Computational Neuroscience Commons^™

Deep Jansen-Rit Parameter Inference For Model-Driven Analysis Of Brain Activity, Deepa Tilwani, Christian O'Reilly

Faculty Publications

Accurately modeling effective connectivity (EC) is critical for understanding how the brain processes and integrates sensory information. Yet, it remains a formidable challenge due to complex neural dynamics and noisy measurements such as those obtained from the electroencephalogram (EEG). Model-driven EC infers local (within a brain region) and global (between brain regions) EC parameters by fitting a generative model of neural activity onto experimental data. This approach offers a promising route for various applications, including investigating neurodevel- opmental disorders. However, current approaches fail to scale to whole-brain analyses and are highly noise-sensitive. In this work, we employ three deep-learning architectures—a …

Theoretical Analysis Of Cnns For Automatic Seizure Detection In Eeg Signals, Jackson T. Small

Honors Undergraduate Theses

Epilepsy is a common brain disorder where neurons in the brain rapidly fire, causing recurring seizures. The brain activity during a seizure can be detected by electroencephalogram (EEG) signals; however, this process is not only labor-intensive and time-consuming but is also subject to inter-rater variability, with a study showing only moderate agreement when diagnosing patients, even among experts. Convolutional Neural Networks (CNNs) are often proposed to detect seizures automatically, achieving high performance. The focus on performance comes at a cost of losing interpretability, leaving the model as effective but seen as a ’black box’. This thesis confronts the interpretability knowledge …

Voluntary Action And Conscious Intention, Jake Gavenas

Computational and Data Sciences (PhD) Dissertations

Traditional experimental methodology in neuroscience involves comparing responses to different stimuli to make inferences about the human brain. However, human volition entails making decisions and acting in a manner that is underdetermined by the external environment. Investigations of the neuronal basis of volition have led to new controversies regarding the existence of free will, and offer potential directions for medical treatments of disorders such as addiction, akinetic mutism, and locked-in syndrome. However, because volition experiments leave aspects of responses “up to” participants, neuroscientists must utilize computational techniques such as modeling, simulation, and advanced analyses to progress our understanding of human …

Unraveling The Neural Basis Of Emotions: Advancing Understanding With Ecologically Valid Paradigms And High-Resolution Intracranial Eeg, Tiankang Xie

Dartmouth College Ph.D Dissertations

Background

Emotion arises from integrating information about the external world with memories of past experiences, current homeostatic states, and future goals. They play a vital role in regulating our thoughts, feelings and behaviors, significantly impacting our mental health. Thus, it is important to understand the neurobiological mechanisms that give rise to emotions. While there has been considerable work investigating the neural basis of emotions, progress has been hampered by several methodological limitations. For example, prior work has relied on relatively simple and isolated stimuli, which often fail to effectively capture the dynamic and multifaceted nature of emotional experiences in real-life …

The Distinction Of Logical Decision According To The Model Of The Analysis Of Brain Signals (Eeg), Akeel Abdulkareem Al-Sakaa, Zaid H. Nasralla, Mohsin Hasan Hussein, Saif A. Abd, Hazim Alsaqaa, Kesra Nermend, Anna Borawska

Karbala International Journal of Modern Science

Recently, brain signal patterns have been recruited by researchers in different life activities. Researchers have studied each life activity and how brain signal patterns appear. These patterns could then be generalised and used in different disciplines. In this paper, we study the brain state during decision making in a lottery experiment. An EEG device is used to capture brain signals during an experiment to extract the optimal state for logical decision making. After collecting data, extracting useful information and then processing it, the proposed method is able to identify rational decisions from irrational ones with a success rate of 67%.

Performance Of Openbci Eeg Binary Intent Classification With Laryngeal Imagery, Nathan George, Samuel Kuhn

Regis University Faculty Publications (comprehensive list)

One of the greatest goals of neuroscience in recent decades has been to rehabilitate individuals who no longer have a functional relationship between their mind and their body. Although neuroscience has produced technologies which allow the brains of paralyzed patients to accomplish tasks such as spell words or control a motorized wheelchair, these technologies utilize parts of the brain which may not be optimal for simultaneous use. For example, if you needed to look at flashing lights to spell words for communication, it would be difficult to simultaneously look at where you are moving. To improve upon this issue, this …

When The Brain Plays A Game: Neural Responses To Visual Dynamics During Naturalistic Visual Tasks, Jason Ki

Dissertations and Theses

Many day-to-day tasks involve processing of complex visual information in a continuous stream. While much of our knowledge on visual processing has been established from reductionist approaches in lab-controlled settings, very little is known about the processing of complex dynamic stimuli experienced in everyday scenarios. Traditional investigations employ event-related paradigms that involve presentation of simple stimuli at select locations in visual space and discrete moments in time. In contrast, visual stimuli in real-life are highly dynamic, spatially-heterogeneous, and semantically rich. Moreover, traditional experiments impose unnatural task constraints (e.g., inhibited saccades), thus, it is unclear whether theories developed under the reductionist …

Mechanisms Of Value-Biased Prioritization In Fast Sensorimotor Decision Making, Kivilcim Afacan-Seref

Dissertations and Theses

In dynamic environments, split-second sensorimotor decisions must be prioritized according to potential payoffs to maximize overall rewards. The impact of relative value on deliberative perceptual judgments has been examined extensively, but relatively little is known about value-biasing mechanisms in the common situation where physical evidence is strong but the time to act is severely limited. This research examines the behavioral and electrophysiological indices of how value biases split-second perceptual decisions and the possible mechanisms underlying the process. In prominent decision models, a noisy but statistically stationary representation of sensory evidence is integrated over time to an action-triggering bound, and value-biases …

Time Frequency Analysis Of Neural Oscillations In Multi-Attribute Decision-Making, Iris Lieuw

Scripps Senior Theses

In our daily lives, we often make decisions that require the use of self-control, weighing trade-offs between various attributes: for example, selecting a food based on its health rather than its taste. Previous research suggests that re-weighting attributes may rely on selective attention, associated with decreased neural oscillations over posterior brain regions in the alpha (8-12 Hz) frequency range. Here, we utilized the high temporal resolution and whole-brain coverage of electroencephalography (EEG) to test this hypothesis in data collected from hungry human subjects exercising dietary self-control. Prior analysis of this data has found time-locked neural activity associated with each food’s …

Low Cost Neurochairs, Frankie Pike

Master's Theses

Electroencephalography (EEG) was formerly confined to clinical and research settings with the necessary hardware costing thousands of dollars. In the last five years a number of companies have produced simple electroencephalograms, priced below $300 and available direct to consumers. These have stirred the imaginations of enthusiasts and brought the prospects of "thought-controlled" devices ever closer to reality. While these new devices were largely targeted at video games and toys, active research on enabling people suffering from debilitating diseases to control wheelchairs was being pursued. A number of neurochairs have come to fruition offering a truly hands-free mobility solution, but whether …