Optimizing BCI-FIT: Brain Computer Interface - Functional Implementation Toolkit
BCI-FIT
1 other identifier
interventional
55
1 country
1
Brief Summary
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P25-P50 for not_applicable
Started Jul 2022
Typical duration for not_applicable
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
July 8, 2020
CompletedFirst Posted
Study publicly available on registry
July 13, 2020
CompletedStudy Start
First participant enrolled
July 15, 2022
CompletedPrimary Completion
Last participant's last visit for primary outcome
May 5, 2025
CompletedStudy Completion
Last participant's last visit for all outcomes
May 5, 2025
CompletedMay 16, 2025
March 1, 2025
2.8 years
July 8, 2020
May 13, 2025
Conditions
Keywords
Outcome Measures
Primary Outcomes (4)
Typing Accuracy
Correct character selections divided by the total character selections in a copy spelling task.
12 data collection sessions over 12 weeks (1 session/week) to assess change
Typing Speed
Correct character selections per minute in a copy spelling task.
12 data collection sessions over 12 weeks (1 session/week) to assess change
Information transfer rate
Time-averaged mutual information between intended and typed symbols from the alphabet, computed using probability distributions in accordance with a language model
12 data collection sessions over 12 weeks (1 session/week) to assess change
User experience
Responses to 10 items on the NASA TLX questionnaire about comfort, workload and satisfaction using the brain-computer interface system during all typing tasks
12 data collection sessions over 12 weeks (1 session/week) to assess change
Study Arms (4)
BCI-FIT multi-modal configuration
EXPERIMENTALFor 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.
Adaptive signal modeling
EXPERIMENTALFor 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.
Active querying techniques
EXPERIMENTALFor 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.
Language modeling
EXPERIMENTALFor 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.
Interventions
Adding a personalized multi-modal access protocol to customize a BCI-FIT access method configuration for each individual end user, based on a combination of user characteristics, clinical expertise, user feedback, and system performance data in the software.
Adding a BCI-FIT adaptive signal modeling that employs transfer learning and on-line model adaptation techniques with noisy labels in the software of this brain-computer interface to eliminate the need for data collection exclusively for model calibration, as well as to address model drift issues associated with drowsiness, fatigue, and other human and environmental factors.
Adding BCI-FIT active querying techniques which are software-based optimal action control policies in the brain-computer interface developed with active and reinforcement learning techniques in order to perform efficient user intent inference to improve the entire speed-accuracy trade-off curve for alternative communication.
Adding vocabulary and location information (called partner and environmental input) to the language models in the brain-computer interface from a user's communication partner.
Eligibility Criteria
You may qualify if:
- Controls
- Able to read and communicate in English
- Capable of participating in study visits lasting 1-3 hours
- Adequate visuospatial skills to select letters, words, or icons to copy or generate messages
- Live within a 2-hour drive of OHSU or is willing to travel to OHSU
- Participants with severe speech and physical impairment:
- Adults between 18-89 years of age
- SSPI that may result from a variety of degenerative or neurodevelopmental conditions, including but not limited to: Duchenne muscular dystrophy, Rett Syndrome, ALS, brainstem CVA, SCI, and Parkinson-plus disorders (MSA, PSP)
- Able to read and communicate in English with speech or AAC device
- Capable of participating in study visits lasting 1-3 hours
- Adequate visuospatial skills to select letters, words or icons to copy or generate basic messages
- Life expectancy greater than 6 months
- Able to give informed consent or assent according to IRB approved policy
You may not qualify if:
- Participants with severe speech and physical impairment:
- Unstable medical conditions (fluctuating health status resulting in multiple hospitalizations within a 6 week interval)
- Unable to tolerate weekly data collection visits
- Photosensitive seizure disorder
- Presence of implanted hydrocephalus shunt, cochlear implant or deep brain stimulator
- High risk of skin breakdown from contact with data acquisition hardware.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Oregon Health & Science University
Portland, Oregon, 97239, United States
Related Publications (1)
Peters B, Celik B, Gaines D, Galvin-McLaughlin D, Imbiriba T, Kinsella M, Klee D, Lawhead M, Memmott T, Smedemark-Margulies N, Wiedrick J, Erdogmus D, Oken B, Vertanen K, Fried-Oken M. RSVP keyboard with inquiry preview: mixed performance and user experience with an adaptive, multimodal typing interface combining EEG and switch input. J Neural Eng. 2025 Feb 4;22(1):10.1088/1741-2552/ada8e0. doi: 10.1088/1741-2552/ada8e0.
PMID: 39793200DERIVED
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Melanie Fried-Oken, PhD
Oregon Health and Science University
Study Design
- Study Type
- interventional
- Phase
- not applicable
- Allocation
- RANDOMIZED
- Masking
- NONE
- Masking Details
- In single case research design, each participant is their own control. The proposed intervention is behavioral and study personnel are aware of each data collection condition.
- Purpose
- BASIC SCIENCE
- Intervention Model
- SEQUENTIAL
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Professor
Study Record Dates
First Submitted
July 8, 2020
First Posted
July 13, 2020
Study Start
July 15, 2022
Primary Completion
May 5, 2025
Study Completion
May 5, 2025
Last Updated
May 16, 2025
Record last verified: 2025-03
Data Sharing
- IPD Sharing
- Will share
- Shared Documents
- ANALYTIC CODE
- Time Frame
- A bcipy.github.io website will be built to share the BCI Python code that is used to collect data and run the brain-computer interface. It is expected that the website will be available in June, 2021 until June, 2025 (during years 2-5 of this award).
- Access Criteria
- Other researchers will have access to neurophysiologic data and outcomes data from the different experimental arms under a data-sharing agreement that provides for: (1) a commitment to using the data only for research purposes and not to identify any individual participant; (2) a commitment to securing the data using appropriate computer technology; and (3) a commitment to destroying or returning the data after analyses are completed.
Three types of information will be available to other researchers. 1. The Python code (called BciPy) that runs the BCI-FIT system is open sourced and available to other laboratories that are building and implementing non-invasive brain-computer interfaces. 2. The datasets of neurophysiological data (EEG, EOG, EMG) collected during use of BciPy in different experimental configurations will be made available. All data are de-identified and maintained in an OHSU-secure BOX folder, an OHSU REDCap database and OHSU approved and compliant human subjects research repository. 3. The typing speed, typing accuracy and user experience data from the four single case research studies will be de-identified and stored in an OHSU REDCap database and OHSU approved and compliant human subjects research repository.