Amyotrophic Lateral SclerosisBCI-FITNCT04468919
Optimizing BCI-FIT: Brain Computer Interface - Functional Implementation Toolkit
A study testing BCI-FIT multi-modal access, BCI-FIT adaptive signal modeling and BCI-FIT active querying in people with Amyotrophic Lateral Sclerosis and Brainstem Stroke, run by Oregon Health and Science University at 1 site in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 89 yearsHealthy volunteers can take part
- Conditions the study is for
- Amyotrophic Lateral Sclerosis, Brainstem Stroke, Muscular Dystrophies
- What is being tested?
- BCI-FIT multi-modal access, BCI-FIT adaptive signal modeling and BCI-FIT active querying
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 55 participants
- Where
- 1 siteUnited States
- Study dates
- 15 Jul 2022to 5 May 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 89 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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:
Show all 13Show fewer
- 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
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
United States1 site
Oregon Health & Science University
Portland, Oregon, 97239
About this study
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.
Read the detailed description
For each specific aim, the development of new assistive technology BCI access methods will be evaluated in one or more experiments using alternating-treatments single-case research design (SCRD) with healthy controls and/or participants with SSPI. SCRD is ideal for examining small, heterogenous populations such as individuals with SSPI. It allows for detailed examination of performance trends and changes over time, and for participant-specific modifications to the intervention as part of an iterative design process. Because each participant serves as their own control, a sample size of five is sufficient to demonstrate and replicate an initial effect. Please see the Statistical Design and Power section for additional information about SCRD and data visualization and analysis.
A total of 60 participants will evaluate the BCI advancements; 15 individuals with SSPI and 45 controls. Participants with SSPI who currently have a reliable means of communication, either using speech and/or an AAC device, will be enrolled. All participants will be within the ages of 18-89 years (NIH-defined adults), with an equal number of men and women. Healthy controls will be matched for age, gender, and education level. In SCRD studies, each participant serves as their own control, so participants will experience all of the baseline and intervention conditions included in each individual study, as described below. Condition order will be randomized in the alternating-treatments, controlled such that each participant completes an equal number of sessions with each intervention, with no more than two consecutive sessions with the same intervention. Blinding is not possible as each subject must know their condition in an alternating treatment design.
All study visits with people with SSPI will be conducted in participants' homes by OHSU staff. Study visits with healthy controls will take place at the OHSU BCI laboratory. For all typing tasks, participants are seated approximately 75cm from an LCD display, set up for the BCI-FIT system. Depending on the user's customized BCI-FIT configuration (procedures described in SA1.1), one or more of the following control signals will be used in each typing session: EEG (ERP, Code or SSVEP), eye movements (gaze position or velocity), or binary switches. The experiments for SA1.3a, SA1.3b, and SA2.2 all involve copy-spelling tasks, in which participants will copy five common 5-letter English words of approximately equal typing difficulty (according to LM input), and correct mistakes by choosing the backspace character when appropriate. Individual signal models will be initialized to population models and will be personalized and refined with each acquired copy-spelling task data set. The experiment for SA3.4 involves a story-retell task, described below in the paragraph about that experiment.
Experiment 1.3a will test the hypothesis that a customized BCI-FIT configuration based on multi-modal input will improve typing accuracy on a copy-spelling task compared to a standard P300 matrix speller. We will pilot test new multi-modal input features with control participants before every SCRD with participants who present with SSPI. It will include five participants with SSPI in an alternating-treatments SCRD and will concentrate on typing accuracy as the primary DV. An initial baseline phase will involve weekly copy-spelling sessions with each participant's existing access method. Three or more baseline sessions will be conducted until stable performance is observed, then the alternating-treatments phase will begin. Treatments consist of two different BCI-FIT configurations: 1) a multi-modal configuration chosen by a combination of the approaches described in SA1.1. (clinically-supported and performance data-supported) and 2) a standard P300 matrix speller. In weekly data-collection visits, participants will complete copy-spelling sessions with each BCI-FIT configuration, with counterbalanced session order. Participants complete at least five sessions with each configuration, more if needed to achieve stable performance.
In Experiment 1.3b, it is hypothesized that adaptive individualized signal modeling will allow people with SSPI to type accurately during a copy-spelling task with BCI-FIT without training a new model for each use. This experiment will also include five participants with SSPI in an alternating-treatments SCRD with typing accuracy as the primary DV. In this study, no baseline is planned, as the comparison of interest is between versions of BCI-FIT with and without adaptive signal modeling. Initially, each participant will complete system optimization procedures described in SA1.1 and SA1.2 to identify their customized BCI-FIT configuration. During each visit, in the alternative treatments experiment, the participant will attempt three copy-spelling sessions with their customized BCI-FIT configuration, using three different model types: (1) a single calibration completed by the same user immediately before copy spelling; (2) multiple calibrations completed by the same user on previous days; (3) multiple calibrations completed by other users. Data will be graphed and analyzed separately (following procedures in the Statistical Design and Power section) to evaluate effects on performance with both system versions.
The experiment in SA2.2 will test the hypothesis that either of two methods of adaptive querying will improve BCI-FIT typing accuracy for users with mediocre AUC scores. It will include five controls and five participants with SSPI, each with an AUC score in the range of 70-80%. (Based on pilot testing, adaptive querying is expected to provide the most benefit to users with this level of baseline performance.) The experiment will follow an alternating-treatments SCRD. In the baseline phase, participants will complete weekly copy-spelling sessions with BCI-FIT without adaptive querying. Each weekly visit will include two copy-spelling sessions with BCI-FIT either with or without adaptive querying techniques. Condition order will be counterbalanced such that conditions occur in random order (with no more than two instances of the same condition in a row) and participants will experience each condition an equal number of times (at least five times each, until stable performance is achieved).
The experiment in SA3.4 will use an alternating-treatments SCRD experiment to test the hypothesis that 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. This experiment will include five controls and five participants with SSPI, each paired with a partner to provide partner input (total enrollment of 10 dyads). In each weekly data-collection visit, participants will engage in two structured story-retell activities, one with and one without the enhanced language model features. Condition order will be counterbalanced such that conditions occur in random order (with no more than two instances of the same condition in a row) and participants will experience each condition an equal number of times (at least five times each, until stable performance is achieved). The story-retell activity will involve the participant watching a short video along with a communication partner, then using BCI-FIT to answer questions posed by a third person. The primary DV in this experiment will be ITR.
- Conditions
- Amyotrophic Lateral Sclerosis, Brainstem Stroke, Muscular Dystrophies, Parkinson's Disease and Parkinsonism, Multiple System Atrophy, Brain Tumor Adult
- Being tested
- BCI-FIT multi-modal access, BCI-FIT adaptive signal modeling, BCI-FIT active querying, BCI-FIT language modeling
- Keywords
- brain-computer interface
- Medical subject headings
- Amyotrophic Lateral Sclerosis, Brain Stem Infarctions, Muscular Dystrophies, Parkinson Disease, Parkinsonian Disorders, Multiple System Atrophy, Spinal Cord Injuries, Locked-In Syndrome
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
BCI-FIT multi-modal accessBehavioural
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.
BCI-FIT adaptive signal modelingBehavioural
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.
BCI-FIT active queryingBehavioural
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.
BCI-FIT language modelingBehavioural
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.
| Study group | Role | What they receive |
|---|---|---|
| BCI-FIT multi-modal configuration | Experimental | 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. Receives: Behavioral: BCI-FIT multi-modal access |
| Adaptive signal modeling | Experimental | 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. Receives: Behavioral: BCI-FIT adaptive signal modeling |
| Active querying techniques | Experimental | 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. Receives: Behavioral: BCI-FIT active querying |
| Language modeling | Experimental | 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. Receives: Behavioral: BCI-FIT language modeling |
What is being measured
The primary outcome is the main question the study is designed to answer.
Typing Accuracy
Correct character selections divided by the total character selections in a copy spelling task.
Measured: 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.
Measured: 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
Measured: 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
Measured: 12 data collection sessions over 12 weeks (1 session/week) to assess change
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Jul 2020
- Study start15 Jul 2022
- Primary completion5 May 2025
- Study completion5 May 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Amyotrophic Lateral SclerosisStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Basic science
- Allocation
- Randomised
- Intervention model
- Sequential
- Model description
- Single case research design with: Alternating treatments without baseline for experiments 1.3a, 2.2; Alternating treatments without baseline for experiments 1.3b and 3.4
- Masking
- None (open label)
- Enrolment
- 55 participants (actual)
Sponsor and investigators
- Lead sponsor
- Oregon Health and Science University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Melanie Fried-Oken, Oregon Health and Science University
- Investigators
- Melanie Fried-Oken, PhD, Principal investigator, Oregon Health and Science University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04468919
- Sponsor's protocol ID
- STUDY00015331
- Acronym
- BCI-FIT
- Status last verified
- Mar 2025
- First submitted
- 8 Jul 2020
- Last updated
- 16 May 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: 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.
Publications
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.
PubMed 39793200
Study at a glance
From the registry record, last updated by the sponsor on 16 May 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Optimizing BCI-FIT: Brain Computer Interface - Functional Implementation Toolkit
- ClinicalTrials.gov ID
- NCT04468919
- Acronym / sponsor ID
- BCI-FIT / STUDY00015331
- Conditions
- Amyotrophic Lateral Sclerosis, Brainstem Stroke, Muscular Dystrophies, Parkinson's Disease and Parkinsonism, Multiple System Atrophy, Brain Tumor Adult
- Keywords
- brain-computer interface
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 55 participants (actual)
- Lead sponsor
- Oregon Health and Science UniversityUniversity, hospital or other
- Study start
- 15 Jul 2022 (actual)
- Primary completion
- 5 May 2025 (actual)
- Study completion
- 5 May 2025 (actual)
- First posted
- 13 Jul 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
For each specific aim, the development of new assistive technology BCI access methods will be evaluated in one or more experiments using alternating-treatments single-case research design (SCRD) with healthy controls and/or participants with SSPI. SCRD is ideal for examining small, heterogenous populations such as individuals with SSPI. It allows for detailed examination of performance trends and changes over time, and for participant-specific modifications to the intervention as part of an iterative design process. Because each participant serves as their own control, a sample size of five is sufficient to demonstrate and replicate an initial effect. Please see the Statistical Design and Power section for additional information about SCRD and data visualization and analysis.
A total of 60 participants will evaluate the BCI advancements; 15 individuals with SSPI and 45 controls. Participants with SSPI who currently have a reliable means of communication, either using speech and/or an AAC device, will be enrolled. All participants will be within the ages of 18-89 years (NIH-defined adults), with an equal number of men and women. Healthy controls will be matched for age, gender, and education level. In SCRD studies, each participant serves as their own control, so participants will experience all of the baseline and intervention conditions included in each individual study, as described below. Condition order will be randomized in the alternating-treatments, controlled such that each participant completes an equal number of sessions with each intervention, with no more than two consecutive sessions with the same intervention. Blinding is not possible as each subject must know their condition in an alternating treatment design.
All study visits with people with SSPI will be conducted in participants' homes by OHSU staff. Study visits with healthy controls will take place at the OHSU BCI laboratory. For all typing tasks, participants are seated approximately 75cm from an LCD display, set up for the BCI-FIT system. Depending on the user's customized BCI-FIT configuration (procedures described in SA1.1), one or more of the following control signals will be used in each typing session: EEG (ERP, Code or SSVEP), eye movements (gaze position or velocity), or binary switches. The experiments for SA1.3a, SA1.3b, and SA2.2 all involve copy-spelling tasks, in which participants will copy five common 5-letter English words of approximately equal typing difficulty (according to LM input), and correct mistakes by choosing the backspace character when appropriate. Individual signal models will be initialized to population models and will be personalized and refined with each acquired copy-spelling task data set. The experiment for SA3.4 involves a story-retell task, described below in the paragraph about that experiment.
Experiment 1.3a will test the hypothesis that a customized BCI-FIT configuration based on multi-modal input will improve typing accuracy on a copy-spelling task compared to a standard P300 matrix speller. We will pilot test new multi-modal input features with control participants before every SCRD with participants who present with SSPI. It will include five participants with SSPI in an alternating-treatments SCRD and will concentrate on typing accuracy as the primary DV. An initial baseline phase will involve weekly copy-spelling sessions with each participant's existing access method. Three or more baseline sessions will be conducted until stable performance is observed, then the alternating-treatments phase will begin. Treatments consist of two different BCI-FIT configurations: 1) a multi-modal configuration chosen by a combination of the approaches described in SA1.1. (clinically-supported and performance data-supported) and 2) a standard P300 matrix speller. In weekly data-collection visits, participants will complete copy-spelling sessions with each BCI-FIT configuration, with counterbalanced session order. Participants complete at least five sessions with each configuration, more if needed to achieve stable performance.
In Experiment 1.3b, it is hypothesized that adaptive individualized signal modeling will allow people with SSPI to type accurately during a copy-spelling task with BCI-FIT without training a new model for each use. This experiment will also include five participants with SSPI in an alternating-treatments SCRD with typing accuracy as the primary DV. In this study, no baseline is planned, as the comparison of interest is between versions of BCI-FIT with and without adaptive signal modeling. Initially, each participant will complete system optimization procedures described in SA1.1 and SA1.2 to identify their customized BCI-FIT configuration. During each visit, in the alternative treatments experiment, the participant will attempt three copy-spelling sessions with their customized BCI-FIT configuration, using three different model types: (1) a single calibration completed by the same user immediately before copy spelling; (2) multiple calibrations completed by the same user on previous days; (3) multiple calibrations completed by other users. Data will be graphed and analyzed separately (following procedures in the Statistical Design and Power section) to evaluate effects on performance with both system versions.
The experiment in SA2.2 will test the hypothesis that either of two methods of adaptive querying will improve BCI-FIT typing accuracy for users with mediocre AUC scores. It will include five controls and five participants with SSPI, each with an AUC score in the range of 70-80%. (Based on pilot testing, adaptive querying is expected to provide the most benefit to users with this level of baseline performance.) The experiment will follow an alternating-treatments SCRD. In the baseline phase, participants will complete weekly copy-spelling sessions with BCI-FIT without adaptive querying. Each weekly visit will include two copy-spelling sessions with BCI-FIT either with or without adaptive querying techniques. Condition order will be counterbalanced such that conditions occur in random order (with no more than two instances of the same condition in a row) and participants will experience each condition an equal number of times (at least five times each, until stable performance is achieved).
The experiment in SA3.4 will use an alternating-treatments SCRD experiment to test the hypothesis that 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. This experiment will include five controls and five participants with SSPI, each paired with a partner to provide partner input (total enrollment of 10 dyads). In each weekly data-collection visit, participants will engage in two structured story-retell activities, one with and one without the enhanced language model features. Condition order will be counterbalanced such that conditions occur in random order (with no more than two instances of the same condition in a row) and participants will experience each condition an equal number of times (at least five times each, until stable performance is achieved). The story-retell activity will involve the participant watching a short video along with a communication partner, then using BCI-FIT to answer questions posed by a third person. The primary DV in this experiment will be ITR.
Study design
- Primary purpose
- Basic science
- Allocation
- Randomised
- Intervention model
- Sequential
- Masking
- None (open label)
- Number of arms
- 4
- Model description
- Single case research design with: Alternating treatments without baseline for experiments 1.3a, 2.2; Alternating treatments without baseline for experiments 1.3b and 3.4
| Arm | Type | Receives |
|---|---|---|
| BCI-FIT multi-modal configuration | Experimental | Behavioral: BCI-FIT multi-modal accessFor 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 | Experimental | Behavioral: BCI-FIT adaptive signal modelingFor 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 | Experimental | Behavioral: BCI-FIT active queryingFor 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 | Experimental | Behavioral: BCI-FIT language modelingFor 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. |
Who may qualify
- Ages
- 18 to 89 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (13 listed)
- 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
Key exclusion criteria (6 listed)
- Participants with severe speech and physical impairment:
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| BCI-FIT multi-modal accessAdding 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. | Behavioural | BCI-FIT multi-modal configuration |
| BCI-FIT adaptive signal modelingAdding 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. | Behavioural | Adaptive signal modeling |
| BCI-FIT active queryingAdding 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. | Behavioural | Active querying techniques |
| BCI-FIT language modelingAdding vocabulary and location information (called partner and environmental input) to the language models in the brain-computer interface from a user's communication partner. | Behavioural | Language modeling |
Primary outcome
Typing Accuracy
Time frame: 12 data collection sessions over 12 weeks (1 session/week) to assess change
Correct character selections divided by the total character selections in a copy spelling task.
Typing Speed
Time frame: 12 data collection sessions over 12 weeks (1 session/week) to assess change
Correct character selections per minute in a copy spelling task.
Information transfer rate
Time frame: 12 data collection sessions over 12 weeks (1 session/week) to assess change
Time-averaged mutual information between intended and typed symbols from the alphabet, computed using probability distributions in accordance with a language model
User experience
Time frame: 12 data collection sessions over 12 weeks (1 session/week) to assess change
Responses to 10 items on the NASA TLX questionnaire about comfort, workload and satisfaction using the brain-computer interface system during all typing tasks
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 May 2025.
- Derived from this study: PubMed 39793200Peters 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.
- Individual participant dataSharing planned: Yes. 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Melanie Fried-Oken, PhD principal investigator
Oregon Health and Science University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing BCI-FIT multi-modal access, BCI-FIT adaptive signal modeling and BCI-FIT active querying in people with Amyotrophic Lateral Sclerosis and Brainstem Stroke, run by Oregon Health and Science University at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 13 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 16 May 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Typing Accuracy (time frame: 12 data collection sessions over 12 weeks (1 session/week) to assess change). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04468919, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 16 May 2025.