Biomedical Engineering and Bioengineering Commons^™

Development Of Multimodal Measurements And Analysis For Early Detection Of Alzheimer’S Disease, Fiza Saeed

Bioengineering Dissertations

Alzheimer's disease (AD) is the leading cause of dementia, and existing diagnostic methods such as PET scans, cerebrospinal fluid sampling and biomarker quantification, and gene sequencing are all either invasive, costly, or not sensitive enough for early detection. This dissertation introduces three different studies that develop a novel multimodal, non-invasive approach to diagnosing AD at its early stages by combining broad band near infrared spectroscopy (bbNIRS) and electroencephalography (EEG) technologies.

The first study showed cerebrovascular-cerebrospinal fluid coupling (CBV-CSF), which is measured by using 2-channel bbNIRS as an indicator of brain aging and early AD. Linear correlations between total blood (Δ[HbT]) …

A Hybrid Ai-Based Framework For Real-Time Epileptic Seizure Detection In Intracranial Eeg Signals Using Brain-Computer Interfaces, Ahmed, M. Salaheldin Dr., Manal Abdel Wahed Prof., Neven Saleh

Future Engineering Journal

Epilepsy is a serious neurological disorder that can significantly impact an individual's quality of life. This study proposes a novel method for the detection and mitigation of epileptic seizures through the integration of artificial intelligence (AI) and a brain-computer interface (BCI) system. Statistical features were extracted from intracranial electroencephalography (IEEG) signals using a multi-resolution decomposition technique and used to train five classification algorithms: support vector machines (SVM), k-nearest neighbors (KNN), Naïve Bayes (NB), artificial neural networks (ANN), and long short-term memory (LSTM). The model demonstrated strong performance, achieving classification accuracies of 98.28% (SVM), 93.88% (KNN), 95.73% (NB), 95.73% (ANN), and …

Using Eeg Signal For Smart Health: An Overview, Addison Clark, Chelsea Bonyata, Ishfaq Ahmad, Hansheng Lei, Khalid Saifullah, Victor Ibarra

Informatics and Engineering Systems Faculty Publications

This survey paper aims to provide an overview of the current state of EEG (Electroencephalography) signal technology as it relates to people with disabilities. It will highlight the various methods and techniques employed, discussing their advantages and disadvantages. The paper will also examine the applications of EEG technology in assisting individuals with disabilities, specifically focusing on Brain-Computer Interfaces (BCIs) and assistive device control. By understanding the current state of EEG signal technology, we can identify the opportunities and challenges involved in utilizing this technology to improve the lives of people with disabilities.

Altered Cortical Activity During A Finger Tap In People With Stroke, Priya Balasubramanian, Roxanne P. Deleon, Dylan B. Snyder, Scott A. Beardsley, Allison S. Hyngstrom, Brian D. Schmit

Biomedical Engineering Faculty Research and Publications

This study describes electroencephalography (EEG) measurements during a simple finger movement in people with stroke to understand how temporal patterns of cortical activation and network connectivity align with prolonged muscle contraction at the end of a task. We investigated changes in the EEG temporal patterns in the beta band (13–26 Hz) of people with chronic stroke (N = 10, 7 F/3 M) and controls (N = 10, 7 F/3 M), during and after a cued movement of the index finger. We quantified the change in beta band EEG power relative to baseline as activation at each electrode and …

2-Channel Eeg Neurofeedback System, Tim Erwin, Donna Nikjou, Sebastian Turkewitz

Electrical Engineering

This work describes the design of an EEG-based neurofeedback system which provides users with real-time feedback on their level of focus or relaxation. By analyzing the spectral content of the brain activity measured via scalp electrodes, focus and relaxation levels can be quantified. Based on these measurements, live feedback in the form of moving bar graphs is provided to users, allowing them to gain awareness of their mental-state and more efficiently learn how to consciously relax or focus. This project covers the design of the system, including amplification and filtering stages, digitization, and signal processing. The system interfaces with a …

Noninvasive Mapping Of Global And Regional Functional Connectivity In The Brain Of Children With Drug Resistant Epilepsy And Healthy Controls, Ludovica Corona

Bioengineering Dissertations - Archive

For patients with drug resistant epilepsy (DRE), resective surgery is the most effective treatment to achieve seizure-freedom. Its success largely depends on the resection, ablation, or disconnection of the epileptogenic zone (EZ), defined as the minimal area indispensable for the generation of seizures, with minimal or no functional deficits. To date, there is no clinical exam to delineate the EZ unambiguously. Instead, the EZ is indirectly defined through multiple noninvasive diagnostic tests used by the multidisciplinary epilepsy team, whose results are often insufficiently concordant or inconclusive. Thus, extra-operative intracranial electroencephalography (iEEG) monitoring may be needed to precisely define the EZ …

Visual Complexity Of The Time-Frequency Image Pinpoints The Epileptogenic Zone: An Unsupervised Deep-Learning Tool To Analyze Interictal Intracranial Eeg, Sarvagya Gupta

Graduate Masters Theses

Epilepsy, a prevalent neurological disorder characterized by recurrent seizures, continues to pose significant challenges in diagnosis and treatment, particularly among children. Despite substantial advancements in medical technology and treatment modalities, localization of the part of brain that causes seizures (Epileptogenic Zone) remains a difficult task. Intracranial EEG (iEEG) is often used to estimate the epileptogenic zone (EZ) in children with drugresistant epilepsy (DRE) and target it during surgery. Conventionally, iEEG signals are inspected in the time domain by human experts aiming to locate epileptiform activity.

Visual scrutiny of the iEEG time-frequency (TF) images can be an alternative way to review …

Analytical Pipeline To Facilitate Electroencephalography Pain State Identification In Patients With Chronic Low Back Pain, Emma Katharina Jacobs

Master's Theses

Chronic low back pain (cLBP) affects the quality of life of over 23% of American adults and places an immense burden on the economy.^1–3 To more effectively diagnose and treat cLBP, quantitative biomarkers are needed to objectively measure pain and better identify the underlying mechanisms of cLBP. The overarching goal of this work is to create an analytical pipeline to investigate brain activity patterns measured with electroencephalogram (EEG) data of cLBP patients treated with transcutaneous spinal cord stimulation (tSCS). The specific aims of this project are to 1) investigate whether there is a difference between baseline EEG measurements and …

Electrophysiological, Hemodynamic, And Metabolic Effects Of Transcranial Photobiomodulation (Tpbm) On Topographical And Physiological Connectivity In The Human Brain, Sadra Shahdadian

Bioengineering Dissertations - Archive

Transcranial photobiomodulation (tPBM) targets the human brain with near-infrared (NIR) light and is shown to affect human cognitive performance and neural electrophysiological activity as well as concentration changes of oxidized cytochrome-c-oxidase ([CCO]) and hemoglobin oxygenation ([HbO]) in human brain. Brain topographical connectivity, which shows the communication between regions of the brain, and its alteration can be assessed to quantify the effects of external stimuli, diseases, and cognitive decline, in resting-state or task-based measurements. Furthermore, understanding the interactions between different physiological representations of neural activity, namely electrophysiological, hemodynamic, and metabolic signals in the human brain, has been an important topic among …

Mapping Of Electroencephalogram (Eeg) Responses To Newsvendor Decision Model And Cerebral Hemodynamic Responses To Transcranial Photobiomodulation (Tpbm) In Humans, Cong Dung Nghi Truong

Bioengineering Dissertations - Archive

Decision-making is one of the most critical activities of human beings. Various studies have investigated the brain responses to a variety of decision tasks by deploying the EEG data. The first aim of my dissertation was to investigate the neurocognitive mechanisms and correlations across multiple subjects when people make risky economic decisions. A decision-making task was designed based on the newsvendor problem (NP) with two scenarios: low-profit margins (LM) as the more challenging scenario and high-profit margins (HM) as the less difficult one. The EEG signals were acquired from healthy humans while subjects were performing the task. The Correlated Component …

Eeg And Fmri Coupling And Decoupling Based On Joint Independent Component Analysis (Jica), Nicholas Heugel, Scott A. Beardsley, Einat Liebenthal

Biomedical Engineering Faculty Research and Publications

Background

Meaningful integration of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) requires knowing whether these measurements reflect the activity of the same neural sources, i.e., estimating the degree of coupling and decoupling between the neuroimaging modalities.

New method

This paper proposes a method to quantify the coupling and decoupling of fMRI and EEG signals based on the mixing matrix produced by joint independent component analysis (jICA). The method is termed fMRI/EEG-jICA.

Results

fMRI and EEG acquired during a syllable detection task with variable syllable presentation rates (0.25–3 Hz) were separated with jICA into two spatiotemporally distinct components, a primary …

Voluntary Control Of Breathing According To The Breathing Pattern During Listening To Music And Non-Contact Measurement Of Heart Rate And Respiration, Dibyajyoti Biswal

Theses and Dissertations--Biomedical Engineering

We investigated if listening to songs changes breathing pattern which changes autonomic responses such as heart rate (HR) and heart rate variability (HRV) or change in breathing pattern is a byproduct of listening to songs or change in breathing pattern as well as listening to songs causes changes in autonomic responses. Seven subjects (4 males and 3 females) participated in a pilot study where they listened to two types of songs and used a custom developed biofeedback program to control their breathing pattern to match the one recorded during listening to the songs.

Coherencies between EEG, breathing pattern and RR …

Inter-Subject Correlation While Listening To Minimalist Music: A Study Of Electrophysiological And Behavioral Responses To Steve Reich’S Piano Phase, Tysen Dauer, Duc T. Nguyen, Nick Gang, Jacek P. Dmochowski, Jonathan Berger, Blair Kaneshiro

Publications and Research

Musical minimalism utilizes the temporal manipulation of restricted collections of rhythmic, melodic, and/or harmonic materials. One example, Steve Reich’s Piano Phase, offers listeners readily audible formal structure with unpredictable events at the local level. For example, pattern recurrences may generate strong expectations which are violated by small temporal and pitch deviations. A hyper-detailed listening strategy prompted by these minute deviations stands in contrast to the type of listening engagement typically cultivated around functional tonal Western music. Recent research has suggested that the inter-subject correlation (ISC) of electroencephalographic (EEG) responses to natural audio-visual stimuli objectively indexes a state of “engagement,” demonstrating …

Machine-Learning-Based Approach To Decoding Physiological And Neural Signals, Elnaz Lashgari

Computational and Data Sciences (PhD) Dissertations

In recent years, machine learning algorithms have been developing rapidly, becoming increasingly powerful tools in decoding physiological and neural signals. The aim of this dissertation is to develop computational tools, and especially machine learning techniques, to identify the most effective methods for feature extraction and classification of these signals. This is particularly challenging due to the highly non-linear, non-stationery, and artifact- and noise-prone nature of these signals.

Among basic human-control tasks, reaching and grasping are ubiquitous in everyday life. I investigated different linear and non-linear dimensionality reduction techniques for feature extraction and classification of electromyography (EMG) during a reach-grasp-lift task. …

Electroencephalography Resting-State Networks In People With Stroke, Dylan B. Snyder, Brian D. Schmit, Allison S. Hyngstrom, Scott A. Beardsley

Biomedical Engineering Faculty Research and Publications

Introduction

The purpose of this study was to characterize resting-state cortical networks in chronic stroke survivors using electroencephalography (EEG).

Methods

Electroencephalography data were collected from 14 chronic stroke and 11 neurologically intact participants while they were in a relaxed, resting state. EEG power was normalized to reduce bias and used as an indicator of network activity. Correlations of orthogonalized EEG activity were used as a measure of functional connectivity between cortical regions.

Results

We found reduced cortical activity and connectivity in the alpha (p < .05; p = .05) and beta (p < .05; p = .03) bands after stroke while connectivity …

Analysis Of Graded Sensorimotor Rhythms For Brain-Computer Interface Applications, Chase Allen Haddix

Theses and Dissertations--Biomedical Engineering

The emerging field of neural engineering is tasked with applying engineering principles towards understanding neuroscience. A by-product of such a venture has been the development of a class of assistive devices known as brain-computer interfaces (BCIs) which link brain activity to actions performed by external devices. One application of this technology is in the rehabilitative sector for individuals with neuromuscular diseases and disorders. Despite tremendous efforts in the last few decades, a reliable signal that reflects fine motor control has yet to be adequately investigated. This gap in knowledge has limited the potential of BCIs to restore movement and communication. …

Characterization Of Modulation And Coherence In Sensorimotor Rhythms Using Different Electroencephalographic Signal Derivations, Stephen Dundon

Theses and Dissertations--Biomedical Engineering

Electroencephalography (EEG) is a widely used technique for monitoring and analyzing brain activity in experimental, diagnostic, and therapeutic applications. Since EEG is sensitive to noise and artefact sources, referential signals at different locations can be combined in different ways to improve signal quality and better localize cortical activity. Four signal derivations were compared against referential EEG in terms of their ability to measure the alpha rhythm modulation (or reactivity) and spatial coherence associated with an eye closure task: a common average reference (CAR), a local average reference (LAR), a large Laplacian (LL), and a focal Laplacian (FL) estimated using a …

Estimating Affective States In Virtual Reality Environments Using The Electroencephalogram, Meghan R. Kumar

Theses and Dissertations

Recent interest in high-performance virtual reality (VR) headsets has motivated research efforts to increase the user's sense of immersion via feedback of physiological measures. This work presents the use of electroencephalographic (EEG) measurements during observation of immersive VR videos to estimate the user's affective state. The EEG of 30 participants were recorded as each passively viewed a series of one minute immersive VR video clips and subjectively rated their level of valence, arousal, dominance, and liking. Correlates between EEG spectral bands and the subjective ratings were analyzed to identify statistically significant frequencies and electrode locations across participants. Model feasibility and …

Machine Learning Approach For Vigilance State Classification In Mice, Anik Muhury

Theses and Dissertations--Electrical and Computer Engineering

Sleep has a significant impact on cognitive abilities such as memory, reaction time, productivity, and creative thinking; however, there are many aspects of this important activity that are not clearly understood. Over the last century, researchers have developed technology and animal models to assist in the study of sleep. Manual sleep scoring is time consuming, reduces productivity, and is impacted by human scorer subjectivity. On the other hand, automatic sleep stage categorization can enhance consistency and reliability, aiding professionals in identifying sleep related health problems.

In recent times various studies reported significant achievements for automatic vigilance detection and overcome the …

When The Brain Plays A Game: Neural Responses To Visual Dynamics During Naturalistic Visual Tasks, Jason Ki

Dissertations and Theses

Many day-to-day tasks involve processing of complex visual information in a continuous stream. While much of our knowledge on visual processing has been established from reductionist approaches in lab-controlled settings, very little is known about the processing of complex dynamic stimuli experienced in everyday scenarios. Traditional investigations employ event-related paradigms that involve presentation of simple stimuli at select locations in visual space and discrete moments in time. In contrast, visual stimuli in real-life are highly dynamic, spatially-heterogeneous, and semantically rich. Moreover, traditional experiments impose unnatural task constraints (e.g., inhibited saccades), thus, it is unclear whether theories developed under the reductionist …

Whole-Head Functional Brain Imaging Of Economic Risk Decision Making And Transcranial Photobiomodulation With Fnirs And Eeg, Hashini I. Wanniarachchi Kankanamalage

Bioengineering Dissertations - Archive

Functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) are two non-invasive brain imaging or mapping modalities that allow us to measure cerebral hemodynamic and electrophysiological changes in the human brain, respectively. LABNIRS is a cutting-edge, whole-head, optical brain mapping system with 40 pairs of light sources (at 780 nm830 nm) and 40 detectors, forming up to 133 detection channels. In my study, whole-head LABNIRS and 64-channel EEG were utilized to investigate respective alterations in response to risk decision- making under business context and to non-invasive photobiomodulation. As the first part of my study, I utilized fNIRS (i.e., LABNIRS) as a tool …

Visually Evoked Responses Are Enhanced When Engaging In A Video Game, Jason J. Ki, Lucas C. Parra, Jacek P. Dmochowski

Publications and Research

While it is well known that vision guides movement, less appreciated is that the motor cortex also provides input to the visual system. Here, we asked whether neural processing of visual stimuli is acutely modulated during motor activity, hypothesizing that visual evoked responses are enhanced when engaged in a motor task that depends on the visual stimulus. To test this, we told participants that their brain activity was controlling a video game that was in fact the playback of a prerecorded game. The deception, which was effective in half of participants, aimed to engage the motor system while avoiding evoked …

Eeg Characterization Of Sensorimotor Networks: Implications In Stroke, Dylan Blake Snyder

Dissertations (1934 -)

The purpose of this dissertation was to use electroencephalography (EEG) to characterize sensorimotor networks and examine the effects of stroke on sensorimotor networks. Sensorimotor networks play an essential role in completion of everyday tasks, and when damaged, as in stroke survivors, the successful completion of seemingly simple motor tasks becomes fantasy. When sensorimotor networks are impaired as a result of stroke, varying degrees of sensorimotor deficits emerge, most often including loss of sensation and difficulty generating upper extremity movements. Although sensory therapies, such as the application of tendon vibration, have been shown to reduce the sensorimotor deficits after stroke, the …

Characterization Of Neuroimage Coupling Between Eeg And Fmri Using Within-Subject Joint Independent Component Analysis, Nicholas Heugel

Dissertations (1934 -)

The purpose of this dissertation was to apply joint independent component analysis (jICA) to electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to characterize the neuroimage coupling between the two modalities. EEG and fMRI are complimentary imaging techniques which have been used in conjunction to investigate neural activity. Understanding how these two imaging modalities relate to each other not only enables better multimodal analysis, but also has clinical implications as well. In particular, Alzheimer’s, Parkinson’s, hypertension, and ischemic stroke are all known to impact the cerebral blood flow, and by extension alter the relationship between EEG and fMRI. By characterizing …

A Robust Lpc Filtering Method For Time-Resolved Morphology Of Eeg Activity Analysis, Jin Xu, Mark Davis, Ruairí De Fréin

Conference Papers

This paper introduces a new time-resolved spectral analysis method based on Linear Prediction Coding (LPC) method that is particularly suited to the study of the dynamics of EEG (Electroencephalogram) activity. The spectral dynamic of EEG signals can be challenging to analyse as they contain multiple frequency components and are often heavily corrupted by noise. Furthermore, the temporal and spectral resolution that can be achieved is limited by the Heisenberg-Gabor uncertainty principle [1]. The method described here is based on a z-plane analysis of the poles of the LPC which allows us to identify and estimate the frequency of the dominant …

Increasing Performance Of Classifiers For Ssvep-Based Brain-Computer Interfaces Using Extension Methods, Ethan Douglas Webster

Legacy Theses & Dissertations (2009 - 2024)

Brain-computer interfaces (BCI) provide an alternative communication method that does not require standard physical mediums (speech, typing, etc.). These systems have been implemented to provide additional communication and control options for people with certain motor disabilities. Classification is an important part of BCI systems and consists of inferring user commands from brain activity. Supervised classification methods often achieve higher accuracy, but unsupervised classification methods are useful when training is not practical for the user. This thesis focuses on unsupervised classification algorithms used for a BCI speller application and presents extensions for two existing classifiers that improve classification accuracy and thus …

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

Browse all Theses and Dissertations

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

Browse all Theses and Dissertations

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°) …

Method For Spatial Overlap Estimation Of Electroencephalography And Functional Magnetic Resonance Imaging Responses, N. Heugel, E. Liebenthal, Scott A. Beardsley

Biomedical Engineering Faculty Research and Publications

Background

Simultaneous functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) measurements may represent activity from partially divergent neural sources, but this factor is seldom modeled in fMRI-EEG data integration.

New method

This paper proposes an approach to estimate the spatial overlap between sources of activity measured simultaneously with fMRI and EEG. Following the extraction of task-related activity, the key steps include, 1) distributed source reconstruction of the task-related ERP activity (ERP source model), 2) transformation of fMRI activity to the ERP spatial scale by forward modelling of the scalp potential field distribution and backward source reconstruction (fMRI source simulation) …

Differentiating Epileptic From Psychogenic Nonepileptic Eeg Signals Using Time Frequency And Information Theoretic Measures Of Connectivity, Sarah Barnes

Masters Theses

Differentiating psychogenic nonepileptic seizures from epileptic seizures is a difficult task that requires timely recording of psychogenic events using video electroencephalography (EEG). Interpretation of video EEG to distinguish epileptic features from signal artifacts is error prone and can lead to misdiagnosis of psychogenic seizures as epileptic seizures resulting in undue stress and ineffective treatment with antiepileptic drugs. In this study, an automated surface EEG analysis was implemented to investigate differences between patients classified as having psychogenic or epileptic seizures. Surface EEG signals were grouped corresponding to the anatomical lobes of the brain (frontal, parietal, temporal, and occipital) and central coronal …