A Generalized Framework for Quantifying the Dynamics of EEG Event-Related Desynchronization

Brains were built by evolution to react swiftly to environmental challenges. Thus, sensory stimuli must be processed ad hoc, i.e., independent—to a large extent—from the momentary brain state incidentally prevailing during stimulus occurrence. Accordingly, computational neuroscience strives to model the robust processing of stimuli in the presence of dynamical cortical states. A pivotal feature of ongoing brain activity is the regional predominance of EEG eigenrhythms, such as the occipital alpha or the pericentral mu rhythm, both peaking spectrally at 10 Hz. Here, we establish a novel generalized concept to measure event-related desynchronization (ERD), which allows one to model neural oscillatory dynamics also in the presence of dynamical cortical states. Specifically, we demonstrate that a somatosensory stimulus causes a stereotypic sequence of first an ERD and then an ensuing amplitude overshoot (event-related synchronization), which at a dynamical cortical state becomes evident only if the natural relaxation dynamics of unperturbed EEG rhythms is utilized as reference dynamics. Moreover, this computational approach also encompasses the more general notion of a “conditional ERD,” through which candidate explanatory variables can be scrutinized with regard to their possible impact on a particular oscillatory dynamics under study. Thus, the generalized ERD represents a powerful novel analysis tool for extending our understanding of inter-trial variability of evoked responses and therefore the robust processing of environmental stimuli

Safety and efficacy of the ChAdOx1 nCoV-19 vaccine (AZD1222) against SARS-CoV-2: an interim analysis of four randomised controlled trials in Brazil, South Africa, and the UK

Background A safe and efficacious vaccine against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), if deployed with high coverage, could contribute to the control of the COVID-19 pandemic. We evaluated the safety and efficacy of the ChAdOx1 nCoV-19 vaccine in a pooled interim analysis of four trials. Methods This analysis includes data from four ongoing blinded, randomised, controlled trials done across the UK, Brazil, and South Africa. Participants aged 18 years and older were randomly assigned (1:1) to ChAdOx1 nCoV-19 vaccine or control (meningococcal group A, C, W, and Y conjugate vaccine or saline). Participants in the ChAdOx1 nCoV-19 group received two doses containing 5 × 1010 viral particles (standard dose; SD/SD cohort); a subset in the UK trial received a half dose as their first dose (low dose) and a standard dose as their second dose (LD/SD cohort). The primary efficacy analysis included symptomatic COVID-19 in seronegative participants with a nucleic acid amplification test-positive swab more than 14 days after a second dose of vaccine. Participants were analysed according to treatment received, with data cutoff on Nov 4, 2020. Vaccine efficacy was calculated as 1 - relative risk derived from a robust Poisson regression model adjusted for age. Studies are registered at ISRCTN89951424 and ClinicalTrials.gov, NCT04324606, NCT04400838, and NCT04444674. Findings Between April 23 and Nov 4, 2020, 23 848 participants were enrolled and 11 636 participants (7548 in the UK, 4088 in Brazil) were included in the interim primary efficacy analysis. In participants who received two standard doses, vaccine efficacy was 62·1% (95% CI 41·0–75·7; 27 [0·6%] of 4440 in the ChAdOx1 nCoV-19 group vs71 [1·6%] of 4455 in the control group) and in participants who received a low dose followed by a standard dose, efficacy was 90·0% (67·4–97·0; three [0·2%] of 1367 vs 30 [2·2%] of 1374; pinteraction=0·010). Overall vaccine efficacy across both groups was 70·4% (95·8% CI 54·8–80·6; 30 [0·5%] of 5807 vs 101 [1·7%] of 5829). From 21 days after the first dose, there were ten cases hospitalised for COVID-19, all in the control arm; two were classified as severe COVID-19, including one death. There were 74 341 person-months of safety follow-up (median 3·4 months, IQR 1·3–4·8): 175 severe adverse events occurred in 168 participants, 84 events in the ChAdOx1 nCoV-19 group and 91 in the control group. Three events were classified as possibly related to a vaccine: one in the ChAdOx1 nCoV-19 group, one in the control group, and one in a participant who remains masked to group allocation. Interpretation ChAdOx1 nCoV-19 has an acceptable safety profile and has been found to be efficacious against symptomatic COVID-19 in this interim analysis of ongoing clinical trials

Safety and efficacy of the ChAdOx1 nCoV-19 vaccine (AZD1222) against SARS-CoV-2: an interim analysis of four randomised controlled trials in Brazil, South Africa, and the UK.

BACKGROUND: A safe and efficacious vaccine against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), if deployed with high coverage, could contribute to the control of the COVID-19 pandemic. We evaluated the safety and efficacy of the ChAdOx1 nCoV-19 vaccine in a pooled interim analysis of four trials. METHODS: This analysis includes data from four ongoing blinded, randomised, controlled trials done across the UK, Brazil, and South Africa. Participants aged 18 years and older were randomly assigned (1:1) to ChAdOx1 nCoV-19 vaccine or control (meningococcal group A, C, W, and Y conjugate vaccine or saline). Participants in the ChAdOx1 nCoV-19 group received two doses containing 5 × 1010 viral particles (standard dose; SD/SD cohort); a subset in the UK trial received a half dose as their first dose (low dose) and a standard dose as their second dose (LD/SD cohort). The primary efficacy analysis included symptomatic COVID-19 in seronegative participants with a nucleic acid amplification test-positive swab more than 14 days after a second dose of vaccine. Participants were analysed according to treatment received, with data cutoff on Nov 4, 2020. Vaccine efficacy was calculated as 1 - relative risk derived from a robust Poisson regression model adjusted for age. Studies are registered at ISRCTN89951424 and ClinicalTrials.gov, NCT04324606, NCT04400838, and NCT04444674. FINDINGS: Between April 23 and Nov 4, 2020, 23 848 participants were enrolled and 11 636 participants (7548 in the UK, 4088 in Brazil) were included in the interim primary efficacy analysis. In participants who received two standard doses, vaccine efficacy was 62·1% (95% CI 41·0-75·7; 27 [0·6%] of 4440 in the ChAdOx1 nCoV-19 group vs71 [1·6%] of 4455 in the control group) and in participants who received a low dose followed by a standard dose, efficacy was 90·0% (67·4-97·0; three [0·2%] of 1367 vs 30 [2·2%] of 1374; pinteraction=0·010). Overall vaccine efficacy across both groups was 70·4% (95·8% CI 54·8-80·6; 30 [0·5%] of 5807 vs 101 [1·7%] of 5829). From 21 days after the first dose, there were ten cases hospitalised for COVID-19, all in the control arm; two were classified as severe COVID-19, including one death. There were 74 341 person-months of safety follow-up (median 3·4 months, IQR 1·3-4·8): 175 severe adverse events occurred in 168 participants, 84 events in the ChAdOx1 nCoV-19 group and 91 in the control group. Three events were classified as possibly related to a vaccine: one in the ChAdOx1 nCoV-19 group, one in the control group, and one in a participant who remains masked to group allocation. INTERPRETATION: ChAdOx1 nCoV-19 has an acceptable safety profile and has been found to be efficacious against symptomatic COVID-19 in this interim analysis of ongoing clinical trials. FUNDING: UK Research and Innovation, National Institutes for Health Research (NIHR), Coalition for Epidemic Preparedness Innovations, Bill & Melinda Gates Foundation, Lemann Foundation, Rede D'Or, Brava and Telles Foundation, NIHR Oxford Biomedical Research Centre, Thames Valley and South Midland's NIHR Clinical Research Network, and AstraZeneca

Single trial Analysen von Enzephalogramm Daten.

Inspiriert, nicht zuletzt durch die Erforschung der Brain-computer-interface (BCI) Technologie präsentieren wir in dieser Dissertation neue Methoden zur Analyse makroskopisch gemessener Hirnsignale. Der Fokus liegt hierbei auf Methoden zur verbesserten Merkmalsextraktion, der Detektion mentaler Zustände und der Analyse der Variabilität von Reizantworten. Bedingte ereigniskorrelierte (De-)Synchronization: Die durch ein Ereignis induzierte Leistungsschwankung in einem Frequenzband wird konventionell als ERD bezeichnet und als relative Veränderung gegenüber der mittleren Grundaktivität gemessen. Wir erweitern den ERD-Begriff in Bezug auf eine verallgemeinerte Referenz. Dafür kontrastieren wir den zeitlichen Verlauf ereigniskorrelierter Aktivität mit dem gemessener single trials ohne spezifische Reizverarbeitung. Aus diesem verallgemeinerten Ansatz leiten wir eine Methode zur Bestimmung der Abhängigkeit der ERD-Antwort von initialen kortikalen Zuständen ab.Vergleichende Untersuchungen auf künstlichen und realen Daten validieren diesen Ansatz. Räumlich-spektrale Filter: Der Common-Spatial-Pattern Algorithmus (CSP) bestimmt für multivariate breitbandige Signale diskriminative räumliche Filter. Wir erweitern den klassischen Ansatz, so dass zusätzlich eine Optimierung einfacher Frequenzfilter erfolgt. Dies ermöglicht eine Adaptation an das individuelle EEG-Frequenzspektrums und somit eine verbesserte Merkmalsextraktion. Ein empirischer Vergleich mit dem klassischen CSP Algorithmus belegt die Vorteile unseres Verfahrens im Kontext der Klassifikation vorgestellter unilateraler Handbewegungen. Extraktion ereigniskorrelierter Potenziale (EKP): Independent component analysis (ICA) ist ein Werkzeug der statistischen Datenanalyse und Signalverarbeitung, welches multivariate Signale linear in ihre Quellkomponenten zerlegen kann. Wir präsentieren eine ICA Methode zur Extraktion von single trial EKP, welche unter Ausnutzung der Phasengebundenheit des EKP verbesserte räumliche Filter bestimmt. Simulationen mit künstlichen und echten Daten validieren diesen Ansatz in Bezug auf ein verbessertes SNR der extrahierten EKP. Zeitlich adaptive Merkmalskombination: Lateralisierte ERD des mu-Rhythmus und bewegungskorrelierte Potenziale sind die gebräuchlichsten diskriminativen Merkmale zur Klassifikation vorgestellter Handbewegungen. Wir präsentieren eine Methode diese zeitlich unterschiedlich ausgeprägten Merkmale für die Echtzeit-Klassifikation zu verwenden. Hierzu trainieren wir zunächst separat zu jedem Zeitpunkt einfache Klassifikatoren für jedes Merkmal und kombinieren diese anschließend adaptive in einem strikt kausalen, probabilistischen Ansatz. Die Leistungsfähigkeit dieses Algorithmus wurde durch seine erfolgreiche Anwendung in den BCI-Wettbewerbe 2003 und 2005 zur Klassifikation vorgestellter unilateraler Handbewegungen nachgewiesen.In this thesis, inspired by the development of the Brain-computer-interface (BCI) technology, we present novel methods for the analysis of macroscopically recorded brain signals. Here the focus is put on improved feature extraction methods, the detection of mental states and the analysis of variability of brain responses. Conditional event-related (de-)synchronization: The fluctuation of signal power in a narrow band induced by an event is conventionally termed event-related (de-)synchronization and is quantified as the relative deviation from the mean baseline activity. We extend the ERD terminology with respect to a generalized reference. To this end, we oppose the time course of the event-related activity against those obtained from single trials without specific stimulus processing. From this generalized approach we derive a method to determine the dependencies of the ERD response on initial cortical states. A comparative study of surrogate and real ERD data validates this approach. Spatio-spectral filters: The common-spatial-pattern algorithm (CSP) determines optimally discriminative spatial filters from multivariate broad-band signals. We extend the conventional algorithm such that it additionally obtains simple frequency filters. This enables adaptation to the individual characteristics of the power spectrum and thus improves feature extraction. An empirical comparison with the conventional CSP method reveals the advantages of our approach in the context of the classification of imaginary unilateral hand movements. Extraction of event-related potentials (ERP): Independent component analysis (ICA) is a tool for statistical data analysis that is able to linearly decompose multivariate signals into their underlying source components. We present an ICA method that uses prior knowledge about the phase-locked property of ERPs for their improved extraction from single trial EEG. The application on artificially generated and real world data validates this approach in terms of an improved signal-to-noise ratio of the extracted ERPs. Adaptive feature combination across time: Lateralized mu-rhythm ERD and lateralized movement-related potentials are the most commonly used discriminative features for the classification of imaginary hand movements. In the context of real time classification we present a method that efficiently combines these temporally differently accentuated features. To this end, we first train weak classifiers for each time instance and each feature separately. Subsequently we combine these weak classifiers in a strictly causal, probabilistic manner. The effectiveness of this approach was proven by its successful application to data from the international BCI competitions in 2003 and 2005

SINGLE TRIAL ANALYSES of ENCEPHALOGRAM DATA

In this thesis, inspired by the development of the Brain-computer-interface (BCI) technology, we present novel methods for the analysis of macroscopically recorded brain signals. Here the focus is put on improved feature extraction methods, the detection of mental states and the analysis of variability of brain responses. Conditional event-related (de-)synchronization: The fluctuation of signal power in a narrow band induced by an event is conventionally termed event-related (de-)synchronization and is quantified as the relative deviation from the mean baseline activity. We extend the ERD terminology with respect to a generalized reference. To this end, we oppose the time course of the event-related activity against those obtained from single trials without specific stimulus processing. From this generalized approach we derive a method to determine the dependencies of the ERD response on initial cortical states. A comparative study of surrogate and real ERD data validates this approach. Spatio-spectral filters: The common-spatial-pattern algorithm (CSP) determines optimally discriminative spatial filters from multivariate broad-band signals. We extend the conventional algorithm such that it additionally obtains simple frequency filters. This enables adaptation to the individual characteristics of the power spectrum and thus improves feature extraction. An empirical comparison with the conventional CSP method reveals the advantages of our approach in the context of the classification of imaginary unilateral hand movements. Extraction of event-related potentials (ERP): Independent component analysis (ICA) is a tool for statistical data analysis that is able to linearly decompose multivariate signals into their underlying source components. We present an ICA method that uses prior knowledge about the phase-locked property of ERPs for their improved extraction from single trial EEG. The application on artificially generated and real world data validates this approach in terms of an improved signal-to-noise ratio of the extracted ERPs. Adaptive feature combination across time: Lateralized mu-rhythm ERD and lateralized movement-related potentials are the most commonly used discriminative features for the classification of imaginary hand movements. In the context of real time classification we present a method that efficiently combines these temporally differently accentuated features. To this end, we first train weak classifiers for each time instance and each feature separately. Subsequently we combine these weak classifiers in a strictly causal, probabilistic manner. The effectiveness of this approach was proven by its successful application to data from the international BCI competitions in 2003 and 2005

An On-Line Method For Segmentation And Identification Of Non-Stationary Time Series

. We present a method for the analysis of non-stationary time series from dynamical systems that switch between multiple operating modes. In contrast to other approaches, our method processes the data incrementally and without any training of internal parameters. It straightaway performs an unsupervised segmentation and classification of the data on-the-fly. In many cases it even allows to process incoming data in real-time. The main idea of the approach is to track and segment changes of the probability density of the data in a sliding window on the incoming data stream. An application to a switching dynamical system demonstrates the potential usefulness of the algorithm in a broad range of applications

A Dynamic HMM for On-line Segmentation of Sequential Data

We propose a novel method for the analysis of sequential data that exhibits an inherent mode switching. In particular, the data might be a non-stationary time series from a dynamical system that switches between multiple operating modes. Unlike other approaches, our method processes the data incrementally and without any training of internal parameters. We use an HMM with a dynamically changing number of states and an on-line variant of the Viterbi algorithm that performs an unsupervised segmentation and classification of the data on-the-fly, i.e. the method is able to process incoming data in real-time. The main idea of the approach is to track and segment changes of the probability density of the data in a sliding window on the incoming data stream. The usefulness of the algorithm is demonstrated by an application to a switching dynamical system