Engineering Commons^™

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. …

Wavelet-Based Single-Channel Eeg Features For The Automated Recognition Of Human Emotion, Anjan Gudigar, Raghavendra U Dr., Maithri M, Samir Kumar Praharaj Dr

Manipal Journal of Science and Technology

Human emotion is a physical and physiological activity that is triggered in response to internal or external stimulation. Automating emotion recognition is a challenging task since emotions change continuously with time, situation, etc. It is a complex activity. Recently, multiple studies were undertaken using CAD tools to automatically detect emotions. EEG signals are one mode through which emotions can be captured. These signals are captured using multiple channels. Processing all these channel features can be a cumbersome task. Hence in the current study, we tried to recognize the emotions using a single T7 channel. Initially, the features of the pre-processed …

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 …

A Routine Electroencephalography Monitoring System For Automated Sports-Related Concussion Detection, Amirsalar Mansouri, Patrick Ledwidge, Khalid Sayood, Dennis L. Molfese

Department of Electrical and Computer Engineering: Faculty Publications

Cases of concussions in the United States keep increasing and are now up to 2 million to 3 million incidents per year. Although concussions are recoverable and usually not life-threatening, the degree and rate of recovery may vary depending on age, severity of the injury, and past concussion history. A subsequent concussion before full recovery may lead to more-severe brain damage and poorer outcomes. Electroencephalography (EEG) recordings can identify brain dysfunctionality and abnormalities, such as after a concussion. Routine EEG monitoring can be a convenient method for reducing unreported injuries and preventing long-term damage, especially among groups with a greater …

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 …

Accelerometer-Based Vigilance State Classification In Dairy Cows, Evan King

Theses and Dissertations--Electrical and Computer Engineering

Globally, dairy farming is a $700 billion industry, with more than 9 million dairy cows in the United States alone. Depriving cows of required activities such as sleep has been shown to negatively impact reproductive efficiency, decrease the volume of milk produced, and increase the risk of culling. Overcrowded herds can decrease individual animal health, demanding the need for automatic behavior detection that would provide insight into their state of health.

Using electroencephalography (EEG), electrooculography (EOG), and electromyography (EMG) to characterize the phases of sleep is a technique which has been used for decades. While these techniques are considered the …

Examination Of Driver Visual And Cognitive Responses To Billboard Elicited Passive Distraction Using Eye-Fixation Related Potential, Yongxiang Wang, William Clifford, Charles Markham, Catherine Deegan

Articles

Distractions external to a vehicle contribute to visual attention diversion that may cause traffic accidents. As a low-cost and efficient advertising solution, billboards are widely installed on side of the road, especially the motorway. However, the effect of billboards on driver distraction, eye gaze, and cognition has not been fully investigated. This study utilises a customised driving simulator and synchronised electroencephalography (EEG) and eye tracking system to investigate the cognitive processes relating to the processing of driver visual information. A distinction is made between eye gaze fixations relating to stimuli that assist driving and others that may be a source …

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. …

Classification Of P300 Based Brain Computer Interface Systems Using Longshort-Term Memory (Lstm) Neural Networks With Feature Fusion, Ali̇ Osman Selvi̇, Abdullah Feri̇koğlu, Derya Güzel

Turkish Journal of Electrical Engineering and Computer Sciences

Enabling to obtain brain activation signs, electroencephalography is currently used in many applications as a medical diagnostic method. Brain-computer interface (BCI) applications are developed to facilitate the lives of individuals who have not lost their brain functions yet have lost their motor and communication abilities. In this study, a BCI system is proposed to make classification using Bi-directional long short term memory (Bi-LSTM) neural networks. In the designed system, spectral entropy method including instantaneous frequency change of signal is used as feature fusion. In the study, electroencephalography (EEG) data of 10 participants are collected with Emotiv EPOC+ device using 2x2 …

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 …