supports the effectiveness of the BCI-FIT treatment for ALS?

Research shows that brain-computer interfaces (BCIs) can help people with late-stage ALS communicate more effectively. For example, one study found that a BCI system improved typing accuracy significantly compared to traditional methods, with one participant achieving up to 100% accuracy. This suggests that BCI technology, like BCI-FIT, may be promising for improving communication in ALS patients. ^{1 2 3 4 5}

Is the BCI-FIT treatment safe for humans?

The research does not provide specific safety data for BCI-FIT, but it mentions that people with ALS have used brain-computer interfaces (BCIs) at home for extended periods, and some are willing to undergo surgery to obtain a BCI, suggesting a level of safety and acceptance. ^{2 3 4 6 7}

How is the BCI-FIT treatment different from other treatments for ALS?

BCI-FIT is unique because it uses a brain-computer interface (BCI) to help people with ALS communicate without moving, by interpreting brain signals. Unlike traditional treatments that may focus on slowing disease progression, this approach directly addresses communication challenges in late-stage ALS, allowing for independent home use and potentially improving quality of life. ^{2 4 7 8 9}

Research Team

Melanie Fried-Oken, PhD

Principal Investigator

Oregon Health and Science University

Eligibility Criteria

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

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

Treatment Details

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.

Participant Groups

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+

Findings from Research

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]

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]

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]

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]

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 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 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]

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]

References

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

SSVEP BCI and Eye Tracking Use by Individuals With Late-Stage ALS and Visual Impairments. [2020]

2. Netherlands pubmed.ncbi.nlm.nih.gov

Stability of a chronic implanted brain-computer interface in late-stage amyotrophic lateral sclerosis. [2020]

3. England pubmed.ncbi.nlm.nih.gov

Acceptance of brain-computer interfaces in amyotrophic lateral sclerosis. [2015]

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

Independent home use of a brain-computer interface by people with amyotrophic lateral sclerosis. [2023]

5. England pubmed.ncbi.nlm.nih.gov

Cognitive assessment in Amyotrophic Lateral Sclerosis by means of P300-Brain Computer Interface: a preliminary study. [2018]

6. Germany pubmed.ncbi.nlm.nih.gov

Nationwide survey of 780 Japanese patients with amyotrophic lateral sclerosis: their status and expectations from brain-machine interfaces. [2021]

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

What would brain-computer interface users want? Opinions and priorities of potential users with amyotrophic lateral sclerosis. [2021]

8. United Arab Emirates pubmed.ncbi.nlm.nih.gov

Applying Auto-Regressive Model's Yule-Walker Approach to Amyotrophic Lateral Sclerosis (ALS) patients' Data. [2020]

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

Brain-computer interface (BCI) evaluation in people with amyotrophic lateral sclerosis. [2023]