Difficulty with communication is a widespread problem, reported by more than 2 million people in the US. Communication may be a particular challenge for people with total paralysis (Locked-in Syndrome or LIS) from disorders such as brainstem strokes or advanced Amyotrophic Lateral Sclerosis (ALS). People with these disorders rely on augmentative and alternative communication (AAC) technology that can be inefficient, often not designed with their unique access needs in mind, and therefore frustrating to implement successfully. Brain-computer interface (BCI) systems provide one promising avenue for restoring communication capabilities to people with LIS. However, these systems currently offer performance well below that achievable by able-bodied computer users. The overall objective of the proposed research is to evaluate advanced neural decoding methods and interfaces for a high-performance communication system for people with LIS. To accomplish this, we propose 3 Specific Aims as follows.
Aim 1 : We will evaluate whether advanced decoding techniques developed in the animal laboratory can improve continuous point-and-click control of a computer cursor from signals decoded from human motor cortex.
Aim 2 : We will optimize parameters for high-speed discrete target selection (i.e., typing) by decoding movement intent from pre-motor cortex.
Aim 3 : We will test new communication interfaces based on the results of Aims 1 and 2 against each participant's usual AAC system, with the goal of providing faster and easier communication. Finally, we will assess the ability of research participants to control a commercial computer GUI and typing interface while switching between continuous and discrete decoding.

Public Health Relevance

Difficulty in communication is a widespread problem, with an estimated 2.5 million people in the US reporting trouble with speech and communication. People with late stage amyotrophic lateral sclerosis (ALS) and locked in syndrome (LIS) from brainstem stroke have unique communication needs that are difficult to address with augmentative and alternative communication technology. Our proposed research aims to dramatically increase the performance of computerized communication systems driven by decoded brain activity, with the goal of improving the quality of life for people with unique communication needs.

Agency
National Institute of Health (NIH)
Institute
National Institute on Deafness and Other Communication Disorders (NIDCD)
Type
Research Project (R01)
Project #
5R01DC014034-03
Application #
9251796
Study Section
Special Emphasis Panel (ZDC1-SRB-L (42))
Program Officer
Miller, Roger
Project Start
2015-04-01
Project End
2020-03-31
Budget Start
2017-04-01
Budget End
2018-03-31
Support Year
3
Fiscal Year
2017
Total Cost
$684,457
Indirect Cost
$193,155
Name
Stanford University
Department
Neurosurgery
Type
Schools of Medicine
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94304
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