Neural Engineering Student Projects
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These projects were created as part of the graduate level neural engineering course which focuses on current topics in neural engineering. Examples include brain-machine interfaces, neurophysiological methods, instrumentation for interfacing electronics to the nervous system (in vivo and in vitro), optogenetics, ultrasound interfaces (for stimulation and recording in the nervous system), neural modeling, and prosthesis design.
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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 Literature Review of the Development Trends of Visual Neural Protheses(2021) Givens, JordanAccording to a systemic review of population-based data sets relevant to global vision impairment and or blindness between 1980 in 2015, there is an estimated 36 million people who suffer from blindness [4]. An additional 405.1 million people with mild to severe visual impairment [4]. Visual impairment of any degree and its growing prevalence are not a new issue. However, recent advancements in neural protheses, such as cochlear implants that aid those who are hearing impaired, leading researchers to turn to visual neural protheses. Visual neural prostheses focus on the concept of artificially inducing vision by using our current understanding of electrical stimulation, visual pathways, and visual sensations. Therefore, all visual protheses focus on creating an artificial sense of vision through the electrical activation of neurons belonging to the visual system of the body [2]. There are a variety of approaches researchers have taken to accomplish this, the approaches differ in the aspects of the visual system they attempt to replace. As of 2019 they are primarily four approaches that encompass most neural prosthetics. Those that focus on the retina, optical nerve, cortical region of the brain, and or the lateral geniculate nucleus (LGN) within the thalamus [2][3]. The most prominent of which is the retina neural prosthetic, having multiple current implants such as the Argus II electronic epiretinal device, this is due to its extracranial location and simpler organization compared to other methods [3][5]. However, the retinal approach is not without faults of its own such as unwanted electrochemical reactions and low resolution. Therefore, in this paper the four approaches of visual neural prostheses will be examined to provide a greater insight into the field.Item A machine learning approach to predict rtms therapy response in major depressive disorder(2020-05) Shams, MohammadMachine learning techniques have been utilized to predict the outcome of repetitive transcranial magnetic stimulation (rTMS) treatment in depression, e.g., through classifying the responders (R) and non-responders (NR) to rTMS treatment for major depression disorder (MDD) patients. MDD is among the leading causes of disability in the world with affecting more than 260 million people, and a major contributor to the overall global burden of disease. In this study, the outputs of the Local Subset Feature Selection (LSFS) method were used by an SVM classifier to evaluate the capability of the proposed method in the prediction of rTMS treatment response in depression cases. A Leave-One-Out cross-validation method is applied to the input data to evaluate the performance of the response classification. The achieved accuracy, sensitivity, and specificity were 89.5%, 90%, and 87%, respectively. The main restriction of this study that would limit its usage in clinical applications is the small sample size.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 Biophysical Neuromodulation: An Integrative Approach(2020) Bush, JoshuaThe brain remains the key to our experiential reality. From sensory perception to an internal expanse the brain remains the hardware and processing unit. The age in which we can precisely alter the hardware with which we process and create experience is dawning. From the advent of optogenetics to the developing use of transcranial magnetic stimulation (TMS), we are rapidly expanding our control over our brains. It is essential that the evolution of our understanding of the brain maintains a reasonable pace with the technological breakthroughs and human urgency. In this pursuit, we explore the emerging methods for biophysical neuromodulation and the materials through which these methods can be enhanced and/or used to develop translational in vitro models. The methods involved encompass thermal, electrical, magnetic, optical, and ultrasonic modulation of neurons and neuronal networks. A variety of materials such as conductive polymers, graphene, and optoelectronic semi-conductors have been utilized to harness these physical stimuli in the study of neuromodulation. Materials continue to emerge with greater precision and control over the matrix mechanical properties, conductivity, ligand densities, and nano-architecture. With environmental control, real-time physical neuromodulation, and evolving multiplexed sensing capabilities, the rate at which we can further our comprehensive understanding of neuronal information encoding expands rapidly. This review aims to provide a cohesive overview of the maturing coupling between biomaterials and biophysical neuromodulation.Item Comparing Muscle Spindle Afferent Models(Teknos, 2020) Hachem, StephanieProprioception, the internal sense of where your body parts are relative to each other, is essential for many, particularly bimanual, daily activities. Unfortunately, because modern prosthetics lacks this sense it is often difficult or impossible to perform hand-eye coordination tasks with them, and thus upper extremity prosthetics can become a nuisance or burden amputees frequently abandon at home (Biddiss & Chau, 2007). Towards creating a naturally-functional prosthetic able to provide proprioception, this project aims to compare computational models of muscle spindle afferents, using experimental data, in hopes that the best computational model could later be used to predict what voltages should be provided to which afferent nerves in a residual limb.Two muscle spindle models were compared using experimentally-measured afferent and muscle length data digitally extracted from figures in 10 articles. Data was from cats and humans. Both models implement the same formulas in either MATLAB or Python, take muscle length as an input, and can provide primary and/or secondary afferent output. Comparing the experimentally-measured and the predicted afferents, the more recent, Python model was found to provide more accurate afferent output.Item Detection of X-Linked Juvenile Retinoschisis(2021) Zhang, JeremyA child’s education during adolescence is essential for mental and confidence development. As one is exposed to a constantly changing learning environment, unaccounted factors are extremely detrimental. One such factor is X-Linked Juvenile Retinoschisis (XLRS). In general, retinoschisis is a condition in which cysts form within the layers of the retina, causing separation of said layers [1]. Photoreceptor cells within the inner layer are permanently damaged, which ensures near complete blindness. XLRS is a genetic disease that affects an estimated 1 in 5000 males during (pre)adolescence. It causes mutation of the RS1 gene, responsible for creating a retina developmental protein named retinoschisin [2]. The absence of retinoschisin results in an underdeveloped outer layer, causing light to pass through, thus forming the cysts. The disease is incurable, and common visual enhancements cannot be applied to improve conditions [2]. Currently, detection followed by proper accommodation is the only viable prevention method. Such detection methods are genetic testing for the RS1 gene mutation, ocular echography (ultrasound), electroretinography (ERG), and optical coherence tomography (OCT). In this paper, we will mainly discuss ERG and OCT [3][4]. The initial diagnostic tool was electroretinography (ERG), in which a darkness adapted retina is exposed to flashes of light [3]. The photoreceptor cells produce an electrical potential that is measured. The aspects of these measurements will be discussed in this paper. As for recent advancements, OCT is the current method for commercial use, and subsequently generates high resolution 2D or 3D images [4]. This method involves utilizing long wavelength light to penetrate organic tissue. The light contacting the desired scan area is reflected and is processed through a signal receiver, while the other light waves are scattered [4]. In this research paper, we will review the various detection methods along with the complications these methods have on XLRS. Relevant information and data will be obtained and complied to determine the most viable methods, as well as the advantages and disadvantages of each. As XLRS is currently incurable and unpreventable, the importance of precise detection and observation is significant for the betterment and accommodation for those affected.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 DNA nanotechnology in neural engineering: A perspective(2020-05) Vargas, MerlynDNA nanotechnology has great potential as a platform to enhance neural engineering approaches. DNA based nanoparticles are biocompatible and easy to functionalize [1]. Peptides or proteins can be conjugated to DNA nanoparticles to target specific cells and tissues [2], [3], as well as imaging agents to help diagnosis and monitoring purposes [4], [5]. In addition, recent studies have shown the capacity of some DNA based nanoparticles to cross the blood-brain barrier to target brain tumors in rats [4]. Others have shown DNA-peptide nanoparticles that enhanced differentiation of neural stem cell proliferation and neural differentiation [6], as well as new technologies to construct DNA-based molecular circuitry [7]. Besides all of these promising features offered by DNA nanotechnology, DNA has also shown to be a great scaffold for the production of nanoelectronics, giving a realistic perspective of the creation of nano devices that can target a desired tissue or cell and perform as nanochips for diagnosis, sensing or modulatory functions. Therefore, this work reviews some of the characteristics of nanotechnology and DNA-based nanoelectronics that are favorable for the development of nanodevices as neural system probes, as well as some perspectives for this type of technology in the field of neural engineering.Item EEG Analysis to detect Attention(2022-05) Nesseem, Caren; Peixoto, NathaliaItem EEG-Based BCI for Household IoT Control(2021) Dunn, BryceBrain-computer interface (BCI) paradigms have existed for decades to improve communication and technological control. Electroencephalography (EEG) represents one of the most common non-invasive approaches toward recording brain signals in human participants [1]. Many features within EEG signal are used to model BCI including five major types of brain wave frequency bands, autoregressive parameters, and power spectral density values [2]. Processing EEG to produce a desired output demands signal filtration, feature extraction and classification. Approach. Using the Emotiv EPOC+ headset, electrodes placed on the scalp record passive mental activity at centimeter resolution [3]. The EPOC+ represents a low-cost alternative to medical-grade hardware, which may allow the development of this platform to be more accessible to end-users. Filtration and classification methods are applied to distinguish signal frequencies of interest. The recorded EEG signal is used to demonstrate potential for passive control a simple household appliance such as a light fixture. To this end, a participant’s passive EEG is recorded during a series of tasks in varying light settings. In addition to technical challenges, there are practical considerations to overcome, such as variability and subject fatigue. Preliminary results suggest a measurable distinction in mental state between tasks. Significance. Following this line of inquiry, a platform for interfacing with the increasingly ubiquitous internet of things (IoT) may be developed in the future. The potential applications of BCI are myriad and promise to better living conditions by enhancing and supplementing central nervous system output. EEG-based signaling may provide means to greater autonomy and technological accessibility for disabled people and patients with neurological deficits.Item EEG-based Emotion Recognition with Music: A Model and Application(2022-11) Scavotto, ZakariyyaWith the growth of music streaming, both for pleasure and other applications, such as music therapy, being able to understand how music makes someone feel has increased in importance. The goal of this study was twofold: first, create a machine learning model to predict a subject’s emotional response to music; then integrate this trained model into an application that can predict someone’s emotional response based on live data. Using support vector machines (SVMs) as the basis of the machine learning model, a model was trained to recognize the correct emotional response with 64% accuracy, and the model was successfully implemented into a demonstration web application.Item Effects of NGF on PC12 Neurite Growth(2021) Alex, AlbinPheochromocytoma cells, or PC12s, are a cell line used for neuroscience research. Even though these cells are derived from a rat adrenal medulla tumor, these cells mimic and behave similar to neurons. To further model these cells as neurons, PC12 are treated with nerve growth factor, or NGF. This process transforms the PC12s to differentiated PC12 which alters the cells morphologically to increase the number of neurite and length of neurites. Studies have use either 1% of 50 ng/mL or 100 ng/mL in the differentiation media, yet no study has tested varying densities. In this study, PC12 cells were cultured with varying concentration to determine what concentration led to the longest neurites.Item Evaluating the Plausibility of using Brain-to-Brain Communication to Create Neuromuscular Connection for Prosthetic Usage(2022) Asuagbor, AlesazemWhen prosthetics are developed, they are created for traumatic amputees to restore quality of life so they can live as they did before their limb loss. However, for an individual with congenital amputation — an underdeveloped or nonexistent limb from birth — there are not as many options due to many prosthetics relying on the user’s prior experience with the missing limb[1][2]. The purpose of this project is to investigate the possibility of using electroencephalogram (EEG) signals to control a prosthetic hand. Based on this knowledge, a prosthetic will be designed for an individual with a congenital amputation. Use of this device may be difficult for these individuals compared to those with trauma-based amputations because they lack experience with the limb in question. This can result in more difficulty during the rehabilitation process due to the neurological disconnect. This can be counteracted by utilizing brain-to-brain communication. By sending the neurophysiological signals of an able-bodied person to the individual with the amputation they can teach the amputee’s brain these new motor skills. First, the biosignals responsible for the motor functions of the specific limb must be identified and analyzed[1][3]. By determining if the signals for moving one limb are identical to the signals for the other limb, they can be sent via brain-to-brain communication to the patient with the missing limb. Sharing these signals will help them learn how to use the prosthetic, thus making rehabilitation easier. The hypothesis is that using the EEG signals communicated to the congenital amputee via brain-to-brain communication will make the neurological connections needed to make moving the prosthetic easier. The end device would be equivalent to a myoelectric prosthetic controlled by Phantom Limb Syndrome (PLS), except it will utilize learned EEG signals instead of depending solely on PLS[1][2].Item Exploring Two Alternative BCIs for Improving Alzheimer’s Disease Rehabilitation(2021) Beaini, FayezNeurodegenerative diseases affect the nervous system of the body, ultimately disturbing movement and or mental function where Alzheimer's disease (AD) is the most common form [1]. Brain-computer interface (BCI) is a technology that allows for an alternative method to previous rehabilitation treatments that help improve human cognitive or sensory-motor functions [2]. Due to the crippling and progressive effects of AD, it gives way for the unique utilization of BCI technology to be used for rehabilitation. A standard BCI utilizes electroencephalography (EEG) stimuli to monitor brain activity to obtain relevant information [2]. These traditional BCIs, rely heavily on instrumental learning, and the users themselves, to assist and control their brain activation [3]. The purpose of this paper is to explore alternative BCIs, such as motor-imagery and emotion-based, to explain if these two alternative BCIs can help in improving AD rehabilitation.Item 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 Graphene-based nanocomposites used as a suitable biomaterial for neural growth and networking(2020) O'Shea, BaileyA novelty to counter neurodegenerative diseases such as Alzheimer’s or peripheral nerve injury (PNI) has been in high demand for the past century. Alzheimer’s disease is a well-known disorder that causes nerve tissue to progressively degrade with time. Additionally, PNI is refers to an array of conditions that cause peripheral nerve damage; for example, brachial plexus injury and Wallerian degeneration are considered to be PNIs. Current treatments, such as pharmaceutical drugs and surgery, are reported to help manage symptoms caused by these injuries and disorders. Microsurgeries are being utilized to alleviate pain from PNI, however, because human biology is so complex which hinders neuronal regeneration, any damage caused may be permanent. While these procedures are effective, they are reported to lack post-neurorepair recoveries and growth [1]. Thus, the need for a commodity to rejuvenate the damaged neuronal tissue has seen a great deal of attention.Item Interpreting Speech and Sounds from Neural Activity, a Brief Overview(2020) Ryan, AndrewFor people who are mute, or are completely paralyzed, one of the primary problems they have to deal with is being able to communicate. One potential solution to compensate for decreased communication functions is by using a brain-computer interface (BCI). The idea would be to quantify neural activation in the brain that correlates to imagined speech from the patient, and decode that into legible text that can be interpreted by the receiver. Due to the intricacy of speech interpretation, direct access to regions of the brain and individual neurons isrequired. As a result, many tests done on BCI speech interpretation involve using ECoG sensors on epilepsy patients when they are available. Some approaches used to analyze these signals for feature extraction include word based classification, and phoneme based classification. One approach mentioned less in the literature, is if there is a method to pull a sound signal directly from the activated regions of the brain. Advancement of the technology has potential use as a speech replacement for people suffering from paralysis, as well as in prosthetics.Item Investigating Zolpidem for Treatment of Various Neurological Conditions(2021) Stewart, CarsonWhile Neural Engineering has had breakthrough technologies including cochlear implants for hearing restoration and deep brain stimulation for a range of diseases including Parkinson’s, engineering approaches to several neurological disorders following traumatic brain injury (TBI) have been largely unsuccessful [1]. The Brain is extraordinarily complex, and despite decades of research, remains poorly understood [1][3]. The pharmaceutical Zolpidem originally prescribed for sleep disorders, has gained attention due to its ability to paradoxically increase alertness in patients with TBI, temporarily reduce the symptoms of Parkinson’s disease, and even transiently induce brief periods of consciousness in comatose patients. Zolpidem has been shown to manifest dramatic differences in mechanistic activity between healthy patients and patients with disorders of consciousness (DOC) or TBI. Only small subsets of patients with TBI’s respond to this form of pharmaceutical intervention. It is a lack of understanding of this phenomenon that is causing schisms between researching its causal mechanisms of neural activity and leveraging neural engineering to treat targeted patients [2][3]. Due to EEG, MEG, and MRI comparisons, advances for neuromodulation using Zolpidem have been made to target specific disorders and TBI’s. This review hopes to examine how Zolpidem is being investigated using neuromodulation to explain the vast differences in patient recovery following severe brain injuries and diseases.
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