Engineering Commons^™

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 …

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 …

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 …