699 research outputs found

    Independent Component Analysis for Source Localization of EEG Sleep Spindle Components

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    Sleep spindles are bursts of sleep electroencephalogram (EEG) quasirhythmic activity within the frequency band of 11–16 Hz, characterized by progressively increasing, then gradually decreasing amplitude. The purpose of the present study was to process sleep spindles with Independent Component Analysis (ICA) in order to investigate the possibility of extracting, through visual analysis of the spindle EEG and visual selection of Independent Components (ICs), spindle “components” (SCs) corresponding to separate EEG activity patterns during a spindle, and to investigate the intracranial current sources underlying these SCs. Current source analysis using Low-Resolution Brain Electromagnetic Tomography (LORETA) was applied to the original and the ICA-reconstructed EEGs. Results indicated that SCs can be extracted by reconstructing the EEG through back-projection of separate groups of ICs, based on a temporal and spectral analysis of ICs. The intracranial current sources related to the SCs were found to be spatially stable during the time evolution of the sleep spindles

    A Python-based Brain-Computer Interface Package for Neural Data Analysis

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    Anowar, Md Hasan, A Python-based Brain-Computer Interface Package for Neural Data Analysis. Master of Science (MS), December, 2020, 70 pp., 4 tables, 23 figures, 74 references. Although a growing amount of research has been dedicated to neural engineering, only a handful of software packages are available for brain signal processing. Popular brain-computer interface packages depend on commercial software products such as MATLAB. Moreover, almost every brain-computer interface software is designed for a specific neuro-biological signal; there is no single Python-based package that supports motor imagery, sleep, and stimulated brain signal analysis. The necessity to introduce a brain-computer interface package that can be a free alternative for commercial software has motivated me to develop a toolbox using the python platform. In this thesis, the structure of MEDUSA, a brain-computer interface toolbox, is presented. The features of the toolbox are demonstrated with publicly available data sources. The MEDUSA toolbox provides a valuable tool to biomedical engineers and computational neuroscience researchers

    Sleep EEG in young people with 22q11.2 deletion syndrome:a cross-sectional study of slow-waves, spindles and correlations with memory and neurodevelopmental symptoms

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    Background:: Young people living with 22q11.2 Deletion Syndrome (22q11.2DS) are at increased risk of schizophrenia, intellectual disability, attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). In common with these conditions, 22q11.2DS is also associated with sleep problems. We investigated whether abnormal sleep or sleep-dependent network activity in 22q11.2DS reflects convergent, early signatures of neural circuit disruption also evident in associated neurodevelopmental conditions. Methods:: In a cross-sectional design, we recorded high-density sleep EEG in young people (6–20 years) with 22q11.2DS (n=28) and their unaffected siblings (n=17), quantifying associations between sleep architecture, EEG oscillations (spindles and slow waves) and psychiatric symptoms. We also measured performance on a memory task before and after sleep. Results:: 22q11.2DS was associated with significant alterations in sleep architecture, including a greater proportion of N3 sleep and lower proportions of N1 and REM sleep than in siblings. During sleep, deletion carriers showed broadband increases in EEG power with increased slow-wave and spindle amplitudes, increased spindle frequency and density, and stronger coupling between spindles and slow-waves. Spindle and slow-wave amplitudes correlated positively with overnight memory in controls, but negatively in 22q11.2DS. Mediation analyses indicated that genotype effects on anxiety, ADHD and ASD were partially mediated by sleep EEG measures. Conclusions:: This study provides a detailed description of sleep neurophysiology in 22q11.2DS, highlighting alterations in EEG signatures of sleep which have been previously linked to neurodevelopment, some of which were associated with psychiatric symptoms. Sleep EEG features may therefore reflect delayed or compromised neurodevelopmental processes in 22q11.2DS, which could inform our understanding of the neurobiology of this condition and be biomarkers for neuropsychiatric disorders. Funding:: This research was funded by a Lilly Innovation Fellowship Award (UB), the National Institute of Mental Health (NIMH 5UO1MH101724; MvdB), a Wellcome Trust Institutional Strategic Support Fund (ISSF) award (MvdB), the Waterloo Foundation (918-1234; MvdB), the Baily Thomas Charitable Fund (2315/1; MvdB), MRC grant Intellectual Disability and Mental Health: Assessing Genomic Impact on Neurodevelopment (IMAGINE) (MR/L011166/1; JH, MvdB and MO), MRC grant Intellectual Disability and Mental Health: Assessing Genomic Impact on Neurodevelopment 2 (IMAGINE-2) (MR/T033045/1; MvdB, JH and MO); Wellcome Trust Strategic Award ‘Defining Endophenotypes From Integrated Neurosciences’ Wellcome Trust (100202/Z/12/Z MO, JH). NAD was supported by a National Institute for Health Research Academic Clinical Fellowship in Mental Health and MWJ by a Wellcome Trust Senior Research Fellowship in Basic Biomedical Science (202810/Z/16/Z). CE and HAM were supported by Medical Research Council Doctoral Training Grants (C.B.E. 1644194, H.A.M MR/K501347/1). HMM and UB were employed by Eli Lilly & Co during the study; HMM is currently an employee of Boehringer Ingelheim Pharma GmbH & Co KG. The views and opinions expressed are those of the author(s), and not necessarily those of the NHS, the NIHR or the Department of Health funders

    Knowledge-based identification of sleep stages based on two forehead electroencephalogram channels

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    © 2014 Huang, Lin, Ko, Liu, Su and Lin. Sleep quality is important, especially given the considerable number of sleep-related pathologies. The distribution of sleep stages is a highly effective and objective way of quantifying sleep quality. As a standard multi-channel recording used in the study of sleep, polysomnography (PSG) is a widely used diagnostic scheme in sleep medicine. However, the standard process of sleep clinical test, including PSG recording and manual scoring, is complex, uncomfortable, and time-consuming. This process is difficult to implement when taking the whole PSG measurements at home for general healthcare purposes. This work presents a novel sleep stage classification system, based on features from the two forehead EEG channels FP1 and FP2. By recording EEG from forehead, where there is no hair, the proposed system can monitor physiological changes during sleep in a more practical way than previous systems. Through a headband or self-adhesive technology, the necessary sensors can be applied easily by users at home. Analysis results demonstrate that classification performance of the proposed system overcomes the individual differences between different participants in terms of automatically classifying sleep stages. Additionally, the proposed sleep stage classification system can identify kernel sleep features extracted from forehead EEG, which are closely related with sleep clinician's expert knowledge. Moreover, forehead EEG features are classified into five sleep stages by using the relevance vector machine. In a leave-one-subject-out cross validation analysis, we found our system to correctly classify five sleep stages at an average accuracy of 76.7 ± 4.0 (SD) % [average kappa 0.68 ± 0.06 (SD)]. Importantly, the proposed sleep stage classification system using forehead EEG features is a viable alternative for measuring EEG signals at home easily and conveniently to evaluate sleep quality reliably, ultimately improving public healthcare

    Thalamocortical Oscillations in Sleep and Anaesthesia

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    The last 20 years have seen a substantial advancement in the understanding of the molecular targets of general anaesthetics however the neural mechanisms involved in causing loss of consciousness remain poorly understood. Thalamocortical oscillations are present in natural sleep and are induced by many general anaesthetics suggesting that modulation of this reciprocal system may be involved in the regulation of consciousness. Dynamic changes of thalamocortical oscillations in natural sleep and anaesthesia were investigated in rats chronically implanted with skull screw and depth electrodes in the cortex and thalamus. The hypothesis that discrete areas within the thalamus are responsible for regulation of arousal was tested. The anaesthetics propofol and dexmedetomidine but not midazolam produced switches in delta frequency at loss of righting reflex (LORR). This switch in frequency mirrored that seen within non-rapid eye movement sleep (NREM), whereas the onset of NREM was characterized by a switch from theta to delta in the EEG. Depth recordings during NREM indicated that the switch into a NREM state occurred in the central medial thalamus (CMT) significantly before the cingulate, barrel cortex and ventrobasal nucleus (VB), and that the CMT switch corresponded to the switch seen in the global EEG. Dexmedetomidine hypnosis showed a delta frequency shift that occurred simultaneously within the thalamus and cortex, and furthermore that the thalamus exhibited phase advancement over the cortex at the point of LORR. In conclusion, globalised changes within the thalamocortical system occur for propofol and dexmedetomidine LORR in the rat. This change represents a transition within drug free NREM and may implicate a common pathway responsible for a decrease in arousal. Furthermore, the phase advancement of the intralaminar thalamus over the cortex at LORR suggests a crucial role for this part of the thalamocortical system for regulating consciousness

    A Unique Pattern of Sleep Structure is Found to be Identical at all Cortical Sites: a Neurobiological Interpretation

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    There is substantial evidence both at the cellular and at the electroencephalogram (EEG) level to support the view that the brainstem activating systems control the sleep-state (stage) progression over time that constitutes the overall sleep structure as seen at the EEG. We argue here that the brainstem therefore modulates the time-courses of spectral power in the different EEG frequency bands. These show during non-rapid eye movement (NREM) sleep a very particular interrelationship the origin of which has received little attention and for which the neuronal transition probability model for sleep structure has proposed a physiological explanation. We advance the hypothesis that if the brainstem is modulating these time-courses then they should show a marked similarity in shape and timing at all sites. Using data from 10 healthy subjects, we measure the degree of similarity of the time-courses over each of the first four NREM episodes at the frontal, central and parietal sites, for each of the frequency bands beta, sigma and delta, and also the cortically generated slow oscillation. All the cross- correlation coefficients are high and statistically significant, indicating that the shape and timing of these time-courses are practically identical at different sites despite regional differences in their average power levels. These results tend to suggest that two processes may operate concurrently: the brainstem controls the shape and timing of the power time-courses while cortical-thalamic interaction controls their site-dependent average powe

    Unveil Sleep Spindles with Concentration of Frequency and Time

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    Objective: Sleep spindles contain crucial brain dynamics information. We introduce the novel non-linear time-frequency analysis tool 'Concentration of Frequency and Time' (ConceFT) to create an interpretable automated algorithm for sleep spindle annotation in EEG data and to measure spindle instantaneous frequencies (IFs). Methods: ConceFT effectively reduces stochastic EEG influence, enhancing spindle visibility in the time-frequency representation. Our automated spindle detection algorithm, ConceFT-Spindle (ConceFT-S), is compared to A7 (non-deep learning) and SUMO (deep learning) using Dream and MASS benchmark databases. We also quantify spindle IF dynamics. Results: ConceFT-S achieves F1 scores of 0.749 in Dream and 0.786 in MASS, which is equivalent to or surpass A7 and SUMO with statistical significance. We reveal that spindle IF is generally nonlinear. Conclusion: ConceFT offers an accurate, interpretable EEG-based sleep spindle detection algorithm and enables spindle IF quantification.Comment: 22 pages, 6 figures, and 1 tabl
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