Spartan Daily, November 15, 1991

Volume 97, Issue 54https://scholarworks.sjsu.edu/spartandaily/8191/thumbnail.jp

SERENITY: THE FUTURE OF COGNITIVE MODULATION FOR THE HYPER ENABLED OPERATOR

In the Special Operations community, cognitive enhancement and resilience is at the forefront of the 2035 Hyper Enabled Operator Program (HEO). The United States Special Operations Command’s vision is to combine cutting-edge communications and data capabilities into a next generation tactical system for the end user. Using algorithms and autonomous systems to enhance the ability to make rational decisions faster can ultimately determine life or death on the battlefield. Over the past several years, cognitive enhancement with the introduction of brain computer interface (BCI) technology has had major breakthroughs in the medical and science fields. This thesis looks to analyze BCI technology for future cognitive dominance and cognitive overmatch in the Hyper Enabled Operator. Machine-assisted cognitive enhancement is not beyond reach for special operations; throughout the research and after multiple interviews with subject matter experts, it has been concluded that interfaces using transcranial alternating current stimulation (tACS), median nerve stimulation (MNS), or several other exploratory procedures have been successful with enhancing cognition and reducing cognitive load. Special Operations should not shy away from transformational innovative technology or wait for commercial or lab-tested solutions. To start, Special Operations should foster avant-garde theories that provide solutions and evolve ideas into unsophisticated prototypes that can be fielded immediately.Major, United States ArmyApproved for public release. Distribution is unlimited

Brain-Computer Interface-Progress and Prospects

Since the advent of Brain-Computer Interface (BCI), this technology has been significantly contributed modern society in many aspects such as medical and informational science. With further approaches in this interdisciplinary technology and based on current research, BCI is considered to be the potential solution to medical or surgical difficulties such as restoration of neurological function or motor abilities. In this article, the current state of BCI development in multiple platforms was briefly introduced. By organizing and analyzing laboratory data from the state-of-the-art BCI research, this article also illustrated the breakthrough on different BCI systems based on the lab data. The multitude of applications and contributions in medical science and engineering of both invasive and non-invasive systems were also discussed with the help of clinical data. Eventually, the potential and future attempts will be projected and inferred based on the present state of such connection in this article. After comparing and contrasting two types of interfaces and analysis, a conclusion could be made that invasive systems will eventually surpass noninvasive methods in more applications areas due to its preponderance of precise control

On Tackling Fundamental Constraints in Brain-Computer Interface Decoding via Deep Neural Networks

A Brain-Computer Interface (BCI) is a system that provides a communication and control medium between human cortical signals and external devices, with the primary aim to assist or to be used by patients who suffer from a neuromuscular disease. Despite significant recent progress in the area of BCI, there are numerous shortcomings associated with decoding Electroencephalography-based BCI signals in real-world environments. These include, but are not limited to, the cumbersome nature of the equipment, complications in collecting large quantities of real-world data, the rigid experimentation protocol and the challenges of accurate signal decoding, especially in making a system work in real-time. Hence, the core purpose of this work is to investigate improving the applicability and usability of BCI systems, whilst preserving signal decoding accuracy. Recent advances in Deep Neural Networks (DNN) provide the possibility for signal processing to automatically learn the best representation of a signal, contributing to improved performance even with a noisy input signal. Subsequently, this thesis focuses on the use of novel DNN-based approaches for tackling some of the key underlying constraints within the area of BCI. For example, recent technological improvements in acquisition hardware have made it possible to eliminate the pre-existing rigid experimentation procedure, albeit resulting in noisier signal capture. However, through the use of a DNN-based model, it is possible to preserve the accuracy of the predictions from the decoded signals. Moreover, this research demonstrates that by leveraging DNN-based image and signal understanding, it is feasible to facilitate real-time BCI applications in a natural environment. Additionally, the capability of DNN to generate realistic synthetic data is shown to be a potential solution in reducing the requirement for costly data collection. Work is also performed in addressing the well-known issues regarding subject bias in BCI models by generating data with reduced subject-specific features. The overall contribution of this thesis is to address the key fundamental limitations of BCI systems. This includes the unyielding traditional experimentation procedure, the mandatory extended calibration stage and sustaining accurate signal decoding in real-time. These limitations lead to a fragile BCI system that is demanding to use and only suited for deployment in a controlled laboratory. Overall contributions of this research aim to improve the robustness of BCI systems and enable new applications for use in the real-world

A Behavioural and Electrophysiological exploration of the Working Memory impairment in Developmental Dyslexia

The current thesis provides a behavioural and electrophysiological exploration of Working Memory (WM) processing in developmental dyslexia. This thesis identifies a debate in the literature regarding the extent to which individuals with dyslexia have a specific phonological WM impairment, or a domain general Central Executive (CE) impairment. Predictions from the latter account suggest that dyslexics should show an impairment in visual, and verbal domains of WM. However, findings in the visual domain have been inconsistent, and research has predominantly focused on children. The experimental work in this thesis examines CE processing in dyslexic adults by assessing the behavioural and ERP responses associated with WM, across 8 experiments. Experiments 1-5 present stimuli in the visual domain, while Experiments 6-8 are conducted in the auditory domain. The results indicate that dyslexics are impaired for verbal information specifically, however subtle RT differences emerge during visual-spatial WM, when participants are required to manipulate information. In order to assess why effects are more robust in the phonological domain, Experiment 8 examines the contribution of auditory perceptual problems and phonological WM processing in dyslexia. The Temporal Sampling Theory of Developmental dyslexia (TSTDD; Goswami, 2011) specifies that dyslexics have a difficulty processing tones with long rise-times. In Experiment 8, dyslexic participants show a WM impairment that is specific to tones with long rise-times. The theoretical implications of these findings are discussed, and a new hypothesis regarding the phonological WM impairment in dyslexia is proposed. The original contribution to knowledge of this thesis are threefold. 1) The ERP responses associated with WM processing in developmental dyslexia are examined across modality, using a range of stimuli. 2) A novel task is used to directly investigate CE processing in dyslexia (Experiment 5). 3) The TSTDD is applied in order to investigate phonological WM in dyslexia

The Neurobiological Development of Reading Fluency

This chapter offers an extensive review of current and foundational research literature on the neurodevelopment of dyslexia and reading fluency worldwide. The impact of different languages and their orthographies on the acquisition of phonological analysis and orthographical features by beginning readers is explored. Contributions from the Psycholinguistic Grain Size Theory and new assessments, i.e. rapid automatized naming, have focused and advanced the understanding of slow phonological and visual processing skills. Recently, the development of new definitions of fluency has led to a proposed continuum of automatized decoding and processing skills required for students of English. Computer technology has enhanced the use of visual hemisphere-specific stimulation to affect the neurodevelopment of efficient word retrieval pathways and to increase reading speed. Processes for subtyping students based on reading behaviors and then stimulating a particular hemisphere of the brain with the fast presentation of words and phrases have been found to change levels of activation in key brain locations and increase the fluent processing of connected text. Newer technologies such as diffusion tensor imaging, while somewhat suspect, may provide the evidence that ultimately will document the changes in communication between regions of interest regulating the automaticity of brain functions in reading

Functional Magnetic Resonance Imaging

"Functional Magnetic Resonance Imaging - Advanced Neuroimaging Applications" is a concise book on applied methods of fMRI used in assessment of cognitive functions in brain and neuropsychological evaluation using motor-sensory activities, language, orthographic disabilities in children. The book will serve the purpose of applied neuropsychological evaluation methods in neuropsychological research projects, as well as relatively experienced psychologists and neuroscientists. Chapters are arranged in the order of basic concepts of fMRI and physiological basis of fMRI after event-related stimulus in first two chapters followed by new concepts of fMRI applied in constraint-induced movement therapy; reliability analysis; refractory SMA epilepsy; consciousness states; rule-guided behavioral analysis; orthographic frequency neighbor analysis for phonological activation; and quantitative multimodal spectroscopic fMRI to evaluate different neuropsychological states

Graphonomics and your Brain on Art, Creativity and Innovation : Proceedings of the 19th International Graphonomics Conference (IGS 2019 – Your Brain on Art)

[Italiano]: “Grafonomia e cervello su arte, creatività e innovazione”. Un forum internazionale per discutere sui recenti progressi nell'interazione tra arti creative, neuroscienze, ingegneria, comunicazione, tecnologia, industria, istruzione, design, applicazioni forensi e mediche. I contributi hanno esaminato lo stato dell'arte, identificando sfide e opportunità, e hanno delineato le possibili linee di sviluppo di questo settore di ricerca. I temi affrontati includono: strategie integrate per la comprensione dei sistemi neurali, affettivi e cognitivi in ambienti realistici e complessi; individualità e differenziazione dal punto di vista neurale e comportamentale; neuroaesthetics (uso delle neuroscienze per spiegare e comprendere le esperienze estetiche a livello neurologico); creatività e innovazione; neuro-ingegneria e arte ispirata dal cervello, creatività e uso di dispositivi di mobile brain-body imaging (MoBI) indossabili; terapia basata su arte creativa; apprendimento informale; formazione; applicazioni forensi. / [English]: “Graphonomics and your brain on art, creativity and innovation”. A single track, international forum for discussion on recent advances at the intersection of the creative arts, neuroscience, engineering, media, technology, industry, education, design, forensics, and medicine. The contributions reviewed the state of the art, identified challenges and opportunities and created a roadmap for the field of graphonomics and your brain on art. The topics addressed include: integrative strategies for understanding neural, affective and cognitive systems in realistic, complex environments; neural and behavioral individuality and variation; neuroaesthetics (the use of neuroscience to explain and understand the aesthetic experiences at the neurological level); creativity and innovation; neuroengineering and brain-inspired art, creative concepts and wearable mobile brain-body imaging (MoBI) designs; creative art therapy; informal learning; education; forensics

Lancet Neurol

The diagnosis of amyotrophic lateral sclerosis can be challenging due to its heterogeneity in clinical presentation and overlap with other neurological disorders. Diagnosis early in the disease course can improve outcomes as timely interventions can slow disease progression. An evolving awareness of disease genotypes and phenotypes and new diagnostic criteria, such as the recent Gold Coast criteria, could expedite diagnosis. Improved prognosis, such as that achieved with the survival model from the European Network for the Cure of ALS, could inform the patient and their family about disease course and improve end-of-life planning. Novel staging and scoring systems can help monitor disease progression and might potentially serve as clinical trial outcomes. Lastly, new tools, such as fluid biomarkers, imaging modalities, and neuromuscular electrophysiological measurements, might increase diagnostic and prognostic accuracy.R01 TS000327/TS/ATSDR CDC HHSUnited States/K23 ES027221/ES/NIEHS NIH HHSUnited States/MR/L501529/1/MRC_/Medical Research CouncilUnited Kingdom/R01 ES030049/ES/NIEHS NIH HHSUnited States/R01 NS120926/NS/NINDS NIH HHSUnited States/R01 NS127188/NS/NINDS NIH HHSUnited States/MR/R024804/1/MRC_/Medical Research CouncilUnited Kingdom/R01 TS000289/TS/ATSDR CDC HHSUnited States