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

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.

Conditions

• Amyotrophic Lateral Sclerosis
• Brainstem Stroke
• Muscular Dystrophies
• Parkinson's Disease and Parkinsonism
• Multiple System Atrophy
• Brain Tumor Adult
• Spinal Cord Injuries
• Locked-in Syndrome

Interventions

BEHAVIORAL

BCI-FIT multi-modal access

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.

BEHAVIORAL

BCI-FIT adaptive signal modeling

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.

BEHAVIORAL

BCI-FIT active querying

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.

BEHAVIORAL

BCI-FIT language modeling

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.

Sponsors & Collaborators

• Oregon Health and Science University

  lead OTHER

Principal Investigators

• Melanie Fried-Oken, PhD · Oregon Health and Science University

Study Design

Allocation

RANDOMIZED

Purpose

BASIC_SCIENCE

Masking

NONE

Model

SEQUENTIAL

Eligibility

Min Age

18 Years

Max Age

89 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2022-07-15

Primary Completion

2025-05-05

Completion

2025-05-05

Countries

• United States

Study Locations