Keywords

Eeg; errors; ern; ne; automation; visual search; motorcycle conspicuity; roadway safety; surface transportation; human factors; human error; human machine systems

Abstract

Error related negativity (ERN) is a pronounced negative evoked response potential (ERP) that follows a known error. This neural pattern has the potential to communicate user awareness of incorrect actions within milliseconds. While the implications for human-machine interface and augmented cognition are exciting, the ERN has historically been evoked only in the laboratory using complex equipment while presenting simple visual stimuli such as letters and symbols. To effectively harness the applied potential of the ERN, detection must be accomplished in complex environments using simple, preferably single-electrode, EEG systems feasible for integration into field and workplace-ready equipment. The present project attempted to use static photographs to evoke and successfully detect the ERN in a complex visual search task: motorcycle conspicuity. Drivers regularly fail to see motorcycles, with tragic results. To reproduce the issue in the lab, static pictures of traffic were presented, either including or not including motorcycles. A standard flanker letter task replicated from a classic ERN study (Gehring et al., 1993) was run alongside, with both studies requiring a binary response. Results showed that the ERN could be clearly detected in both tasks, even when limiting data to a single electrode in the absence of artifact correction. These results support the feasibility of applied ERN detection in complex visual search in static images. Implications and opportunities will be discussed, limitations of the study explained, and future directions explored.

Notes

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Graduation Date

2015

Semester

Summer

Advisor

Karwowski, Waldemar

Degree

Master of Science (M.S.)

College

College of Engineering and Computer Science

Department

Industrial Engineering and Management Systems

Degree Program

Engineering and Computer Science

Format

application/pdf

Identifier

CFE0005885

URL

http://purl.fcla.edu/fcla/etd/CFE0005885

Language

English

Release Date

August 2015

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

Subjects

Dissertations, Academic -- Engineering and Computer Science; Engineering and Computer Science -- Dissertations, Academic

Restricted to the UCF community until August 2015; it will then be open access.

Included in

Engineering Commons

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