Neural Engineering Research
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The Neural Engineering Laboratory is housed within the College of Engineering and Computing and is comprised of faculty and students from the Electrical and Computer Engineering Department and the Department of Bioengineering at Mason. The research programs of the Neural Engineering Laboratory involve the development and use of in vitro and in vivo neural interface technologies. Specific projects are focused on dynamical modeling and pattern steering of living neuronal networks, the development and characterization of novel materials for neural interface technologies, exploration of new paradigms assistive device control, and function-based neuropharmacological assay development and demonstration.
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Browsing Neural Engineering Research by Author "Peixoto, Nathalia"
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Item A Literature Review of Network Models in Neuroscience(2022-05) Kazemi Abharian, Sanaz; Parsa, Maryam; Peixoto, NathaliaNeuroscience is developing in size, scope, and complexity of neural data obtained from large portions of nervous systems. The primary aim of network models in neuroscience is to map, record, and model the components and interactions of the human brain and nervous systems. In this paper, our goal is to survey graph-based network models in neuroscience and study their applications to detect diseases and disorders related to the human brain.Item A Review of the Effects of Microwave Radiation on Spatial Memory and Learning(2022-05) Dockum, Allison; Peixoto, NathaliaMicrowave radiation refers to electromagnetic waves between 300 MHz and 300 GHz. Microwaves are used in communication systems, manufacturing, medical treatments, and military operations. The ability to modify waveform parameters, such as frequency and duty cycle, contribute towards the versatility of microwave radiation. In the same way, the ability to adjust each parameter also contributes to the complexity of understanding the biological effects of microwaves. For some time, researchers have studied the microwave effects on learning and spatial memory in rodents. Rodents provide scientists with a neurologically similar model to humans, which is easy to study both on the cellular levels and to assess behaviorally due to the development of maze performance tests. However, the mechanisms disrupting spatial memory remain largely unknown because of the nearly infinite number of ways microwave can be modified, combined with the multitude of neurological effects, which could impact behavior. It is important for scientists to continue to study rodent models under various microwave exposure conditions to prevent harmful exposure conditions with humans. In this review microwave exposure conditions will be introduced, followed by an introduction to the cruciality of synaptic plasticity to spatial memory and learning. Synaptic plasticity can be impacted through various neurological mechanisms; of which the NMDAR receptors, neurotransmitter release, and activation of intracellular signaling cascades, and cell apoptosis will be reviewed.Item Attempted Prediction of Emotional Valence from EEG Using Multidimensional Directed Information(2022-05) Clayton A Baker; Peixoto, NathaliaQuantitative measurement of a person’s emotional state can aid performance in a number of areas, such as human-machine interactions, and psychological research. Electroencephalogram (EEG) data has shown potential as a predictor of emotional valence based on asymmetric activation patterns between the left and right hemispheres of the prefrontal cortex. Multidimensional directed information (MDI) is a computational tool that allows the measurement of information content transferred between different signals in a connected system, and has previously seen applications in EEG-based affective measurement in order to detect the presence of an emotional response. This study aimed to use MDI with EEG data from published datasets in order to derive a directional bias metric as a predictor for emotional valence based on frontal hemisphere asymmetry. Two methods of MDI computation were attempted; significant differences were observed in results between the two, suggesting possible errors in implementation. Neither method yielded output correlating with valence.Item Development of a low cost Electroencephalogram patient simulator(2022-05) Lancaster, Brandon; Peixoto, NathaliaConducting human subjects research has some challenges for bioengineers such as lack of repeatability of bio-signals, IRB approval and recruitment. The COVID-19 pandemic has made it even more difficult for researchers to access research subjects. One biosignal that is economical, portable and quick to collect data from is Electroencephalography (EEG). Here we present a solution to researchers’ reduction of access to in vivo EEG signals. The solution is in the form of a patient EEG simulator that is controlled by an ATmega328P, connected to an analog to digital converter and other analog circuitry. Other commercial devices exist but this device was also constructed for educational purposes. The output signal can be adjusted to better match a real bio-potential from a human. Neurophysiology recordings can be loaded onto the device for use. Open source data is readily available for download to add to the database on the device. This simulator has other potential applications such as unit testing for devices under development and more convenient and rapid prototype iteration. Optimization of the device is also discussed.Item EEG Analysis to detect Attention(2022-05) Nesseem, Caren; Peixoto, NathaliaItem Gesture Classification from sEMG Signals using Machine Learning Approaches(2022-05-12) Rabbat, Nada; Peixoto, NathaliaThe open-access sEMG dataset [2] is utilized to classify hand motor movements to the respective gesture they represent, using machine learning approaches. This is done as a prospective benchmark for fast and efficient communication in cases of disability, where the gesture class can be replaced with a letter in the alphabet, to form a sentence from a combination of gestures. The Machine Learning approaches tested in this paper are Logistic Regression, Random Forest, and Bagging Classifier algorithms. All approaches will be tested for their accuracy in classifying the sEMG data. The Bagging classification algorithm had the highest accuracy score, followed by Random Forest and Logistic Regression.Item Investigation into the Link between Object Perception and Neural Activity in the Human Brain Using ECoG Data(2022-05) Stenberg, Sabrina; Peixoto, NathaliaOne area under investigation in the field of neuroscience is the link between object perception and neural activity in visual cortical areas of the human brain. By investigating the electrical potentials from the ventral temporal cortical surface in humans, the Stanford University study selected for this paper sought to collect sufficient information for spontaneous and near-instantaneous identification of a subject’s perceptual state. The brain signal data collection technique used by the researchers was electrocorticography (ECoG), using ECoG arrays placed on the subtemporal cortical surface of seven epilepsy patients. ECoG is an invasive electrogram method, requiring access to the surface of the brain, which can be applied to measure brain signals in response to specific stimuli. Using publicly available human ECoG recording data previously collected and made publicly available, this paper investigates visual object processing in the human brain. The data are taken from a study where seven epilepsy patients were shown house and face images in quick succession. We use those data and filter, process, and plot selected data to investigate the correct identification of the stimuli. We discovered that the incorrect stimuli matches are driven by variance in the human brain activity corresponding to the same set of stimuli. Better understanding of the visual processing capabilities of the human brain could lead to developments in machine learning, as well as generate recommendations for future data collection in human visual object processing.Item Mixed Reality Utilization in Anatomical Surgical Practice with HoloLens(2022-05) Montenegro, Fabian; Peixoto, NathaliaSurgeons have demonstrated common issues when in procedures. MR and AR utilization have recently been implemented into the medical field. Headsets like the HoloLens give advantages that are able to benefit both the patient and surgeon. Studies have shown this and provided data to which makes this a desirable change. An early build of this sort of software is being developed which include holograms of organs and characteristics to aid in medical procedures.Item Predicting Finger Movement Using an Ensemble Machine Learning Approach(2022-05) Handjinicolaou, Peter; Peixoto, NathaliaDebilitating brain trauma caused by injury or stroke, and other neurological disorders, can hinder a person’s ability to use their hands. Brain-Computer Interfaces (BCIs) are a subject of great interest regarding augmenting or restoring functionality to victims of this type of trauma. Motor imagery is a process in which a subject under test imagines performing an action without physically doing so [3]. Using brainwave sensors such as EEGs, the state of neural communication for that action can be recorded without the interference caused by the movement associated with it [8]. Using Machine learning classification techniques such as Support Vector Machines (SVMs), Multilayer Perception models (MLPs), and Fischer Linear Discrimination Analysis (LDA), it is possible to select for features and accurately predict upcoming movement by using motor imagery training and EEG data collection.Item Review of different modes of TES used in treatment of neurodegenerative disorders(2022-05) Kaur, Antarjot; Peixoto, NathaliaLiterature ReviewItem The Study of Retinal Organoids: Development, Modeling, and Transplantation(2022-05) Farag, Sally; Peixoto, NathaliaThere are several different retinal diseases prominent within the United States including age-related macular degeneration (AMD), diabetic retinopathy, and glaucoma. These three particular retinal diseases affect a total of 12.5 million people in the US alone, showing that retinal diseases are a very prevalent issue that require an improvement in the methodology of treatment. Retinal diseases can cause blindness and drastically decrease quality of life. To better treat these diseases and provide patients with proper care, it is important to maintain a proper understanding of the retina. It has been found that retinal organoids (ROs) can be produced through the use of stem cells. When perfected, these ROs can provide advanced modeling of the retina, as well as modeling of novel treatments for retinal diseases and allow for better testing. Additionally, ROs can be modified in order to better depict retinal diseases and their microenvironments. They may also be used for retinal transplants as a method of treatment. However, ROs are still being developed and understood. Before the full use and benefits of ROs can be reaped, the differences between ROs and natural retinas must be understood and minimized. Additionally, various issues within the ROs themself must be tackled in order for their uses to become more advanced.Item The Use of Neuroengineering to Model Brain Disease: Alzheimer’s Disease, Parkinson’s Disease, and Schizophrenia(2022-05) Wright, Lacey; Peixoto, NathaliaPredicting, diagnosing, and treating neurological disease is a significant challenge in current medical research. This review will consider computational neuroengineering models of the brain for three specific neurological diseases: Alzheimers disease (AD), Parkinson’s disease (PD), and schizophrenia. These diseases were specifically chosen because they affect similar areas of the brain and involve similar symptoms. The physiology and pathology of each disease will be reviewed, as well as the most promising computational models of these diseases. Computational simulation is expected to help improve the understanding of the relationship between cellular pathology and cognitive performance. Future approaches for diagnosing and treating AD, PD, and schizophrenia will be suggested based on conclusions made from the models’ results.Item Upper Limb Prosthetic Literature Review(2022-05) Philipson, Thomas; Peixoto, NathaliaThe goal of this study is to determine how the needs of those who operate upper limb prosthetics can be met so that the prosthetic retention rate is higher, reflecting an improvement in their quality of life. As we use our extremities to feel and interact with our environment, it is crucial that developments are made specifically in the hands to allow individuals to complete tasks that require high dexterity and precision. Upper limb prosthetic users have a high rate of abandonment due to the lack of ownership and embodiment felt regarding the prosthetic limb [1]. Improved functionality of the prosthetic limb would reduce the load applied to the functional limb, which would lead to better prosthetic retention. Another area for improvement that is addressed in this literature review is the accuracy of the BCI component in signal acquisition and translation to prosthetic automation. Accuracy is important not only for functionality but for a sense of self in the user. A sense of self is vital for those using prosthetic limbs as the goal is for it to be a cohesive part of the user. This review will discuss the different methods for data acquisition and how to improve the accuracy of the resultant movement.