Signal Processing Commons^™

Estimating And Detecting Slow-Wave Events In Eeg Signals, Zhenghao Xiong

McKelvey School of Engineering Theses & Dissertations

Slow wave activity (SWA) is an electroencephalogram (EEG) pattern commonly occurring during anesthesia and deep sleep, and is hence a candidate biomarker to quantify such states and understand their connection to various phenotypes. SWA consists of individual slow waves (ISW), high-amplitude deflections lasting for approximately 0.5 to 1 second, and occurring quasi-periodically. This latter fact poses a challenge for conventional power spectral density EEG analysis methods that perform best when there is persistency of oscillatory activity. In this work, we pursue a time-domain detection framework for identifying and quantifying ISWs as a metric for SWA. Our method works, in essence, …

Eeg-Based Spanish Language Proficiency Classification: An Eeg Power Spectrum And Cross-Spectrum Analysis, Blaise Xavier O'Mara, Skyler Baumer

Honors Theses and Capstones

Second language proficiency may be predicted with electrophysiological techniques. In a machine learning application, this electrophysiological data may be used for language instructors and language students to assess their language learning. This study identifies how electroencephalogram (EEG) power spectrum and cross spectrum data of the brain cortex relates to Spanish second language (L2) proficiency of 20 Spanish language students of varying proficiency levels at the University of New Hampshire. The two metrics for assessing cortical power and processing were event-related desynchronization (ERD)—a measure of relative change in power—of the alpha (8-12 Hz) brain frequency band, and alpha and beta (13-30Hz) …

Enabling Daily Tracking Of Individual’S Cognitive State With Eyewear, Soha Rostaminia

Doctoral Dissertations

Research studies show that sleep deprivation causes severe fatigue, impairs attention and decision making, and affects our emotional interpretation of events, which makes it a big threat to public safety, and mental and physical well-being. Hence, it would be most desired if we could continuously measure one’s drowsiness and fatigue level, their emotion while making decisions, and assess their sleep quality in order to provide personalized feedback or actionable behavioral suggestions to modulate sleep pattern and alertness levels with the aim of enhancing performance, well-being, and quality of life. While there have been decades of studies on wearable devices, we …

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 …

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 …

A Robust Lpc Filtering Method For Time-Resolved Morphology Of Eeg Activity Analysis, Jin Xu, Mark Davis, Ruairí De Fréin

Conference Papers

This paper introduces a new time-resolved spectral analysis method based on Linear Prediction Coding (LPC) method that is particularly suited to the study of the dynamics of EEG (Electroencephalogram) activity. The spectral dynamic of EEG signals can be challenging to analyse as they contain multiple frequency components and are often heavily corrupted by noise. Furthermore, the temporal and spectral resolution that can be achieved is limited by the Heisenberg-Gabor uncertainty principle [1]. The method described here is based on a z-plane analysis of the poles of the LPC which allows us to identify and estimate the frequency of the dominant …

Eeg And Emg Sensorimotor Measurements To Assess Proprioception Following Acl Reconstruction, Teagan Frances Northrup

Honors Theses and Capstones

The Anterior Cruciate Ligament (ACL) is the primary source of rotational stability in the knee by preventing the tibia from sliding in front of the femur. When the ACL is torn, it typically must be repaired through reconstructive surgery which results in proprioceptive deficiencies in the knee. Proprioception plays an important role in understanding where one’s knee is in space, sensing movement and reacting accordingly. This study examines an alternative method of measuring proprioceptive responses to a stimulus (motion) by using electromyogram (EMG) and electroencephalogram (EEG) signals to observe muscle and brain activity. Two participants (one with an ACL reconstruction …

Gui For Mri-Compatible Neural Stimulator And Recorder, Soo Han Soon, Nishant Babaria, Ranajay Mandal, Zhongming Liu

The Summer Undergraduate Research Fellowship (SURF) Symposium

Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are useful tools to analyze brain activities given active stimulation. However, the electromagnetic noise from the MRI distorts the brain signal recording and damages the subject with excessive heat generated on the electrodes attached to the skin. MRI-compatible recording and stimulation systems previously developed at LIBI lab were capable of removing the electromagnetic noise during the imaging process. Previously, the hardware systems had required the integrative software that could control both circuits simultaneously and enable users to easily change recording and stimulation parameters. Graphical user interface (GUI) programmed with computer language informed …

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. …

Estimating Dynamic Cortical Connectivity From Motor Imagery Eeg Using Kalman Smoother & Em Algorithm, S. Balqis Samdin, Chee-Ming Ting Phd, Sh-Hussain Salleh, Mahyar Hamedi, Alias Mohd Noor

Chee-Ming Ting

This paper considers identifying effective cortical connectivity from scalp EEG. Recent studies use time-varying multivariate autoregressive (TV-MAR) models to better describe the changing connectivity between cortical regions where the TV coefficients are estimated by Kalman filter (KF) within a state-space framework. We extend this approach by incorporating Kalman smoothing (KS) to improve the KF estimates, and the expectation-maximization (EM) algorithm to infer the unknown model parameters from EEG. We also consider solving the volume conduction problem by modeling the induced instantaneous correlations using a full noise covariate. Simulation results show the superiority of KS in tracking the coefficient changes. We …