Signals from the brain can provide non-muscular communication and control channels, or brain-computer interfaces (BCIs), to people with amyotrophic lateral sclerosis (ALS), brainstem stroke, cerebral palsy, or spinal cord injury. BCIs can allow people who are severely paralyzed, or even """"""""locked in,"""""""" to use brain signals to write, communicate with others, control their environments, access the Internet, or operate neuroprostheses. The realization of clinically useful BCI systems requires work in three areas: (1) acquisition of brain signals;(2) signal processing;and (3) clinical implementation. Because these areas involve very different disciplines, research groups usually focus on only one area. Thus, at the beginning of this BRP, despite the exciting achievements of researchers around the world, the field had progressed only to the point of laboratory demonstrations, in large part because achievements in one area were not integrated with those in others. In the past grant period, this BRP changed that landscape. First, it developed and disseminated to more than 160 research groups a general-purpose BCI software platform, called BCI2000, that facilitates all aspects of interdisciplinary BCI research and development, from laboratory to home. Furthermore, it used BCI2000 to develop the first BCI system designed for independent home use, and successfully tested this prototype in long-term home use by a small group of people severely disabled by ALS. Building on this work, the goal of this renewal proposal is to establish the first vertically-integrated BCI research and development program, and use it to produce BCI systems that are fully practical for independent use in clinical and home settings. The proposed program extends from signal acquisition, to signal processing, to application development and clinical implementation. By including and coordinating the activities and achievements in these different areas, this program will create and validate the first BCI systems suitable for widespread independent use by people with severe motor disabilities. Each BRP partner is in the forefront of one or more of the essential research areas, from hardware design to clinical testing. In limited ways, they already collaborate with one another. Working closely together and implementing new ideas, they will: (1) improve signal acquisition by developing more reliable, robust, and convenient chronic methods for recording electroencephalographic activity (EEG) and for exploring the BCI capabilities of electrocorticography (ECoG);(2) optimize adaptive feature extraction and translation algorithms for these signals;and (3) incorporate the results into BCI systems that are fully practical for home and clinical settings and establish the value of these systems for daily use by people with severe motor disabilities. By achieving these aims, disseminating the resulting technology, and providing other researchers access to its vertically-integrated framework, this BRP program should enable BCI research to produce BCIs that actually improve the lives of people with severe motor disabilities.

Public Health Relevance

Brain-computer interfaces (BCIs) can restore communication and control to people severely paralyzed or even """"""""locked-in"""""""" by amyotrophic lateral sclerosis (ALS), brainstem stroke, cerebral palsy, or other devastating neuromuscular disorders. The goal of this Bioengineering Research Partnership proposal is to establish the first comprehensive BCI research and development program and use it to produce the first BCI systems suitable for widespread independent home use by people with severe motor disabilities.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB000856-09
Application #
8116452
Study Section
Special Emphasis Panel (ZRG1-MDCN-G (02))
Program Officer
Peng, Grace
Project Start
2002-09-20
Project End
2013-06-30
Budget Start
2011-07-01
Budget End
2012-06-30
Support Year
9
Fiscal Year
2011
Total Cost
$1,001,770
Indirect Cost
Name
Wadsworth Center
Department
Type
DUNS #
153695478
City
Menands
State
NY
Country
United States
Zip Code
12204
Remsik, Alexander B; Dodd, Keith; Williams Jr, Leroy et al. (2018) Behavioral Outcomes Following Brain-Computer Interface Intervention for Upper Extremity Rehabilitation in Stroke: A Randomized Controlled Trial. Front Neurosci 12:752
Mohanty, Rosaleena; Sinha, Anita M; Remsik, Alexander B et al. (2018) Machine Learning Classification to Identify the Stage of Brain-Computer Interface Therapy for Stroke Rehabilitation Using Functional Connectivity. Front Neurosci 12:353
Mohanty, Rosaleena; Sinha, Anita M; Remsik, Alexander B et al. (2018) Early Findings on Functional Connectivity Correlates of Behavioral Outcomes of Brain-Computer Interface Stroke Rehabilitation Using Machine Learning. Front Neurosci 12:624
Jian, Wenjuan; Chen, Minyou; McFarland, Dennis J (2017) Use of phase-locking value in sensorimotor rhythm-based brain-computer interface: zero-phase coupling and effects of spatial filters. Med Biol Eng Comput 55:1915-1926
Jian, Wenjuan; Chen, Minyou; McFarland, Dennis J (2017) EEG based zero-phase phase-locking value (PLV) and effects of spatial filtering during actual movement. Brain Res Bull 130:156-164
Dodd, Keith C; Nair, Veena A; Prabhakaran, Vivek (2017) Role of the Contralesional vs. Ipsilesional Hemisphere in Stroke Recovery. Front Hum Neurosci 11:469
McFarland, Dennis J; Parvaz, Muhammad A; Sarnacki, William A et al. (2017) Prediction of subjective ratings of emotional pictures by EEG features. J Neural Eng 14:016009
Riès, Stephanie K; Dhillon, Rummit K; Clarke, Alex et al. (2017) Spatiotemporal dynamics of word retrieval in speech production revealed by cortical high-frequency band activity. Proc Natl Acad Sci U S A 114:E4530-E4538
Schalk, Gerwin; Marple, Joshua; Knight, Robert T et al. (2017) Instantaneous voltage as an alternative to power- and phase-based interpretation of oscillatory brain activity. Neuroimage 157:545-554
Ryan, D B; Townsend, G; Gates, N A et al. (2017) Evaluating brain-computer interface performance using color in the P300 checkerboard speller. Clin Neurophysiol 128:2050-2057

Showing the most recent 10 out of 174 publications