While current augmentative and alternative communication (AAC) devices present a variety of access methods for message generation to benefit people with complex communication needs, there still exists a group of literate adults with severe speech and motor impairments (SSMI) who cannot identify a functional means for typing, which is an important tool for computer-based communication. In prior NIDCD- supported research, our research team developed a high performance intracortical brain-computer interface (iBCI) that decodes movement intentions directly from brain activity. This technology has allowed people to control a cursor on a computer screen for communication simply by imagining movements of their own arm. The proposed R01 clinical research will extend this prior work on improving the performance of iBCI systems, as part of the multi-site BrainGate consortium, and utilizing a new fully- implantable, wireless system being developed under separate NIH BRAIN Initiative funded project. The goals of the project are to leverage the discovery of new motor and cognitive signals in human motor cortex to implement and evaluate three new methods for iBCI typing and general purpose computer use: (1) an automatic Error Detect and Undo (EDU) system that uses error-related signals from motor cortex, (2) decoding techniques that create continuous high degree-of-freedom control signals from motor cortex to increase rates of point-and-click iBCI typing in 3D and 4D as compared with 2D, and (3) decoding techniques that classify multiple different ?click? signals from motor cortex. A rigorous uniform experimental procedure with clear evaluation metrics will be utilized across all three Specific Aims, in all three research participants, and at each clinical site using a standardized suite of iBCI tasks, assuring consistency across sessions and participants. Upon completion, this project will advance both the capabilities of iBCIs for communication and our understanding of the function of human motor cortex.

Public Health Relevance

People with severe speech and motor impairment often have difficulty with accessing communication technology, which is increasingly based on interaction with computers. This project aims to develop new methods of interacting with computers via decoding newly discovered brain signals to provide faster and more facile communication. Successful completion of this project could allow this marginalized segment of the US population to once again have the means to manage their health care, form meaningful relationships, have a means for recreation and entertainment, and interact socially with their health care providers and loved ones.

Agency
National Institute of Health (NIH)
Institute
National Institute on Deafness and Other Communication Disorders (NIDCD)
Type
Research Project (R01)
Project #
2R01DC014034-06
Application #
9886850
Study Section
Bioengineering of Neuroscience, Vision and Low Vision Technologies Study Section (BNVT)
Program Officer
Miller, Roger
Project Start
2015-04-01
Project End
2025-08-31
Budget Start
2020-09-01
Budget End
2021-08-31
Support Year
6
Fiscal Year
2020
Total Cost
Indirect Cost
Name
Stanford University
Department
Neurosurgery
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
Brandman, David M; Hosman, Tommy; Saab, Jad et al. (2018) Rapid calibration of an intracortical brain-computer interface for people with tetraplegia. J Neural Eng 15:026007
Pandarinath, Chethan; O'Shea, Daniel J; Collins, Jasmine et al. (2018) Inferring single-trial neural population dynamics using sequential auto-encoders. Nat Methods 15:805-815
Stavisky, Sergey D; Kao, Jonathan C; Nuyujukian, Paul et al. (2018) Brain-machine interface cursor position only weakly affects monkey and human motor cortical activity in the absence of arm movements. Sci Rep 8:16357
Willett, Francis R; Murphy, Brian A; Young, Daniel R et al. (2018) A Comparison of Intention Estimation Methods for Decoder Calibration in Intracortical Brain-Computer Interfaces. IEEE Trans Biomed Eng 65:2066-2078
Willett, Francis R; Murphy, Brian A; Memberg, William D et al. (2017) Signal-independent noise in intracortical brain-computer interfaces causes movement time properties inconsistent with Fitts' law. J Neural Eng 14:026010
Pandarinath, Chethan; Nuyujukian, Paul; Blabe, Christine H et al. (2017) High performance communication by people with paralysis using an intracortical brain-computer interface. Elife 6:
Willett, Francis R; Pandarinath, Chethan; Jarosiewicz, Beata et al. (2017) Feedback control policies employed by people using intracortical brain-computer interfaces. J Neural Eng 14:016001
Gilja, Vikash; Pandarinath, Chethan; Blabe, Christine H et al. (2015) Clinical translation of a high-performance neural prosthesis. Nat Med 21:1142-5
Jarosiewicz, Beata; Sarma, Anish A; Bacher, Daniel et al. (2015) Virtual typing by people with tetraplegia using a self-calibrating intracortical brain-computer interface. Sci Transl Med 7:313ra179
Wu, Anna H; Stanczyk, Frank Z; Wang, Renwei et al. (2013) Sleep duration, spot urinary 6-sulfatoxymelatonin levels and risk of breast cancer among Chinese women in Singapore. Int J Cancer 132:891-6