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Full-Text Articles in Engineering

Observing P300 Amplitudes In Multiple Sensory Channels Using Cognitive Probing, Cody Lee Wintermute Jan 2020

Observing P300 Amplitudes In Multiple Sensory Channels Using Cognitive Probing, Cody Lee Wintermute

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High cognitive workload occurs when excessive working memory resources have been deployed to resolve sensory and cognitive processing, resulting in decremented task performance. The P300 event-related potential (ERP) component has shown sensitivity to cognitive load, and it was hypothesized that an attenuated P300 amplitude could be indicative of high cognitive load. We tested this hypothesis by having eight participants complete two continual performance tasks at increasing workload levels while simultaneously performing an oddball task, evoking P300 ERPs in either the auditory or tactile sensory channel. In our experiment, electroencephalographic recordings were collected over the parietal region to observe the P300 …


Visual Sampling With The Eeg Alpha Oscillation, Kevin Eugene Alexander Jan 2020

Visual Sampling With The Eeg Alpha Oscillation, Kevin Eugene Alexander

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The posterior alpha rhythm, seen in human electroencephalograms (EEG), is posited to originate from cycling inhibitory/excitatory states of visual relay cells in the thalamus, which could result in discrete sampling of visual information. Here, we tested this hypothesis by presenting light flashes at perceptual threshold intensity through closed eyelids to 20 participants during times of spontaneous alpha oscillations. Alpha phase and amplitude were calculated relative to each individual’s retina-to-V1 conduction delay, estimated by the individuals’ C1 visual-evoked potential latency. Our results show that an additional 20.96% of stimuli are observed when afferenting at V1 during an alpha wave trough (272.41°) …


A Novel Synergistic Model Fusing Electroencephalography And Functional Magnetic Resonance Imaging For Modeling Brain Activities, Konstantinos Michalopoulos Jan 2014

A Novel Synergistic Model Fusing Electroencephalography And Functional Magnetic Resonance Imaging For Modeling Brain Activities, Konstantinos Michalopoulos

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Study of the human brain is an important and very active area of research. Unraveling the way the human brain works would allow us to better understand, predict and prevent brain related diseases that affect a significant part of the population. Studying the brain response to certain input stimuli can help us determine the involved brain areas and understand the mechanisms that characterize behavioral and psychological traits.

In this research work two methods used for the monitoring of brain activities, Electroencephalography (EEG) and functional Magnetic Resonance (fMRI) have been studied for their fusion, in an attempt to bridge together the …


A Validation Of A Prototype Dry Electrode System For Electroencephalography, Jason Monnin Jan 2011

A Validation Of A Prototype Dry Electrode System For Electroencephalography, Jason Monnin

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Current physiologically-driven operator cognitive state assessment technology relies primarily on electroencephalographic (EEG) signals. Traditionally, gel-based electrodes have been used; however, the application of gel-based electrodes on the scalp requires expertise and a considerable amount of preparation time. Additionally, discomfort can occur from the abrasion of the scalp during preparation, and the electrolyte will also begin to dry out over extended periods of time. These drawbacks have hindered the transition of operator state assessment technology into an operational environment. QUASAR, Inc., (San Diego, CA) has developed a prototype dry electrode system for electroencephalography that requires minimal preparation. A comparison of the …


Optimal Eeg Channels And Rhythm Selection For Task Classification, Vikramvarun Kannan Adikarapatti Jan 2007

Optimal Eeg Channels And Rhythm Selection For Task Classification, Vikramvarun Kannan Adikarapatti

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The Primary Objective of this research is to implement an automatic method for selecting the most optimal EEG channels for task classification purposes. The secondary objective of this research is to choose the most optimal EEG rhythm from which the optimal EEG channels would be selected automatically. The automatic selection of the optimal channels is enabled by implementing the Common Spatial Patterns algorithm (CSP). Common spatial analysis is performed on the data recorded. By choosing the channels with high spatial pattern values the optimal channels are chosen. The optimal frequency bands are chosen by splitting the data from a single …