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Simultaneous Use of Physiological Sensors for a Neuromarketing Task
Title
Simultaneous Use of Physiological Sensors for a Neuromarketing Task.
Creator
Descheneaux, Charles, Reinerman, Lauren, Barber, Daniel, Karwowski, Waldemar, Goldiez, Brian, University of Central Florida
Abstract / Description
ABSTRACTPhysiological measurements have become more popular in Psychological research over the past ten years. These advancements allowed different objective sensors to become another measurement tool in a scientific arsenal of collecting data. Traditionally, performance and after task subjective measures have been used for most studies in Psychological research. With the opportunity to use these subjective measures along with objective measures, more data can be collected during research and...
Show moreABSTRACTPhysiological measurements have become more popular in Psychological research over the past ten years. These advancements allowed different objective sensors to become another measurement tool in a scientific arsenal of collecting data. Traditionally, performance and after task subjective measures have been used for most studies in Psychological research. With the opportunity to use these subjective measures along with objective measures, more data can be collected during research and therefore potentially produce better quality conclusions.Eye Tracking (ET), functional near infrared (fNIR), transcranial Doppler ultrasound (TCD), electrocardiogram (EKG) and the electroencephalogram (EEG) have shown great promise in their ability to produce reliable and powerful objective data for research. Consequently, these devices are being used at the same time. The simultaneous use has the potential for interference between devices. Further, these devices are used on human subjects who can find these devices uncomfortable. These issues have the ability to skew data simply due to the measurement devices used.The effort of this study was to determine if the above devices could be used simultaneously without affecting their data quality, determine if difference combinations are more or less beneficial and determine if the combination of sensors have an effect on participant experience. A negative effect from discomfort has the potential to effect data. A study was conducted utilizing the ET, EEG, EKG, fNIR and TCD together in various combinations and also alone to determine if data is compromised and to determine if the combinations have an effect on participant experience.
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Date Issued
2018
Identifier
CFE0007323, ucf:52130
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0007323
Motor imagery classification using sparse representation of EEG signals
Title
Motor imagery classification using sparse representation of EEG signals.
Creator
Saidi, Pouria, Atia, George, Vosoughi, Azadeh, Berman, Steven, University of Central Florida
Abstract / Description
The human brain is unquestionably the most complex organ of the body as it controls and processes its movement and senses. A healthy brain is able to generate responses to the signals it receives, and transmit messages to the body. Some neural disorders can impair the communication between the brain and the body preventing the transmission of these messages. Brain Computer Interfaces (BCIs) are devices that hold immense potential to assist patients with such disorders by analyzing brain...
Show moreThe human brain is unquestionably the most complex organ of the body as it controls and processes its movement and senses. A healthy brain is able to generate responses to the signals it receives, and transmit messages to the body. Some neural disorders can impair the communication between the brain and the body preventing the transmission of these messages. Brain Computer Interfaces (BCIs) are devices that hold immense potential to assist patients with such disorders by analyzing brain signals, translating and classifying various brain responses, and relaying them to external devices and potentially back to the body. Classifying motor imagery brain signals where the signals are obtained based on imagined movement of the limbs is a major, yet very challenging, step in developing Brain Computer Interfaces (BCIs). Of primary importance is to use less data and computationally efficient algorithms to support real-time BCI. To this end, in this thesis we explore and develop algorithms that exploit the sparse characteristics of EEGs to classify these signals. Different feature vectors are extracted from EEG trials recorded by electrodes placed on the scalp.In this thesis, features from a small spatial region are approximated by a sparse linear combination of few atoms from a multi-class dictionary constructed from the features of the EEG training signals for each class. This is used to classify the signals based on the pattern of their sparse representation using a minimum-residual decision rule.We first attempt to use all the available electrodes to verify the effectiveness of the proposed methods. To support real time BCI, the electrodes are reduced to those near the sensorimotor cortex which are believed to be crucial for motor preparation and imagination.In a second approach, we try to incorporate the effect of spatial correlation across the neighboring electrodes near the sensorimotor cortex. To this end, instead of considering one feature vector at a time, we use a collection of feature vectors simultaneously to find the joint sparse representation of these vectors. Although we were not able to see much improvement with respect to the first approach, we envision that such improvements could be achieved using more refined models that can be subject of future works. The performance of the proposed approaches is evaluated using different features, including wavelet coefficients, energy of the signals in different frequency sub-bands, and also entropy of the signals. The results obtained from real data demonstrate that the combination of energy and entropy features enable efficient classification of motor imagery EEG trials related to hand and foot movements. This underscores the relevance of the energies and their distribution in different frequency sub-bands for classifying movement-specific EEG patterns in agreement with the existence of different levels within the alpha band. The proposed approach is also shown to outperform the state-of-the-art algorithm that uses feature vectors obtained from energies of multiple spatial projections.
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Date Issued
2015
Identifier
CFE0005882, ucf:50884
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005882
A study of EEG signature associated with Emotional and stress responses due to cyberbullying
Title
A study of EEG signature associated with Emotional and stress responses due to cyberbullying.
Creator
Alhujailli, Ashraf, Karwowski, Waldemar, Reinerman, Lauren, Hancock, Peter, Wan, Thomas, University of Central Florida
Abstract / Description
The human brain processes vital information regarding human feelings. Prior research has focused on the problems of underage bullying, workplace bullying, burnout, mobbing and, most recently, cyberbullying. Scholars have traditionally examined the adverse outcomes of cyberbullying using subjective measures of stress and emotion for decades. However, very few studies examined cyberbullying using objective measures like EEG. The main goal of this study was to explore the relationship between...
Show moreThe human brain processes vital information regarding human feelings. Prior research has focused on the problems of underage bullying, workplace bullying, burnout, mobbing and, most recently, cyberbullying. Scholars have traditionally examined the adverse outcomes of cyberbullying using subjective measures of stress and emotion for decades. However, very few studies examined cyberbullying using objective measures like EEG. The main goal of this study was to explore the relationship between the brain's EEG, expressed by the power spectral density, and emotions and stress due to two types of cyberbullying, specifically: 1) social exclusion, and 2) verbal harassment. This research also examined how cyberbullying factors of social interaction and publicity affect the emotional and stress responses. EEG data were collected from twenty-nine undergraduate students, aged 18-22, using 10/5 EEG system with 64 channels. Each cyberbullying experimental condition was treated as an independent study. The first study investigated the effects of social exclusion on EEG activity and the related emotional and stress factors while playing a virtual ball-tossing game known as cyberball. EEG results showed significant differences in alpha and beta power in the right posterior brain regions due to social exclusion. There were also significant differences in beta and gamma power in the left anterior brain regions due to social exclusion. The results suggest that EEG activity in the left anterior brain region may be important to identify social exclusion. The second study utilized a hypothetical scenario presented as impolite or complimentary online comments. EEG results showed marginally significant differences in gamma power at right- and left- anterior and midline brain regions due to verbal harassment. The results suggest that changes in gamma power at anterior brain regions might play an essential role in the processing of verbal harassment information. Self-reported measures confirmed that verbal harassment was more distressing than social exclusion.
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Date Issued
2018
Identifier
CFE0006968, ucf:51628
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006968
Neurophenomenological Methods: Experiences of Earth and Space in Simulation
Title
Neurophenomenological Methods: Experiences of Earth and Space in Simulation.
Creator
Morrow, Patricia, Reinerman, Lauren, Cash, Mason, Janz, Bruce, Gallagher, Shaun, University of Central Florida
Abstract / Description
The present study explores the nature and structure of spiritual and aesthetic experiences through the interdisciplinary application of neurophenomenology (NP). This approach merges aspects of psychology, neurophysiology, and phenomenology into a unified methodology. The study is nested within a larger project, Space, Science, and Spirituality, and as such, it carries a common goal to use simulation to evoke spiritual and aesthetic responses similar to those expressed by astronauts and...
Show moreThe present study explores the nature and structure of spiritual and aesthetic experiences through the interdisciplinary application of neurophenomenology (NP). This approach merges aspects of psychology, neurophysiology, and phenomenology into a unified methodology. The study is nested within a larger project, Space, Science, and Spirituality, and as such, it carries a common goal to use simulation to evoke spiritual and aesthetic responses similar to those expressed by astronauts and cosmonauts. Careful analysis of previous work in NP provided methodological (")lessons learned("), which guided the experimental design, execution, and analysis of the present study. The data collected provides support for experience as a phenomenon that can be studied through empirical means. Further, the articulation of spiritual and aesthetic experiences akin to astronaut experiences corresponds to specific neurological and psychological indicators. Among those indicators are differences in EEG measures during simulation time relative to expressions of spiritual experience following the simulation and changes in visual processing across theta, alpha, and beta signals as correlated with self-identification. These findings support an embodied theory of experience that incorporates memory, executive function, perception, and consciousness. In addition to its academic contribution, this research holds implications for commercial space flight, long-term space missions, post-traumatic stress disorder therapies, and the entertainment industry.
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Date Issued
2013
Identifier
CFE0005035, ucf:50018
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005035
BIOSIGNAL PROCESSING CHALLENGES IN EMOTION RECOGNITIONFOR ADAPTIVE LEARNING
Title
BIOSIGNAL PROCESSING CHALLENGES IN EMOTION RECOGNITIONFOR ADAPTIVE LEARNING.
Creator
Vartak, Aniket, Mikhael, Wasfy, University of Central Florida
Abstract / Description
User-centered computer based learning is an emerging field of interdisciplinary research. Research in diverse areas such as psychology, computer science, neuroscience and signal processing is making contributions the promise to take this field to the next level. Learning systems built using contributions from these fields could be used in actual training and education instead of just laboratory proof-of-concept. One of the important advances in this research is the detection and assessment of...
Show moreUser-centered computer based learning is an emerging field of interdisciplinary research. Research in diverse areas such as psychology, computer science, neuroscience and signal processing is making contributions the promise to take this field to the next level. Learning systems built using contributions from these fields could be used in actual training and education instead of just laboratory proof-of-concept. One of the important advances in this research is the detection and assessment of the cognitive and emotional state of the learner using such systems. This capability moves development beyond the use of traditional user performance metrics to include system intelligence measures that are based on current neuroscience theories. These advances are of paramount importance in the success and wide spread use of learning systems that are automated and intelligent. Emotion is considered an important aspect of how learning occurs, and yet estimating it and making adaptive adjustments are not part of most learning systems. In this research we focus on one specific aspect of constructing an adaptive and intelligent learning system, that is, estimation of the emotion of the learner as he/she is using the automated training system. The challenge starts with the definition of the emotion and the utility of it in human life. The next challenge is to measure the co-varying factors of the emotions in a non-invasive way, and find consistent features from these measures that are valid across wide population. In this research we use four physiological sensors that are non-invasive, and establish a methodology of utilizing the data from these sensors using different signal processing tools. A validated set of visual stimuli used worldwide in the research of emotion and attention, called International Affective Picture System (IAPS), is used. A dataset is collected from the sensors in an experiment designed to elicit emotions from these validated visual stimuli. We describe a novel wavelet method to calculate hemispheric asymmetry metric using electroencephalography data. This method is tested against typically used power spectral density method. We show overall improvement in accuracy in classifying specific emotions using the novel method. We also show distinctions between different discrete emotions from the autonomic nervous system activity using electrocardiography, electrodermal activity and pupil diameter changes. Findings from different features from these sensors are used to give guidelines to use each of the individual sensors in the adaptive learning environment.
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Date Issued
2010
Identifier
CFE0003301, ucf:48503
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0003301
NEUROERGONOMICS STUDY: ANALYSIS OF BRAIN EEG's ACTIVITY DURING MANUAL LIFTING TASKS
Title
NEUROERGONOMICS STUDY: ANALYSIS OF BRAIN EEG's ACTIVITY DURING MANUAL LIFTING TASKS.
Creator
Aljuaid, Awad, Xanthopoulos, Petros, Karwowski, Waldemar, Hancock, Peter, McCauley, Pamela, Lee, Gene, Kincaid, John, University of Central Florida
Abstract / Description
Electroencephalography (EEG) has been shown to be a reliable tool in neuroergonomics studies due to the relatively low cost of brain data collection and limited body invasion. The application of EEG frequency bands (including theta, alpha and beta), enjoyed a wide range of interest in physical and cognitive ergonomics. The psychophysical approach has been used for decades to improve safe work practices by understanding human limitations in manual materials handling. The main objective of this...
Show moreElectroencephalography (EEG) has been shown to be a reliable tool in neuroergonomics studies due to the relatively low cost of brain data collection and limited body invasion. The application of EEG frequency bands (including theta, alpha and beta), enjoyed a wide range of interest in physical and cognitive ergonomics. The psychophysical approach has been used for decades to improve safe work practices by understanding human limitations in manual materials handling. The main objective of this research project was to study the brain's EEG activity expressed by the power spectral density during manual lifting tasks related to: 1) the maximum acceptable weight of lift (MAWL) and 2) isokinetic and isometric lifting strength tests measurement outcomes. The first study investigated the changes in EEG power spectral density during determination of MAWL under low, medium, and high lifting frequencies. A high-density wireless dry cell EEG device has been used to record EEG signals. Twenty healthy males participated in this study. Subjects repeated the same experiment after two weeks. Analysis of variance (ANOVA) showed significant differences in EEG power spectral density between different lifting frequencies at three main brain areas (frontal, central, and parietal). The second study revealed differences in brain activities during isokinetic and isometric strength measurements, based on the recording and analysis of EEG power spectral density. This research project is the first study of EEG activity during manual lifting tasks, including the assessment of MAWL by the psychophysical method, as well as the measurement of human isokinetic and isometric strengths. The results of this project are considered critical to our increased understanding of the neural correlates of human physical activities, and consequently should have a positive impact on workplace design that considers brain activity related to specific human capabilities and limitations in manual lifting tasks.
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Date Issued
2016
Identifier
CFE0006067, ucf:50996
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006067
Applied Error Related Negativity: Single Electrode Electroencephalography in Complex Visual Stimuli
Title
Applied Error Related Negativity: Single Electrode Electroencephalography in Complex Visual Stimuli.
Creator
Sawyer, Benjamin, Karwowski, Waldemar, Hancock, Peter, Xanthopoulos, Petros, University of Central Florida
Abstract / Description
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...
Show moreError 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.
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Date Issued
2014
Identifier
CFE0005885, ucf:50886
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005885
An investigation of physiological measures in a marketing decision task
Title
An investigation of physiological measures in a marketing decision task.
Creator
Lerma, Nelson, Karwowski, Waldemar, Elshennawy, Ahmad, Xanthopoulos, Petros, Reinerman, Lauren, University of Central Florida
Abstract / Description
The objective of the present study was to understand the use of physiological measures as an alternative to traditional market research tools, such as self-reporting measures and focus groups. For centuries, corporations and researchers have relied almost exclusively on traditional measures to gain insights into consumer behavior. Oftentimes, traditional methods have failed to accurately predict consumer demand, and this has prompted corporations to explore alternative methods that will...
Show moreThe objective of the present study was to understand the use of physiological measures as an alternative to traditional market research tools, such as self-reporting measures and focus groups. For centuries, corporations and researchers have relied almost exclusively on traditional measures to gain insights into consumer behavior. Oftentimes, traditional methods have failed to accurately predict consumer demand, and this has prompted corporations to explore alternative methods that will accurately forecast future sales. One the most promising alternative methods currently being investigated is the use of physiological measures as an indication of consumer preference. This field, also referred to as neuromarketing, has blended the principles of psychology, neuroscience, and market research to explore consumer behavior from a physiological perspective. The goal of neuromarketing is to capture consumer behavior through the use of physiological sensors. This study investigated the extent to which physiological measures where correlated to consumer preferences by utilizing five physiological sensors which included two neurological sensors (EEG and ECG) two hemodynamic sensors (TCD and fNIR) and one optic sensor (eye-tracking). All five physiological sensors were used simultaneously to capture and record physiological changes during four distinct marketing tasks. The results showed that only one physiological sensor, EEG, was indicative of concept type and intent to purchase. The remaining four physiological sensors did not show any significant differences for concept type or intent to purchase.Furthermore, Machine Learning Algorithms (MLAs) were used to determine the extent to which MLAs (Na(&)#239;ve Bayes, Multilayer Perceptron, K-Nearest Neighbor, and Logistic Regression) could classify physiological responses to self-reporting measures obtained during a marketing task. The results demonstrated that Multilayer Perceptron, on average, performed better than the other MLAs for intent to purchase and concept type. It was also evident that the models faired best with the most popular concept when categorizing the data based on intent to purchase or final selection. Overall, the four models performed well at categorizing the most popular concept and gave some indication to the extent to which physiological measures are capable of capturing intent to purchase. The research study was intended to help better understand the possibilities and limitations of physiological measures in the field of market research. Based on the results obtained, this study demonstrated that certain physiological sensors are capable of capturing emotional changes, but only when the emotional response between two concepts is significantly different. Overall, physiological measures hold great promise in the study of consumer behavior, providing great insight on the relationship between emotions and intentions in market research.
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Date Issued
2015
Identifier
CFE0006345, ucf:51563
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006345
Investigating the universality and comprehensive ability of measures to assess the state of workload
Title
Investigating the universality and comprehensive ability of measures to assess the state of workload.
Creator
Abich, Julian, Reinerman, Lauren, Lackey, Stephanie, Szalma, James, Taylor, Grant, University of Central Florida
Abstract / Description
Measures of workload have been developed on the basis of the various definitions, some are designed to capture the multi-dimensional aspects of a unitary resource pool (Kahneman, 1973) while others are developed on the basis of multiple resource theory (Wickens, 2002). Although many theory based workload measures exist, others have often been constructed to serve the purpose of specific experimental tasks. As a result, it is likely that not every workload measure is reliable and valid for all...
Show moreMeasures of workload have been developed on the basis of the various definitions, some are designed to capture the multi-dimensional aspects of a unitary resource pool (Kahneman, 1973) while others are developed on the basis of multiple resource theory (Wickens, 2002). Although many theory based workload measures exist, others have often been constructed to serve the purpose of specific experimental tasks. As a result, it is likely that not every workload measure is reliable and valid for all tasks, much less each domain. To date, no single measure, systematically tested across experimental tasks, domains, and other measures is considered a universal measure of workload. Most researchers would argue that multiple measures from various categories should be applied to a given task to comprehensively assess workload. The goal for Study 1 to establish task load manipulations for two theoretically different tasks that induce distinct levels of workload assessed by both subjective and performance measures was successful. The results of the subjective responses support standardization and validation of the tasks and demands of that task for investigating workload. After investigating the use of subjective and objective measures of workload to identify a universal and comprehensive measure or set of measures, based on Study 2, it can only be concluded that not one or a set of measures exists. Arguably, it is not to say that one will never be conceived and developed, but at this time, one does not reside in the psychometric catalog. Instead, it appears that a more suitable approach is to customize a set of workload measures based on the task. The novel approach of assessing the sensitivity and comprehensive ability of conjointly utilizing subjective, performance, and physiological workload measures for theoretically different tasks within the same domain contributes to the theory by laying the foundation for improving methodology for researching workload. The applicable contribution of this project is a stepping-stone towards developing complex profiles of workload for use in closed-loop systems, such as human-robot team interaction. Identifying the best combination of workload measures enables human factors practitioners, trainers, and task designers to improve methodology and evaluation of system designs, training requirements, and personnel selection.
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Date Issued
2013
Identifier
CFE0005119, ucf:50675
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005119