Author

Max Quinn

Date

February 2013

Document Type

Thesis

Degree Name

M.S.

Department

Dept. of Biomedical Engineering

Institution

Oregon Health & Science University

Abstract

Understanding and monitoring cognitive processes is a difficult task; made more so by the apparently complex underlying resource networks that make up cognition, as well as by confounds such as variability in the effort expended by subjects while performing cognitive tasks. In this paper, we describe an electroencephalogram (EEG) and machine learning-based approach to estimating the cognitive effort exerted by a subject while performing a task. We describe the components of an EEG processing pipeline and a machine learning system, the challenges associated with employing these systems, and consider several implementation options. We contribute a novel method for separating ocular artifacts from cortical activity that represents a possible improvement upon existing techniques. In addition, we have investigated a number of alternative approaches to classification and found that these perform in a similar manner as those in prior research. Although these approaches did not produce desir

Identifier

doi:10.6083/M4833Q22

School

School of Medicine

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