Passive brain computer interfaces (BCIs) have commonly been used to measure cognitive workload and focus, but are applicable in many other fields. This dissertation explores a variety of novel fields for BCIs, including STEM education, language acquisition, human-agent interaction, and multitasking. Through my work, I show two BCIs, one for education and one for work, that set the foundation for systems that expand outside of the contexts of cognitive workload and focus. I conducted five studies that show how to detect brain signals related to learning, multitasking, and teamwork with two separate neuroimaging methods, created a novel, browser based BCI, integrated new functionality into an existing BCI, and developed a faster and more accurate tool for finding similarities between brain signals. This dissertation demonstrates the variety of cognitive states that are detectable by portable, non-invasive neuroimaging devices and how they can be applied into everyday environments.

Creator

• Howell-Munson, Alicia

Contributors

• Lammert, Adam
• Stanley, David
• Solovey, Erin
• Paffenroth, Randy Clinton

Degree

• PhD

Unit

• Bioinformatics & Computational Biology

Publisher

• Worcester Polytechnic Institute

Identifier

• etd-106271

Keyword

• brain-computer interface
• education data mining
• non-invasive neuorimaging

Advisor

• Solovey, Erin

Orcid

• https://orcid.org/0000-0002-7390-9675

Committee

• Lammert, Adam
• Stanley, David
• Paffenroth, Randy

Defense date

• 2023-04-20

Year

• 2023

Date created

• 2023-04-27

Resource type

• Dissertation

Source

• etd-106271

Rights statement

• In Copyright

Last modified

• 2023-10-09