Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 143

This supplement to Aerospace Medicine and Biology (NASA SP-7011) lists 251 reports, articles and other documents announced during June 1975 in Scientific and Technical Aerospace Reports (STAR) or in International Aerospace Abstracts (IAA). The first issue of the bibliography was published in July 1964; since that time, monthly supplements have been issued. In its subject coverage, Aerospace Medicine and Biology concentrates on the biological, physiological, and environmental effects to which man is subjected during and following simulated or actual flight in the earth's atmosphere or in interplanetary space. References describing similar effects of biological organisms of lower order are also included. Such related topics as sanitary problems, pharmacology, toxicology, safety and survival, life support systems, exobiology, and personnel factors receive appropriate attention. In general, emphasis is placed on applied research, but references to fundamental studies and theoretical principles related to experimental development also qualify for inclusion

Sleep Stage Classification: A Deep Learning Approach

Sleep occupies significant part of human life. The diagnoses of sleep related disorders are of great importance. To record specific physical and electrical activities of the brain and body, a multi-parameter test, called polysomnography (PSG), is normally used. The visual process of sleep stage classification is time consuming, subjective and costly. To improve the accuracy and efficiency of the sleep stage classification, automatic classification algorithms were developed. In this research work, we focused on pre-processing (filtering boundaries and de-noising algorithms) and classification steps of automatic sleep stage classification. The main motivation for this work was to develop a pre-processing and classification framework to clean the input EEG signal without manipulating the original data thus enhancing the learning stage of deep learning classifiers. For pre-processing EEG signals, a lossless adaptive artefact removal method was proposed. Rather than other works that used artificial noise, we used real EEG data contaminated with EOG and EMG for evaluating the proposed method. The proposed adaptive algorithm led to a significant enhancement in the overall classification accuracy. In the classification area, we evaluated the performance of the most common sleep stage classifiers using a comprehensive set of features extracted from PSG signals. Considering the challenges and limitations of conventional methods, we proposed two deep learning-based methods for classification of sleep stages based on Stacked Sparse AutoEncoder (SSAE) and Convolutional Neural Network (CNN). The proposed methods performed more efficiently by eliminating the need for conventional feature selection and feature extraction steps respectively. Moreover, although our systems were trained with lower number of samples compared to the similar studies, they were able to achieve state of art accuracy and higher overall sensitivity

Decoding Neural Signals with Computational Models: A Systematic Review of Invasive BMI

There are significant milestones in modern human's civilization in which mankind stepped into a different level of life with a new spectrum of possibilities and comfort. From fire-lighting technology and wheeled wagons to writing, electricity and the Internet, each one changed our lives dramatically. In this paper, we take a deep look into the invasive Brain Machine Interface (BMI), an ambitious and cutting-edge technology which has the potential to be another important milestone in human civilization. Not only beneficial for patients with severe medical conditions, the invasive BMI technology can significantly impact different technologies and almost every aspect of human's life. We review the biological and engineering concepts that underpin the implementation of BMI applications. There are various essential techniques that are necessary for making invasive BMI applications a reality. We review these through providing an analysis of (i) possible applications of invasive BMI technology, (ii) the methods and devices for detecting and decoding brain signals, as well as (iii) possible options for stimulating signals into human's brain. Finally, we discuss the challenges and opportunities of invasive BMI for further development in the area.Comment: 51 pages, 14 figures, review articl

Enhancing memory-related sleep spindles through learning and electrical brain stimulation

Sleep has been strongly implicated in mediating memory consolidation through hippocampal-neocortical communication. Evidence suggests offline processing of encoded information in the brain during slow wave sleep (SWS), specifically during slow oscillations and spindles. In this work, we used active exploration and learning tasks to study post-experience sleep spindle density changes in rats. Experiences lead to subsequent changes in sleep spindles, but the strength and timing of the effect was task-dependent. Brain stimulation in humans and rats have been shown to enhance memory consolidation. However, the exact stimulation parameters which lead to the strongest memory enhancement have not been fully explored. We tested the efficacy of both cortical sinusoidal direct current stimulation and intracortical pulse stimulation to enhance slow oscillations and spindle density. Pulse stimulation reliably evoked state-dependent slow oscillations and spindles during SWS with increased hippocampal ripple-spindle coupling, demonstrating potential in memory enhancement

AFIT School of Engineering Contributions to Air Force Research and Technology Calendar Year 1973

This report contains abstracts of Master of Science Theses, Doctoral dissertations, and selected faculty publications completed during the 1973 calendar year at the School of Engineering, Air Force Institute of Technology, at Wright-Patterson Air Force Base, Ohio

Automatic sleep staging based on classifícation methods

Dissertação de Mestrado em Engenharia Biomédica apresentada à Faculdade de Ciências e Tecnologia da Universidade de CoimbraDuring the sleep the brain generates different types of waves depending on the brain stage. To characterise these brain states, the two structures exist: the macrostructure and microstructure. The macrostructure is composed by five sleep stages (designated by N3, N2, N1, REM, W) whose classification is based on the present wave types. The microstructure is characterised by transitional states and the Cyclic Alternating Pattern (CAP) is an example of it. CAP is a periodic cerebral activity prevalent during NREM sleep-stage and composed by A-phases (A1, A2 or A3) and B-phases. The visual scoring of macro- and microstructure are important elements for the diagnosis and prognosis of some diseases. Although this task of both is a time consuming process, which demands automatic scoring. This thesis proposes different classifications methods (discriminate classifiers, kNN and SVM) to detect automatically the sleep stages and A-phases. The classifiers are validated with a dataset that comprise 30 patients. For sleep stages the better model is SVM which obtained an accuracy off 72%, the sensitivities for the each sleep stage are 62%, 54%, 73%, 83% and 69% for W, N1, N2, N3 and REM stages. Regarding the CAP staging the best classifier method is also SVM with an accuracy of 71%, the sensitivities are 76%, 58%, 44% and 24% for B, A1, A2 and A3, respectively. The prediction of A-phases with the SVM yield the best results to date.Durante o sono o c´erebro gera diferentes tipos de ondas, dependo do estado em que se encontra. Para caracterizar estes estados cerebrais existem duas estruturas: a macroestrutura e microestrutura. A macroestrutura ´e composta por cinco diferentes estados de sono (designados por N3, N2, N1, REM, W), cuja classifica¸c˜ao ´e baseada de acordo com o tipo de onda gerado. A microestrutura ´e caracterizada por estados transicionais, sendo um exemplo o Padr˜ao C´ıclico Alternante (CAP). O CAP ´e uma actividade cerebral peri´odica prevalente durando o estado de sono NREM e composto por fases A (A1, A2 e A3) e fases B. O estadiamento visual da macro e microestrutura s˜ao importante para o diagn´ostico e progn´ostico de algumas doen¸cas. Contudo, esta tarefa ´e um processo bastante demorado para ambas as estruturas, o que gera uma necessidade de um estadiamento autom´atico. Esta tese prop˜oes diferentes m´etodos de classifica¸c˜ao (classificadores discriminantes, k-NN e SVM) para detectar automaticamente os diferentes estados de sono e as fases A. Estes classificadores s˜ao validados com uma amostra composta por 30 pacientes. Para os estados de sono o melhor modelo ´e o SVM que obt´em uma taxa de sucesso de 72% e de sensibilidades para cada estado de sono W, N1, N2, N3 e REM de 62%, 54%, 73%, 83% e 69%, respectivamente. Quanto ao estadiamento do CAP o melhor m´etodo de classifica¸c˜ao ´e tamb´em o SVM com uma taxa de sucesso de 71% e sensibilidades de 76%, 58%, 44% e 24% para as fases B, A1, A2 e A3. As sensibilidades obtidas por este ´ultimo m´etodo s˜ao muito acima das encontradas na literatura at´e `a data

AFIT School of Engineering Contributions to Air Force Research and Technology Calendar Year 1973

This report contains abstracts of Master of Science Theses, Doctoral dissertations, and selected faculty publications completed during the 1973 calendar year at the School of Engineering, Air Force Institute of Technology, at Wright-Patterson Air Force Base, Ohio

Efficiency evaluation of external environments control using bio-signals

There are many types of bio-signals with various control application prospects. This dissertation regards possible application domain of electroencephalographic signal. The implementation of EEG signals, as a source of information used for control of external devices, became recently a growing concern in the scientific world. Application of electroencephalographic signals in Brain-Computer Interfaces (BCI) (variant of Human-Computer Interfaces (HCI)) as an implement, which enables direct and fast communication between the human brain and an external device, has become recently very popular. Currently available on the market, BCI solutions require complex signal processing methodology, which results in the need of an expensive equipment with high computing power. In this work, a study on using various types of EEG equipment in order to apply the most appropriate one was conducted. The analysis of EEG signals is very complex due to the presence of various internal and external artifacts. The signals are also sensitive to disturbances and non-stochastic, what makes the analysis a complicated task. The research was performed on customised (built by the author of this dissertation) equipment, on professional medical device and on Emotiv EPOC headset. This work concentrated on application of an inexpensive, easy to use, Emotiv EPOC headset as a tool for gaining EEG signals. The project also involved application of embedded system platform - TS-7260. That solution caused limits in choosing an appropriate signal processing method, as embedded platforms characterise with a little efficiency and low computing power. That aspect was the most challenging part of the whole work. Implementation of the embedded platform enables to extend the possible future application of the proposed BCI. It also gives more flexibility, as the platform is able to simulate various environments. The study did not involve the use of traditional statistical or complex signal processing methods. The novelty of the solution relied on implementation of the basic mathematical operations. The efficiency of this method was also presented in this dissertation. Another important aspect of the conducted study is that the research was carried out not only in a laboratory, but also in an environment reflecting real-life conditions. The results proved efficiency and suitability of the implementation of the proposed solution in real-life environments. The further study will focus on improvement of the signal-processing method and application of other bio-signals - in order to extend the possible applicability and ameliorate its effectiveness