Optimizing BCI-FIT: Brain Computer Interface - Functional Implementation Toolkit

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.

Sex

All

Minimum age

18 Years

Maximum age

89 Years

Inclusion Criteria:

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

Exclusion Criteria:

* 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.

• DERIVEDPeters 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 39793200