Biomedical Engineering and Bioengineering Commons^™

Eeg Functional Connectivity In Infants At Elevated Familial Likelihood For Autism Spectrum Disorder, Christian O'Reilly, Scott Huberty, Stefon Van Noordt, James Desjardins, Nicky Wright, Julie Scorah, Sara Jane Webb, Mayada Elsabbagh, Basis Team

Publications

Background

Many studies have reported that autism spectrum disorder (ASD) is associated with atypical structural and functional connectivity. However, we know relatively little about the development of these differences in infancy.

Methods

We used a high-density electroencephalogram (EEG) dataset pooled from two independent infant sibling cohorts, to characterize such neurodevelopmental deviations during the first years of life. EEG was recorded at 6 and 12 months of age in infants at typical (N = 92) or elevated likelihood for ASD (N = 90), determined by the presence of an older sibling with ASD. We computed the functional connectivity between …

Evaluating Eeg–Emg Fusion-Based Classification As A Method For Improving Control Of Wearable Robotic Devices For Upper-Limb Rehabilitation, Jacob G. Tryon

Electronic Thesis and Dissertation Repository

Musculoskeletal disorders are the biggest cause of disability worldwide, and wearable mechatronic rehabilitation devices have been proposed for treatment. However, before widespread adoption, improvements in user control and system adaptability are required. User intention should be detected intuitively, and user-induced changes in system dynamics should be unobtrusively identified and corrected. Developments often focus on model-dependent nonlinear control theory, which is challenging to implement for wearable devices.

One alternative is to incorporate bioelectrical signal-based machine learning into the system, allowing for simpler controller designs to be augmented by supplemental brain (electroencephalography/EEG) and muscle (electromyography/EMG) information. To extract user intention better, sensor …

A Dynamical Systems Approach To Characterizing Brain–Body Interactions During Movement: Challenges, Interpretations, And Recommendations, Derek C. Monroe, Nathaniel T. Berry, Peter C. Fino, Christopher K. Rhea

Rehabilitation Sciences Faculty Publications

Brain–body interactions (BBIs) have been the focus of intense scrutiny since the inception of the scientific method, playing a foundational role in the earliest debates over the philosophy of science. Contemporary investigations of BBIs to elucidate the neural principles of motor control have benefited from advances in neuroimaging, device engineering, and signal processing. However, these studies generally suffer from two major limitations. First, they rely on interpretations of ‘brain’ activity that are behavioral in nature, rather than neuroanatomical or biophysical. Second, they employ methodological approaches that are inconsistent with a dynamical systems approach to neuromotor control. These limitations represent a …

Effects Of Noise Electrical Stimulation On Proprioception, Force Control, And Corticomuscular Functional Connectivity, Li-Wei Chou, Shiang-Lin Hou, Hui-Min Lee, Felipe Fregni, Alice Yen, Vincent Chiun-Fan Chen, Shun-Hwa Wei, Chung-Lan Kao

Engineering Science Faculty Publications

Sensory afferent inputs play an important role in neuromuscular functions. Subsensory level noise electrical stimulation enhances the sensitivity of peripheral sensory system and improves lower extremity motor function. The current study aimed to investigate the immediate effects of noise electrical stimulation on proprioceptive senses and grip force control, and whether there are associated neural activities in the central nervous system. Fourteen healthy adults participated in 2 experiments on 2 different days. In day 1, participants performed grip force and joint proprioceptive tasks with and without (sham) noise electrical stimulation. In day 2, participants performed grip force steady hold task before …

Volitional Control Of Lower-Limb Prosthesis With Vision-Assisted Environmental Awareness, S M Shafiul Hasan

FIU Electronic Theses and Dissertations

Early and reliable prediction of user’s intention to change locomotion mode or speed is critical for a smooth and natural lower limb prosthesis. Meanwhile, incorporation of explicit environmental feedback can facilitate context aware intelligent prosthesis which allows seamless operation in a variety of gait demands. This dissertation introduces environmental awareness through computer vision and enables early and accurate prediction of intention to start, stop or change speeds while walking. Electromyography (EMG), Electroencephalography (EEG), Inertial Measurement Unit (IMU), and Ground Reaction Force (GRF) sensors were used to predict intention to start, stop or increase walking speed. Furthermore, it was investigated whether …

Mapping Of Auditory Cortical Functions Using Electrocorticography, James Robert Swift

Legacy Theses & Dissertations (2009 - 2024)

Communication is a dynamic process through which we translate our inner thoughts in such a way that they can be shared with another person. This complex neurological phenomenon is a key predictor of our productivity and health. When our ability to communicate is compromised, our quality of life suffers. Although numerous methods to investigate the neuroscientific underpinnings of human language exist, our understanding of this process remains incomplete. Improving our understanding of where, when, and how auditory cortical activity occurs can enhance diagnostic techniques and improve treatment methods for neurological conditions that can impact auditory processing, such as epilepsy, or …

Deep Cellular Recurrent Neural Architecture For Efficient Multidimensional Time-Series Data Processing, Lasitha S. Vidyaratne

Electrical & Computer Engineering Theses & Dissertations

Efficient processing of time series data is a fundamental yet challenging problem in pattern recognition. Though recent developments in machine learning and deep learning have enabled remarkable improvements in processing large scale datasets in many application domains, most are designed and regulated to handle inputs that are static in time. Many real-world data, such as in biomedical, surveillance and security, financial, manufacturing and engineering applications, are rarely static in time, and demand models able to recognize patterns in both space and time. Current machine learning (ML) and deep learning (DL) models adapted for time series processing tend to grow in …

The Brain's Large-Scale Electrophysiological Signals : Fundamental Attributes And Neurosurgical Applications, Mohammad Amin N/A Nourmohammadi

Legacy Theses & Dissertations (2009 - 2024)

Brain’s electrophysiological signals are most certainly the ultimate source for studying the sophisticated neural network inside our cranium. The unparalleled complexity of these biosignalsis the quintessential manifestation of their underlying complicated neurophysiological processes. Studying brain signals on the cellular level provides valuable information regarding the brain’s electrophysiology on the small-scale. However, it is the remarkable network in the large-scale that gives rise to the brain’s extraordinary attributes and exceptional capabilities—perception, cognition, computation, and consciousness are all the emergent byproducts of the dynamic neuronal interactions on the network level. In this sense, the large-scale electrophysiological signals, recorded from the surface of …

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 …

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 …

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 …

Computational Characterization Of The Cellular Origins Of Electroencephalography, Shane Hesprich

Master's Theses (2009 -)

Electroencephalography (EEG) is a non-invasive technique used to measure brain activity. Despite its near ubiquitous presence in neuroscience, very little research has gone into connecting the electrical potentials it measures on the scalp to the underlying network activity which generates those signals. This results in most EEG analyses being more macroscopically focused (e.g. coherence and correlation analyses). Despite the many uses of macroscopically focuses analyses, limiting research to only these analyses neglects the insights which can be gained from studying network and microcircuit architecture. The ability to study these things through non-invasive techniques like EEG depends upon the ability to …

Brain Connectivity Networks For The Study Of Nonlinear Dynamics And Phase Synchrony In Epilepsy, Hoda Rajaei

FIU Electronic Theses and Dissertations

Assessing complex brain activity as a function of the type of epilepsy and in the context of the 3D source of seizure onset remains a critical and challenging endeavor. In this dissertation, we tried to extract the attributes of the epileptic brain by looking at the modular interactions from scalp electroencephalography (EEG). A classification algorithm is proposed for the connectivity-based separation of interictal epileptic EEG from normal. Connectivity patterns of interictal epileptic discharges were investigated in different types of epilepsy, and the relation between patterns and the epileptogenic zone are also explored in focal epilepsy.

A nonlinear recurrence-based method is …

Cortical Oscillations During A Lateral Balance Perturbation While Walking, Joseph Lee

Dissertations (1934 -)

The role of sensory systems in the cortical control of dynamic balance was examined using electroencephalography (EEG) recordings during balance perturbations while walking. Specifically, we examined the impact of sensory deficits on cortical oscillations using vibratory stimuli to suppress sensory feedback and by comparing cortical oscillations during balance perturbations while walking in people with sensory deficits associated with cervical myelopathy and neurologically intact controls. Balance during walking provides a rich framework for investigating cortical control using EEG during a functionally relevant task. While this approach is promising, substantial technical challenges remain in recording and processing EEG in the noisy, artifact …

Magnetic Resonance Imaging And Histology Correlation In The Neocortex In Temporal Lobe Epilepsy., Maged Goubran, Robert R Hammond, Sandrine De Ribaupierre, Jorge G Burneo, Seyed Mirsattari, David A Steven, Andrew G Parrent, Terry M Peters, Ali R Khan

Robarts Imaging Publications

OBJECTIVE: To investigate the histopathological correlates of quantitative relaxometry and diffusion tensor imaging (DTI) and to determine their efficacy in epileptogenic lesion detection for preoperative evaluation of focal epilepsy.

METHODS: We correlated quantitative relaxometry and DTI with histological features of neuronal density and morphology in 55 regions of the temporal lobe neocortex, selected from 13 patients who underwent epilepsy surgery. We made use of a validated nonrigid image registration protocol to obtain accurate correspondences between in vivo magnetic resonance imaging and histology images.

RESULTS: We found T1 to be a predictor of neuronal density in the neocortical gray matter (GM) …

Registration Of In-Vivo To Ex-Vivo Mri Of Surgically Resected Specimens: A Pipeline For Histology To In-Vivo Registration., Maged Goubran, Sandrine De Ribaupierre, Robert R Hammond, Catherine Currie, Jorge G Burneo, Andrew G Parrent, Terry M Peters, Ali R Khan

Robarts Imaging Publications

BACKGROUND: Advances in MRI have the potential to improve surgical treatment of epilepsy through improved identification and delineation of lesions. However, validation is currently needed to investigate histopathological correlates of these new imaging techniques. The purpose of this work is to develop and evaluate a protocol for deformable image registration of in-vivo to ex-vivo resected brain specimen MRI. This protocol, in conjunction with our previous work on ex-vivo to histology registration, completes a registration pipeline for histology to in-vivo MRI, enabling voxel-based validation of novel and existing MRI techniques with histopathology.

NEW METHOD: A combination of image-based and landmark-based 3D …

Pre-Saccadic Modulation Of The Visually Evoked Potential, Leslie Guadron

Dissertations and Theses

Saccades are rapid eye movements that allow us to focus the fovea on different parts of our visual environment. Many psychophysical studies have shown that subjects can discriminate stimuli presented at the saccadic target better than at any other location before an eye movement is made. It is believed that covert attention allotted to the intended destination of a saccade, called pre-saccadic attention, accounts for the improved discrimination of stimuli. Covert top-down attention, which consists of orienting your attention, and not your gaze, to an object in your visual periphery, is known to facilitate behavioral performance in a manner similar …

Slow Potentials Of The Sensorimotor Cortex During Rhythmic Movements Of The Ankle, Ryan J. Mckindles

Dissertations (1934 -)

The objective of this dissertation was to more fully understand the role of the human brain in the production of lower extremity rhythmic movements. Throughout the last century, evidence from animal models has demonstrated that spinal reflexes and networks alone are sufficient to propagate ambulation. However, observations after neural trauma, such as a spinal cord injury, demonstrate that humans require supraspinal drive to facilitate locomotion. To investigate the unique nature of lower extremity rhythmic movements, electroencephalography was used to record neural signals from the sensorimotor cortex during three cyclic ankle movement experiments. First, we characterized the differences in slow movement-related …

Scalp Eeg And Tms Based Electrophysiological Study Of Brain Function Of Motor Control In Aging, Mehmed Satuk Bayram

ETD Archive

Voluntary movements of human body are controlled by the brain through corticomuscular pathways. Although neuromuscular control mechanisms of voluntary movements have been studied extensively, many remain to be learned, especially neuromuscular adaptations related to clinical conditions such as neurological disorders and aging. This research aims at a better understanding of functional connection between the brain and muscle during voluntary motor activities in aging and the extent to which this connection can be changed by training the neuromuscular system. Three research projects were conducted to achieve this aim. The analyses in the first two projects are based on comparisons of non-invasive …

Investigation Of The Utility Of Center Frequency In Electroencephalographic Classification Of Cognitive Workload Transitions, Jones Melissa

Browse all Theses and Dissertations

Successful classification of human cognitive workload is a vital component in identifying and avoiding potential performance deficits resulting from operator work overload. Previous research suggests that electroencephalogram (EEG) derived features, including center frequency, provide a robust signal which may be used to obtain highly accurate workload classification. The purpose of this work is to investigate evidence of physiological hysteresis and determine if center frequency improves a classifier's ability to correctly identify workload level. Results confirmed that including spectral data creates the most robust feature sets, while center frequency across all bands is equally reliable for classifying workload in the case …

Electroencephalography (Eeg)-Based Brain Computer Interfaces For Rehabilitation, Dandan Huang

Theses and Dissertations

Objective: Brain-computer interface (BCI) technologies have been the subject of study for the past decades to help restore functions for people with severe motor disabilities and to improve their quality of life. BCI research can be generally categorized by control signals (invasive/non-invasive) or applications (e.g. neuroprosthetics/brain-actuated wheelchairs), and efforts have been devoted to better understand the characteristics and possible uses of brain signals. The purpose of this research is to explore the feasibility of a non-invasive BCI system with the combination of unique sensorimotor-rhythm (SMR) features. Specifically, a 2D virtual wheelchair control BCI is implemented to extend the application of …

Fusion And Visualization Of Intraoperative Cortical Images With Preoperative Models For Epilepsy Surgical Planning And Guidance., A Wang, S M Mirsattari, A G Parrent, T M Peters

Robarts Imaging Publications

OBJECTIVE: During epilepsy surgery it is important for the surgeon to correlate the preoperative cortical morphology (from preoperative images) with the intraoperative environment. Augmented Reality (AR) provides a solution for combining the real environment with virtual models. However, AR usually requires the use of specialized displays, and its effectiveness in the surgery still needs to be evaluated. The objective of this research was to develop an alternative approach to provide enhanced visualization by fusing a direct (photographic) view of the surgical field with the 3D patient model during image guided epilepsy surgery.

MATERIALS AND METHODS: We correlated the preoperative plan …

Measurement Of The Electroencephalogram (Eeg) Coherence, Atmospheric Noise, And Schumann Resonances In Group Meditation, Douglas A. Newandee

Theses

Electrical activity in the human body was investigated using EEG and ECG measurements while subjects remained with eyes open, eyes closed and in a meditation state. During these measurements, additional antennas were attached to the equipment to record atmospheric noise and signal activity simultaneously. The obtained data was analyzed and various observations were made. Processed data based on antenna signals clearly showed the presence of man-made signals, having narrow spectral widths that could be treated as atmospheric noise in the frequency range up to 50 Hz. In addition, signals clustered around 7.8, 14.1, 20.3, 26.4, and 32.5 Hz were observed …

Electroencephalography: Subdural Multi-Electrode Brain Chip, John E. Rosenstengel

Theses and Dissertations

In October 1995, a CMOS brain chip consisting of two 8 x 17 multiplexed sub-arrays designed to measure electrical potentials at the cortical column level, was implanted on the somatosensory cortex of a laboratory rhesus monkey. Electroencephalograph (EEG) and averaged evoked response (AEG) data were taken over a period of 40 minutes. The brain chip was replaced with an identical chip, and data were again taken for 40 minutes. In both instances AEG signals of approximately 150 µV_pp were recorded. Additionally, the first implanted chip recorded three phases of data: (1) AEG; (2) large clock noise (during a period …

Characterization Of Sleep Eeg, Seetharamiah Sateesh

Theses

The physiological relationship between the various components of sleep and its variation due to drug administration has been used as one of the primary tools to analyze the performance of drug. A number of studies have been performed in recent years in this direction. Electroencephalogram (EEG) has been characterized with the help of variables ranging from measurements of the duration of different sleep stages to the activities that define the stages themselves. Advances in computer hardware and software have improved the methods of data acquisition and storage. Analysis of long stretch of data has always been a problem considering the …

The Afit Multielectrode Array For Neural Recording And Simulation: Design, Testing, And Encapsulation, James R. Reid Jr

Theses and Dissertations

A two-dimensional, X-Y addressable, multiplexed array of 256 electrodes (16 x 16) has been fabricated using conventional semiconductor processing techniques. The individual electrodes are 16O microns x 160 microns, approximating the size of the cortical columns; the overall array size is 3910 microns x 3910 microns. The array has been fitted to a chronically implantable package and tested for several days in a simulated neural environment. EEG-like data were collected successfully from individual electrodes in the array. This array improves on a previous design of a 16 electrode (4 x 4) array that was chronically implanted on the cortex of …