Engineering Commons^™

Eareeg Final Report, Tyler Stuessi, Jeremy Herwig, Dillon Hunneke, Evan Goble, Arthur Vidineyev

Chancellor’s Honors Program Projects

No abstract provided.

Alpha Rhythm And The Default Mode Network: An Eeg/Fmri Study, Anthony Dean Bowman

All ETDs from UAB

Reports of the relationship between the default mode network (DMN) and alpha power are conflicting in the literature. Our goal for this study was to assess this relationship by analyzing concurrently obtained EEG/fMRI data using hypothesis-independent methods. To accomplish this, we collected fMRI and EEG data during eyes-closed rest in 20 participants aged 19-37 (10 females) and performed independent component analysis on the fMRI data and a Hamming windowed Fast Fourier Transform on the EEG data. We correlated fMRI fluctuations in the DMN with alpha power. Of the six independent components (ICs) found to have significant relationships with alpha, four …

Closed-Loop Afferent Nerve Electrical Stimulation For Rehabilitation Of Hand Function In Subjects With Incomplete Spinal Cord Injury, Christopher J. Schildt

Theses and Dissertations--Biomedical Engineering

Peripheral nerve stimulation (PNS) is commonly used to promote use-dependent cortical plasticity for rehabilitation of motor function in spinal cord injury. Pairing transcranial magnetic stimulation (TMS) with PNS has been shown to increase motor evoked potentials most when the two stimuli are timed to arrive in the cortex simultaneously. This suggests that a mechanism of timing-dependent plasticity (TDP) may be a more effective method of promoting motor rehabilitation. The following thesis is the result of applying a brain-computer interface to apply PNS in closed-loop simultaneously to movement intention onset as measured by EEG of the sensorimotor cortex to test whether …

Extrema Based Signal Transforms For Biomedical Signal Analysis, Bhadhan Roy Joy

All ETDs from UAB

Signal transforms are very important tools to extract useful information from scientific, engineering, or medical raw data. Unfortunately, traditional transform techniques impose unrealistic assumptions on the signal, often producing erroneous interpretation of results. Well-known integral transforms, such as short time Fourier transform, though have fast implementation algorithms (e.g., FFT), are still computationally expensive. They have multiple parameters that should be tuned, and it is not readily clear how to tune them for long-duration nonstationary signals. To solve these problems, one needs a computationally inexpensive transform with no parameters that will highlight important data aspects. We propose a simple transform based …

Human Intracranial High Frequency Oscillation Detection Using Time Frequency Analysis And Its Relation To The Seizure Onset Zone, Riazul Islam

Masters Theses

One third of the patients diagnosed with focal epilepsy do not respond to antiepileptic drugs. For these patients the possible diagnosis options to give seizure freedom or at least reduce seizure frequencies significantly would be surgical resection or seizure interrupting implantable devices. The success of these procedures depends on accurate detection of the region causing seizure also known as epileptic zone. This requires detail pre-surgical evaluation including Invasive Video Electroencephalographic Monitoring (IVEM). The resulting great volume of intracranial Electroencephalography (iEEG) signal is visually examined by an expert epileptologist which can be time consuming, extremely complex, and not always effective. We …

Multichannel Characterization Of Brain Activity In Neurological Impairments, Yalda Shahriari

Biomedical Engineering Theses & Dissertations

Hundreds of millions of people worldwide suffer from various neurological and psychiatric disorders. A better understanding of the underlying neurophysiology and mechanisms for these disorders can lead to improved diagnostic techniques and treatments. The objective of this dissertation is to create a novel characterization of multichannel EEG activity for selected neurological and psychiatric disorders based on available datasets. Specifically, this work provides spatial, spectral, and temporal characterizations of brain activity differences between patients/animal models and healthy controls, with focus on modern techniques that quantify cortical connectivity, which is widely believed to be abnormal in such disorders. Exploring the functional brain …

Development Of A Practical Visual-Evoked Potential-Based Brain-Computer Interface, Nicholas R. Waytowich

Biomedical Engineering Theses & Dissertations

There are many different neuromuscular disorders that disrupt the normal communication pathways between the brain and the rest of the body. These diseases often leave patients in a `locked-in" state, rendering them unable to communicate with their environment despite having cognitively normal brain function. Brain-computer interfaces (BCIs) are augmentative communication devices that establish a direct link between the brain and a computer. Visual evoked potential (VEP)- based BCIs, which are dependent upon the use of salient visual stimuli, are amongst the fastest BCIs available and provide the highest communication rates compared to other BCI modalities. However. the majority of research …

Experimental-Computational Analysis Of Vigilance Dynamics For Applications In Sleep And Epilepsy, Farid Yaghouby

Theses and Dissertations--Biomedical Engineering

Epilepsy is a neurological disorder characterized by recurrent seizures. Sleep problems can cooccur with epilepsy, and adversely affect seizure diagnosis and treatment. In fact, the relationship between sleep and seizures in individuals with epilepsy is a complex one. Seizures disturb sleep and sleep deprivation aggravates seizures. Antiepileptic drugs may also impair sleep quality at the cost of controlling seizures. In general, particular vigilance states may inhibit or facilitate seizure generation, and changes in vigilance state can affect the predictability of seizures. A clear understanding of sleep-seizure interactions will therefore benefit epilepsy care providers and improve quality of life in patients. …

Improving Golf Putt Performance With Statistical Learning Of Eeg Signals, Qing Guo

Graduate Theses and Dissertations

In this thesis, a machine learning based method is proposed to predict the putt outcomes of golfers based on their electroencephalogram (EEG) data. The method can be used as a core building block of a brain-computer interface, which is designed to provide guidance to golf players based on their EEG patterns. The proposed method includes three steps. First, multi-channel 1-second EEG trials were extracted during golfers' preparation of putting. Second, different features are calculated such as correlation coefficient, power spectrum density and coherence, which are used as features for the classification algorithm. To predict golfers' performance, the support vector machine …

Empirical Modeling Of Asynchronous Scalp Recorded And Intracranial Eeg Potentials, Komalpreet Kaur

Electrical & Computer Engineering Theses & Dissertations

A Brain-Computer Interface (BCI) is a system that allows people with severe neuromuscular disorders to communicate and control devices using their brain signals. BCIs based on scalp-recorded electroencephalography (s-EEG) have recently been demonstrated to provide a practical, long-term communication channel to severely disabled users. These BCIs use time-domain s-EEG features based on the P300 event-related potential to convey the user's intent. The performance of s-EEG-based BCIs has generally stagnated in recent years, and high day-to-day performance variability exists for some disabled users. Recently intracranial EEG (i-EEG), which is recorded from the cortical surface or the hippocampus, has been successfully used …

Dynamic Complexity And Causality Analysis Of Scalp Eeg For Detection Of Cognitive Deficits, Joseph Curtis Mcbride

Doctoral Dissertations

This dissertation explores the potential of scalp electroencephalography (EEG) for the detection and evaluation of neurological deficits due to moderate/severe traumatic brain injury (TBI), mild cognitive impairment (MCI), and early Alzheimer’s disease (AD). Neurological disorders often cannot be accurately diagnosed without the use of advanced imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). Non-quantitative task-based examinations are also used. None of these techniques, however, are typically performed in the primary care setting. Furthermore, the time and expense involved often deters physicians from performing them, leading to potential worse prognoses for patients.

If …

A Quantitative Tool For Identifying The Epileptogenic Zone Using Network Connectivity Analysis, James Michael Gurisko

Masters Theses

Approximately one-third of patients diagnosed with focal epilepsy do not respond to medication and may be candidates for surgery to remove epileptogenic tissue known as the epileptogenic zone. A detailed pre-surgical evaluation is required and often includes invasive video electroencephalographic monitoring (IVEM) using intracranial surface and depth electrodes, and a camera. The resulting large pools of electrocorticorticographic (ECoG) data are manually analyzed by an expert epileptologist to determine epileptic events. The process is time consuming and prone to human error. This thesis investigates the use of measures to identify the causal relationship between ECoG signals during propagation of a seizure …

Development Of A Compact, Low-Cost Wireless Device For Biopotential Acquisition, Graham Kelly

Theses and Dissertations

A low-cost circuit board design is presented, which in one embodiment is smaller than a credit card, for biopotential (EMG, ECG, or EEG) data acquisition, with a focus on EEG for brain-computer interface applications. The device combines signal conditioning, low-noise and high-resolution analog-to-digital conversion of biopotentials, user motion detection via accelerometer and gyroscope, user-programmable digital pre-processing, and data transmission via Bluetooth communications. The full development of the device to date is presented, spanning three embodiments. The device is presented both as a functional data acquisition system and as a template for further development based on its publicly-available schematics and computer-aided …

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

Browse all Theses and Dissertations

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

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

Development Of An Eeg Brain-Machine Interface To Aid In Recovery Of Motor Function After Neurological Injury, Elizabeth Salmon

Theses and Dissertations--Biomedical Engineering

Impaired motor function following neurological injury may be overcome through therapies that induce neuroplastic changes in the brain. Therapeutic methods include repetitive exercises that promote use-dependent plasticity (UDP), the benefit of which may be increased by first administering peripheral nerve stimulation (PNS) to activate afferent fibers, resulting in increased cortical excitability. We speculate that PNS delivered only in response to attempted movement would induce timing-dependent plasticity (TDP), a mechanism essential to normal motor learning. Here we develop a brain-machine interface (BMI) to detect movement intent and effort in healthy volunteers (n=5) from their electroencephalogram (EEG). This could be used in …

Low Cost Neurochairs, Frankie Pike

Master's Theses

Electroencephalography (EEG) was formerly confined to clinical and research settings with the necessary hardware costing thousands of dollars. In the last five years a number of companies have produced simple electroencephalograms, priced below $300 and available direct to consumers. These have stirred the imaginations of enthusiasts and brought the prospects of "thought-controlled" devices ever closer to reality. While these new devices were largely targeted at video games and toys, active research on enabling people suffering from debilitating diseases to control wheelchairs was being pursued. A number of neurochairs have come to fruition offering a truly hands-free mobility solution, but whether …

Integration Of Eeg-Fmri In An Auditory Oddball Paradigm Using Joint Independent Component Analysis, Jain Mangalathu Arumana

Dissertations (1934 -)

The integration of event-related potential (ERP) and functional magnetic resonance imaging (fMRI) can contribute to characterizing neural networks with high temporal and spatial resolution. The overall objective of this dissertation is to determine the sensitivity and limitations of joint independent component analysis (jICA) within-subject for integration of ERP and fMRI data collected simultaneously in a parametric auditory oddball paradigm. The main experimental finding in this work is that jICA revealed significantly stronger and more extensive activity in brain regions associated with the auditory P300 ERP than a P300 linear regression analysis, both at the group level and within-subject. The results …

Optimization Of Feature Selection In A Brain-Computer Interface Switch Based On Event-Related Desynchronization And Synchronization Detected By Eeg, Mason Montgomery

Theses and Dissertations

There are hundreds of thousands of people who could benefit from a Brain-Computer Interface. However, not all are willing to undergo surgery, so an EEG is the prime candidate for use as a BCI. The features of Event-Related Desynchronization and Synchronization could be used for a switch and have been in the past. A new method of feature selection was proposed to optimize classification of active motor movement vs a non-active idle state. The previous method had pre-selected which frequency and electrode to use as electrode C3 at the 20Hz bin. The new method used SPSS statistical software to determine …

Neural Correlates Of Phantom Auditory Perception, Paul Joseph S. Deguzman

Dissertations and Theses

No abstract provided.

Monitoring, Diagnosis, And Control For Advanced Anesthesia Management, Zhibin Tan

Wayne State University Dissertations

Modern anesthesia management is a comprehensive and the most critical issue in medical care. During the past dacades, a large amount of research works have been focused on the problems of monitoring anesthesia depth, modeling the dynamics of anesthesia patient for the purpose of control, prediction, and diagnosis.

Monitoring the anesthesia depth is not only for keeping the patient in adquate anesthesia level but also for preventing the patient from overdosing. Several EEG based indexes have been developed such as the BIS, and Entropy etc. for measuring depth. However, reports mentioned that those indexes in some cases fail in detecting …

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

Browse all Theses and Dissertations

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

Biosignal Processing Challenges In Emotion Recognitionfor Adaptive Learning, Aniket Vartak

Electronic Theses and Dissertations

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 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 …

Computer-Aided Detection Of Sleep Apnea And Sleep Stage Classification Using Hrv And Eeg Signals, Edson F. Estrada

Open Access Theses & Dissertations

Sleep is a circadian rhythm essential for human life. Many events occur in the body during this state. In the past, significant efforts have been made to provide clinicians with reliable and less intrusive tools to automatically classify the sleep stages and detect apnea events. A few systems are available in the market to accomplish this task. However, sleep specialists may not have full confidence and trust in such systems due to issues related to their accuracy, sensitivity and specificity. The main objective of this work is to explore possible relationships among sleep stages and apneic events and improve on …

Development Of An Electroencephalography-Based Brain-Computer Interface Supporting Two-Dimensional Cursor Control, Dandan Huang

Theses and Dissertations

This study aims to explore whether human intentions to move or cease to move right and left hands can be decoded from spatiotemporal features in non-invasive electroencephalography (EEG) in order to control a discrete two-dimensional cursor movement for a potential multi-dimensional Brain-Computer interface (BCI). Five naïve subjects performed either sustaining or stopping a motor task with time locking to a predefined time window by using motor execution with physical movement or motor imagery. Spatial filtering, temporal filtering, feature selection and classification methods were explored. The performance of the proposed BCI was evaluated by both offline classification and online two-dimensional cursor …

Bio-Signal Analysis In Fatigue And Cancer Related Fatigue;Weakening Of Corticomuscular Functional Coupling, Qi Yang

ETD Archive

Fatigue is a common experience that reduces productivity and increases chance of injury, and has been reported as one of most common symptoms with greatest impact on quality-of-life parameters in cancer patients. Neural mechanisms behind fatigue and cancer related fatigue (CRF) are not well known. Recent research has shown dissociation between changes in brain and muscle signals during voluntary muscle fatigue, which may suggest weakening of functional corticomuscular coupling (fCMC). However, this weakening of brain-muscle coupling has never been directly evaluated. More important information could be gained if fCMC is directly detected during fatigue because a voluntary muscle contraction depends …

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

Browse all Theses and Dissertations

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