Engineering Commons^™

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 …

Enabling Daily Tracking Of Individual’S Cognitive State With Eyewear, Soha Rostaminia

Doctoral Dissertations

Research studies show that sleep deprivation causes severe fatigue, impairs attention and decision making, and affects our emotional interpretation of events, which makes it a big threat to public safety, and mental and physical well-being. Hence, it would be most desired if we could continuously measure one’s drowsiness and fatigue level, their emotion while making decisions, and assess their sleep quality in order to provide personalized feedback or actionable behavioral suggestions to modulate sleep pattern and alertness levels with the aim of enhancing performance, well-being, and quality of life. While there have been decades of studies on wearable devices, we …

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 …

Seizure Detection Using Deep Learning, Information Theoretic Measures And Factor Graphs, Bahareh Salafian

Electronic Thesis and Dissertation Repository

Epilepsy is a common neurological disorder that disrupts normal electrical activity in the brain causing severe impact on patients’ daily lives. Accurate seizure detection based on long-term time-series electroencephalogram (EEG) signals has gained vital importance for epileptic seizure diagnosis. However, visual analysis of these recordings is a time-consuming task for neurologists. Therefore, the purpose of this thesis is to propose an automatic hybrid model-based /data-driven algorithm that exploits inter-channel and temporal correlations. Hence, we use mutual information (MI) estimator to compute correlation between EEG channels as spatial features and employ a carefully designed 1D convolutional neural network (CNN) to extract …

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 …

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 …

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 …

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 …

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. …

Analysis And Enhancement Of Human Cognitive Control Using Noninvasive Brain-Computer Interfaces, Soheil Borhani

Doctoral Dissertations

Cognitive control including attention and working memory are crucial to human daily life. Whether a civilian who walks across a street or a military service member who is responsible for navigating a mission, cognitive control is involved, entirely. This ability is subject to impairment. People with attention disorder are easily disposed to distraction and lacks the ability to maintain the focus to a task. Multiple treatment strategies have been suggested which most of them has been pharmaceutical. Evidently, the medical treatment has side effects for long-term use. Moreover, it has a risk of drug misuse. Another line of treatment is …

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 …

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 …

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 …

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°) …

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 …

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) …

Interpolated Perturbation-Based Decomposition As A Method For Eeg Source Localization, Gabriel Zelik Lipof

Master's Theses

In this thesis, the perturbation-based decomposition technique developed by Szlavik [1] was used in an attempt to solve the inverse problem in EEG source localization. A set of dipole locations were forward modeled using a 4-layer sphere model of the head at uniformly distributed lead locations to form the vector basis necessary for the method. Both a two-dimensional and a pseudo-three-dimensional versions of the model were assessed with the two-dimensional model yielding decompositions with minimal error and the pseudo-three-dimensional version having unacceptable levels of error. The utility of interpolation as a method to reduce the number of data points to …

Identification Of The Seizure Onset Zone By Auto-Regressive Model Residual Modulation Applied To Intracranial Eeg And Its Correlation To Channels With High Preponderances Of Detected Hfos, Allison L. Rogutich

Masters Theses

The objective of this thesis was to examine the ability of the Autoregressive Model Residual Modulation (ARRm) method to identify the Seizure Onset Zone (SOZ) in intracranial electroencephalogram (iEEG) of patients with refractory epilepsy. Patients who have not become seizure free after multiple trials of antiepileptic drugs (AEDs) may seek treatment through epilepsy surgery. Cortical electrodes are implanted directly on the cerebral cortex, then iEEG is collected. A specialized neurologist reviews the iEEG, then in consultation with the neurosurgeon, the SOZ is determined and areas of the brain may be chosen for resection. The success rate of epilepsy surgery varies, …

Cortical Statistical Correlation Tomography Of Eeg Resting State Networks, Chuang Li, Han Yuan, Guofa Shou, Yoon-Hee Cha, Sridhar Sunderam, Walter Besio, Lei Ding

Biomedical Engineering Faculty Publications

Resting state networks (RSNs) have been found in human brains during awake resting states. RSNs are composed of spatially distributed regions in which spontaneous activity fluctuations are temporally and dynamically correlated. A new computational framework for reconstructing RSNs with human EEG data has been developed in the present study. The proposed framework utilizes independent component analysis (ICA) on short-time Fourier transformed inverse source maps imaged from EEG data and statistical correlation analysis to generate cortical tomography of electrophysiological RSNs. The proposed framework was evaluated on three sets of resting-state EEG data obtained in the comparison of two conditions: (1) healthy …

Effect Of Sensory Attenuation On Cortical Movement-Related Oscillations, Joseph J. Lee, Brian Schmit

Biomedical Engineering Faculty Research and Publications

This study examined the impact of induced sensory deficits on cortical, movement-related oscillations measured using electroencephalography (EEG). We hypothesized that EEG patterns in healthy subjects with induced sensory reduction would be comparable to EEG found after chronic loss of sensory feedback. EEG signals from 64 scalp locations were measured from 10 healthy subjects. Participants dorsiflexed their ankle after prolonged vibration of the tibialis anterior (TA). Beta band time frequency decompositions were calculated using wavelets and compared across conditions. Changes in patterns of movement-related brain activity were observed following attenuation of sensory feedback. A significant decrease in beta power of event-related …

Eeg And Emg Sensorimotor Measurements To Assess Proprioception Following Acl Reconstruction, Teagan Frances Northrup

Honors Theses and Capstones

The Anterior Cruciate Ligament (ACL) is the primary source of rotational stability in the knee by preventing the tibia from sliding in front of the femur. When the ACL is torn, it typically must be repaired through reconstructive surgery which results in proprioceptive deficiencies in the knee. Proprioception plays an important role in understanding where one’s knee is in space, sensing movement and reacting accordingly. This study examines an alternative method of measuring proprioceptive responses to a stimulus (motion) by using electromyogram (EMG) and electroencephalogram (EEG) signals to observe muscle and brain activity. Two participants (one with an ACL reconstruction …

Eeg Characterization During Motor Tasks That Are Difficult For Movement Disorder Patients, Adam Joshua Aslam

Master's Theses

Movement disorders are a group of syndromes that often arise due to neurological abnormalities. Approximately 40 million Americans are affected by some form of movement disorder, significantly impacting patients’ quality of life and their ability to live independently. Deep brain stimulation (DBS) is one treatment that has shown promising results in the past couple decades, however, the currently used open-loop system has several drawbacks. By implementing a closed-loop or adaptive DBS (aDBS) system, the need for expensive parameter reprogramming sessions would be reduced, side-effects may be relieved, and habituation could be avoided. Several biomarkers, for example signals or activity derived …

Gui For Mri-Compatible Neural Stimulator And Recorder, Soo Han Soon, Nishant Babaria, Ranajay Mandal, Zhongming Liu

The Summer Undergraduate Research Fellowship (SURF) Symposium

Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are useful tools to analyze brain activities given active stimulation. However, the electromagnetic noise from the MRI distorts the brain signal recording and damages the subject with excessive heat generated on the electrodes attached to the skin. MRI-compatible recording and stimulation systems previously developed at LIBI lab were capable of removing the electromagnetic noise during the imaging process. Previously, the hardware systems had required the integrative software that could control both circuits simultaneously and enable users to easily change recording and stimulation parameters. Graphical user interface (GUI) programmed with computer language informed …