Open Access. Powered by Scholars. Published by Universities.®

Bioelectrical and Neuroengineering Commons™

Open Access. Powered by Scholars. Published by Universities.®

Machine Learning

Articles 1 - 6 of 6

Full-Text Articles in Bioelectrical and Neuroengineering

Advancing Electrical Stimulation: Full-Head Mri Segmentation For Abnormal Brain Anatomy With Tdcs, Andrew Birnbaum Jan 2025

Advancing Electrical Stimulation: Full-Head Mri Segmentation For Abnormal Brain Anatomy With Tdcs, Andrew Birnbaum

Dissertations and Theses

Evaluating the effectiveness of transcranial direct current stimulation (tDCS) is essential for guiding its integration into therapeutic and performance-enhancement applications. In our laboratory, we investigate the efficacy of tDCS across multiple experimental models, including both animal and human studies. I have contributed significantly to the execution and analysis of these experiments, which include studies in rats and healthy human participants aimed at evaluating whether electrical stimulation of the motor cortex can enhance motor learning. These studies assess improvements in fine motor performance resulting from tDCS. In stroke patients, I contribute to our investigation of tDCS as a rehabilitative intervention, particularly …

Go to article

Resting-State Eeg Microstate Features For Major Depressive Disorder Classification, George M. V. Quinn Sep 2024

Resting-State Eeg Microstate Features For Major Depressive Disorder Classification, George M. V. Quinn

Dissertations, Theses, and Capstone Projects

Neuroimaging studies have revealed consistent abnormalities in functional connectivity within specific neural networks that may serve as biomarkers for major depressive disorder (MDD). It is important to find inexpensive, non-invasive techniques that target these biomarkers to make diagnosis easier and more objective. EEG microstates are quasi-stable potential topographies that are thought to reflect the quasi-stable network activity of the underlying neural generators. MDD has been shown to alter features of the four canonical EEG microstates (A, B, C, D) with some conflicting results. The most consistent network abnormalities in MDD are found in the anterior default mode network, and this …

Go to article

Data-Driven Insights Into Spatial Patterns And Disease Etiologies Of White Matter Hyperintensities, Sugandha Roy May 2024

Data-Driven Insights Into Spatial Patterns And Disease Etiologies Of White Matter Hyperintensities, Sugandha Roy

McKelvey School of Engineering Graduate Student Theses & Dissertations

In this thesis, we have applied Orthogonal Projective Non-Negative Matrix Factorization (opNMF) to identify spatial patterns of white matter hyperintensities (WMH) within UK Biobank's imaging data. Our selection criteria excluded subjects with a history of neurological, mental, and specific cerebrovascular conditions, allowing us to focus on WMH patterns in a healthy aging population. We have interrogated the association of location-specific WMHs with a variety of demographic, clinical, and genetic factors. Our multivariable regression analysis evaluates the strength and nature of the associations between these factors and WMH distribution. The analysis integrates variables such as age, sex, smoking habits, medication usage …

Go to article

Multi-Magnetic Material Transcranial Magnetic Stimulation Coils Development And Electric Field Measurement & Modeling Using Machine Learning, Mohannad Tashli Jan 2024

Multi-Magnetic Material Transcranial Magnetic Stimulation Coils Development And Electric Field Measurement & Modeling Using Machine Learning, Mohannad Tashli

Theses and Dissertations

Transcranial Magnetic Stimulation (TMS) is a safe, effective, and non-invasive therapy for treating several psychiatric and neurological disorders. TMS is Food and Drug Administration (FDA) approved treatment and is commonly applied to patients who do not respond to medications for the treatment of clinical depression, smoking cessation, obsessive-compulsive disorder and migraine. Recently, there has been an increase in the development of electromagnetic neuromodulation techniques targeted at enhancing the effectiveness of TMS devices for the treatment of mental diseases. In TMS stimulation, focality is an important factor which determines the specificity of the pulses induced in different brain tissues. The electromagnetic …

Go to article

Estimating Affective States In Virtual Reality Environments Using The Electroencephalogram, Meghan R. Kumar Jan 2021

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 …

Go to article

Finding A Viable Neural Network Architecture For Use With Upper Limb Prosthetics, Maxwell Lavin Dec 2019

Finding A Viable Neural Network Architecture For Use With Upper Limb Prosthetics, Maxwell Lavin

Master of Science in Computer Science Theses

This paper attempts to answer the question of if it’s possible to produce a simple, quick, and accurate neural network for the use in upper-limb prosthetics. Through the implementation of convolutional and artificial neural networks and feature extraction on electromyographic data different possible architectures are examined with regards to processing time, complexity, and accuracy. It is found that the most accurate architecture is a multi-entry categorical cross entropy convolutional neural network with 100% accuracy. The issue is that it is also the slowest method requiring 9 minutes to run. The next best method found was a single-entry binary cross entropy …

Go to article