| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
|
| Initial Amendment Date: | May 3, 2017 |
| Latest Amendment Date: | May 3, 2017 |
| Award Number: | 1730705 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Wendy Nilsen
wnilsen@nsf.gov (703)292-2568 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | August 1, 2017 |
| End Date: | July 31, 2018 (Estimated) |
| Total Intended Award Amount: | $82,738.00 |
| Total Awarded Amount to Date: | $82,738.00 |
| Funds Obligated to Date: |
|
| History of Investigator: |
|
| Recipient Sponsored Research Office: |
2130 FULTON ST SAN FRANCISCO CA US 94117 (415)422-5203 |
| Sponsor Congressional District: |
|
| Primary Place of Performance: |
CA US 94117-1080 |
| Primary Place of
Performance Congressional District: |
|
| Unique Entity Identifier (UEI): |
|
| Parent UEI: |
|
| NSF Program(s): | CCRI-CISE Cmnty Rsrch Infrstrc |
| Primary Program Source: |
|
| Program Reference Code(s): |
|
| Program Element Code(s): |
|
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
Brain-computer interfaces (BCIs) are incredibly helpful for physically disabled users, allowing them to provide input through controlling their brain activity, which is sensed through specialized hardware. Such hardware is rapidly becoming less expensive and obtrusive, paving the way for developing BCIs for a wider audience. This project is about developing an infrastructure for BCI-based sensing of a person's cognitive workload, then using this information to structure how the computer interacts with the person. The infrastructure will enable research around several kinds of adaptive interface, including educational tools that use brain activity to make lessons harder or easier and ways to evaluate the comprehensibility and difficulty of data visualizations. The research has the potential to impact society by improving education, data analysis, and interfaces for both people in general and people with disabilities. Further, the lead researcher will use the infrastructure and enabled research to attract students from groups traditionally underrepresented in computer science, as well as to support courses on affective computing and data visualization.
The funding supports the infrastructure necessary to build and test adaptive user interfaces that respond intelligently to user cognitive state in real-time. In particular, the PIs will acquire a multichannel frequency domain fNIRS (functional near infrared spectroscopy) device and develop the algorithms required to process fNIRS signals to extract workload information. Signals will be pre-processed to account for motion artifacts and to derive meaningful features (notably, mean and linear regression slope based no the literature) for each of the 16 channels provided by the fNIRS device. The team plans to use personalized support vector machine-based models to distinguish between high and low workload states, based on prior work by the PIs that shows the effectiveness of this approach.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
Brain-computer interfaces (BCIs) are incredibly helpful for physically disabled users, allowing them to provide input through controlling their brain activity, which is sensed through specialized hardware. Such hardware is rapidly becoming less expensive and obtrusive, paving the way for developing BCIs for a wider audience. This project is about developing an infrastructure for BCI-based sensing of a person's cognitive workload, then using this information to structure how the computer interacts with the person.
We have completed the project outcome of this award which was to build and test the infrastructure necessary for adaptive user interfaces that respond intelligently to user cognitive state in real-time. In particular, we acquired a multichannel frequency domain fNIRS (functional near infrared spectroscopy) device and developed the algorithms required to process fNIRS signals to extract workload information. Signals are pre-processed to account for motion artifacts and to derive meaningful features (notably, mean and linear regression slope based on the literature) for each of the 16 channels provided by the fNIRS device. We used personalized support vector machine-based models to distinguish between high and low workload states, based on prior work by the PIs that shows the effectiveness of this approach. We have evaluated the infrastructure built and it shows clear differences in cognitive workload in the brain signals of participants. The testing is done using stimuli that is well known to elicit high and low cognitive workload in the scientific literature.
The building and evaluation of the infrastructure enabled students from groups that are traditionally underrepresented in computer science to work on the research. Students have worked on this project through the Human-Computer Interaction Lab at the University of San Francisco which has a proportion of women, students from underrepresented minorities and students with disabilities. Students worked as research assistants either through internal funding, volunteerism, or for study credit through directed studies. Students will continue working on the infrastructure, both in terms of developing and testing the algorithms to distinguish between high and low cognitive workload, and also in using the infrastructure for research into other areas. The infrastructure created with this award will enable research around several kinds of adaptive interface, including educational tools that use brain activity to make lessons harder or easier and ways to evaluate the comprehensibility and difficulty of data visualizations. The research has the potential to impact society by improving education, data analysis, and interfaces for both people in general and people with disabilities.
Last Modified: 11/28/2018
Modified by: Beste Yuksel
Please report errors in award information by writing to: awardsearch@nsf.gov.
