Using EEG Signal to Analyze IS Decision Making Cognitive Processes

Nabila Salma (), Bin Mai (), Kamesh Namuduri (), Rasel Mamun (), Yassir Hashem (), Hassan Takabi (), Natalie Parde () and Rodney Nielsen ()
Additional contact information
Nabila Salma: University of North Texas
Bin Mai: University of North Texas
Kamesh Namuduri: University of North Texas
Rasel Mamun: University of North Texas
Yassir Hashem: University of North Texas
Hassan Takabi: University of North Texas
Natalie Parde: University of North Texas
Rodney Nielsen: University of North Texas

A chapter in Information Systems and Neuroscience, 2018, pp 211-218 from Springer

Abstract: Abstract In this paper, we demonstrate how electroencephalograph (EEG) signals can be used to analyze people’s mental states while engaging in cognitive processes during IS decision-making. We design an experiment in which participants are required to complete several cognitive tasks with various cognitive demands and under various stress levels. We collect their EEG signals as they perform the tasks and analyze those signals to infer their mental state (e.g., relaxation level and stress level) based on their EEG signal power.

Keywords: EEG; Decision making; Signal processing; Cognitive process (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-319-67431-5_24

Ordering information: This item can be ordered from
http://www.springer.com/9783319674315

DOI: 10.1007/978-3-319-67431-5_24

Access Statistics for this chapter

More chapters in Lecture Notes in Information Systems and Organization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().