Induced brain activity as indicator of cognitive processes: experimental-methodical analyses and algorithms for online-applications

Die Signalverarbeitung von elektroenzephalographischen (EEG) Signalen ist ein entscheidendes Werkzeug, um die kognitiven Prozessen verstehen zu können. Beispielweise wird induzierte Hirnaktivität in mehreren Untersuchungen mit kognitiver Leistung assoziiert. Deshalb ist die Gewinnung von elektrophysiologischen Parametern grundlegend für die Charakterisierung von kognitiven Prozessen sowie von kognitiven Dysfunktionen in neurologischen Erkrankungen. Besonders bei Epilepsie treten häufig Störungen wie Gedächtnis-, oder Aufmerksamkeitsprobleme auf, zusätzlich zu Anfällen. Neurofeedback (bzw. EEG-Biofeedback) ist eine Therapiemethode, die zusätzlich zu medikamentösen- und chirurgischen Therapien bei der Behandlung vieler neurologischer Krankheiten, einschließlich Epilepsie, erfolgreich praktiziert wird. Neurofeedback wird jedoch meist dafür angewendet, eine Anfallsreduzierung zu erzielen. Dagegen wird eine Verbesserung kognitiver Fähigkeiten auf der Basis elektrophysiologischer Änderungen selten vorgesehen. Darüber hinaus sind die aktuellen Neurofeedbackstrategien für diesen Zweck ungeeignet. Der Grund dafür sind unter anderem nicht adäquate Verfahren für die Gewinnung und Quantifizierung induzierter Hirnaktivität. Unter Berücksichtigung der oben genannten Punkten wurden die kognitiven Leistungen von einer Patientengruppe (Epilepsie) und einer Probandengruppe anhand der ereignisbezogenen De-/Synchronisation (ERD/ERS) Methode untersucht. Signifikante Unterschiede wurden im Theta bzw. Alpha Band festgestellt. Diese Ergebnisse unterstützen die Verwertung von auf ERD/ERS basierten kognitiven Parametern bei Epilepsie. Anhand einer methodischen Untersuchung von dynamischen Eigenschaften wurde ein onlinefähiger ERD/ERS Algorithmus für zukünftige Neurofeedback Applikationen ausgewählt. Basierend auf dem ausgewählten Parameter wurde eine Methodik für die online Gewinnung und Quantifizierung von kognitionsbezogener induzierter Hirnaktivität entwickelt. Die dazugehörigen Prozeduren sind in Module organisiert, um die Prozessapplikabilität zu erhöhen. Mehrere Bestandteile der Methodik, einschließlich der Rolle von Elektrodenmontagen sowie die Eliminierung bzw. Reduktion der evozierten Aktivität, wurden anhand kognitiver Aufgaben evaluiert und optimiert. Die Entwicklung einer geeigneten Neurofeedback Strategie sowie die Bestätigung der psychophysiologischen Hypothese anhand einer Pilotstudie sollen Gegenstand der zukünftigen Arbeitschritte sein.Processing of electroencephalographic (EEG) signals is a key step towards understanding cognitive brain processes. Particularly, there is growing evidence that the analysis of induced brain oscillations is a powerful tool to analyze cognitive performance. Thus, the extraction of electrophysiological features characterizing not only cognitive processes but also cognitive dysfunctions by neurological diseases is fundamental. Especially in the case of epilepsy, cognitive dysfunctions such as memory or attentional problems are often present additionally to seizures. Neurofeedback (or EEG-biofeedback) is a psychological technique that, as a supplement to medication and surgical therapies, has been demonstrated to provide further improvement in many neurological diseases, including epilepsy. However, most efforts of neurofeedback have traditionally been dedicated to the reduction of seizure frequency, and little attention has been paid for improving cognitive deficits by means of specific electrophysiological changes. Furthermore, current neurofeedback approaches are not suitable for these purposes because the parameters used do not take into consideration the relationship between memory performance and event-induced brain activity. Considering all these aspects, the cognitive performance of a group of epilepsy patients and a group of healthy controls was analyzed based on the event-related de /synchronization (ERD/ERS) method. Significant differences between both populations in the theta and upper alpha bands were observed. These findings support the possible exploitation of cognitive quantitative parameters in epilepsy based on ERD/ERS. An algorithm for the online ERD/ERS calculation was selected for future neurofeedback applications, as the result of a comparative dynamic study. Subsequently, a methodology for the online extraction and quantification of cognitive-induced brain activity was developed based on the selected algorithm. The procedure is functionally organized in blocks of algorithms in order to increase applicability. Several aspects, including the role of electrode montages and the reduction or minimization of the evoked activity, were examined based on cognitive studies as part of the optimization process. Future steps should include the design of a special training paradigm as well as a pilot study for confirming the theoretical approach proposed in this work

Dynamics on networks: the role of local dynamics and global networks on the emergence of hypersynchronous neural activity.

Published onlineJournal ArticleResearch Support, Non-U.S. Gov'tGraph theory has evolved into a useful tool for studying complex brain networks inferred from a variety of measures of neural activity, including fMRI, DTI, MEG and EEG. In the study of neurological disorders, recent work has discovered differences in the structure of graphs inferred from patient and control cohorts. However, most of these studies pursue a purely observational approach; identifying correlations between properties of graphs and the cohort which they describe, without consideration of the underlying mechanisms. To move beyond this necessitates the development of computational modeling approaches to appropriately interpret network interactions and the alterations in brain dynamics they permit, which in the field of complexity sciences is known as dynamics on networks. In this study we describe the development and application of this framework using modular networks of Kuramoto oscillators. We use this framework to understand functional networks inferred from resting state EEG recordings of a cohort of 35 adults with heterogeneous idiopathic generalized epilepsies and 40 healthy adult controls. Taking emergent synchrony across the global network as a proxy for seizures, our study finds that the critical strength of coupling required to synchronize the global network is significantly decreased for the epilepsy cohort for functional networks inferred from both theta (3-6 Hz) and low-alpha (6-9 Hz) bands. We further identify left frontal regions as a potential driver of seizure activity within these networks. We also explore the ability of our method to identify individuals with epilepsy, observing up to 80% predictive power through use of receiver operating characteristic analysis. Collectively these findings demonstrate that a computer model based analysis of routine clinical EEG provides significant additional information beyond standard clinical interpretation, which should ultimately enable a more appropriate mechanistic stratification of people with epilepsy leading to improved diagnostics and therapeutics.Funding was from Epilepsy Research UK (http://www.epilepsyresearch.org.uk) via grant number A1007 and the Medical Research Council (http://www.mrc.ac.uk) via grants (MR/K013998/1 and G0701310)

Motion Artifact Processing Techniques for Physiological Signals

The combination of reducing birth rate and increasing life expectancy continues to drive the demographic shift toward an ageing population and this is placing an ever-increasing burden on our healthcare systems. The urgent need to address this so called healthcare \time bomb" has led to a rapid growth in research into ubiquitous, pervasive and distributed healthcare technologies where recent advances in signal acquisition, data storage and communication are helping such systems become a reality. However, similar to recordings performed in the hospital environment, artifacts continue to be a major issue for these systems. The magnitude and frequency of artifacts can vary signicantly depending on the recording environment with one of the major contributions due to the motion of the subject or the recording transducer. As such, this thesis addresses the challenges of the removal of this motion artifact removal from various physiological signals. The preliminary investigations focus on artifact identication and the tagging of physiological signals streams with measures of signal quality. A new method for quantifying signal quality is developed based on the use of inexpensive accelerometers which facilitates the appropriate use of artifact processing methods as needed. These artifact processing methods are thoroughly examined as part of a comprehensive review of the most commonly applicable methods. This review forms the basis for the comparative studies subsequently presented. Then, a simple but novel experimental methodology for the comparison of artifact processing techniques is proposed, designed and tested for algorithm evaluation. The method is demonstrated to be highly eective for the type of artifact challenges common in a connected health setting, particularly those concerned with brain activity monitoring. This research primarily focuses on applying the techniques to functional near infrared spectroscopy (fNIRS) and electroencephalography (EEG) data due to their high susceptibility to contamination by subject motion related artifact. Using the novel experimental methodology, complemented with simulated data, a comprehensive comparison of a range of artifact processing methods is conducted, allowing the identication of the set of the best performing methods. A novel artifact removal technique is also developed, namely ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA), which provides the best results when applied on fNIRS data under particular conditions. Four of the best performing techniques were then tested on real ambulatory EEG data contaminated with movement artifacts comparable to those observed during in-home monitoring. It was determined that when analysing EEG data, the Wiener lter is consistently the best performing artifact removal technique. However, when employing the fNIRS data, the best technique depends on a number of factors including: 1) the availability of a reference signal and 2) whether or not the form of the artifact is known. It is envisaged that the use of physiological signal monitoring for patient healthcare will grow signicantly over the next number of decades and it is hoped that this thesis will aid in the progression and development of artifact removal techniques capable of supporting this growth

Characterizing Unstructured Motor Behaviors in the Epilepsy Monitoring Unit

Key advancements in recording hardware, data computation, clinical care, and cognitive science continue to drive new possibilities in how humans and machines can interact directly through thought. Neural data analyses with these advancements has progressed neuroscience research in functional brain mapping and brain-computer interfaces (BCIs). Much of our knowledge about BCIs is informed by data collected through carefully controlled experiments. Constraining BCI experiments with structured paradigms allows researchers to collect a high number of consistent data in a short amount of time, while also controlling for external confounds. Very little is currently known about how well these task-based relationships extend to daily life, in part because collecting data outside of the lab is challenging. To further understand natural brain activity, we must study more complex behaviors in more environmentally relevant settings. The results of this dissertation address three general challenges to studying neural correlates to unstructured behaviors. First, we continuously monitored unstructured human movements in the epilepsy monitoring unit using a video sensor synchronized to clinical intracortical electrodes. Second, we annotated unstructured behaviors from these video using both manual and computer vision methods. Finally, analyzed neural features with respect to unstructured human movements, and evaluated the performance of features identified in previous task-based studies. The preliminary nature of this work means that a majority of our demonstrations are whether the continuous paradigm can be leveraged, how one might go about leveraging it, and evaluations that tie our results back to earlier task-based studies. Our advances here motivate future works that focus more intently on what types of behaviors and neural signal features to explore

Magnetoencephalography

This is a practical book on MEG that covers a wide range of topics. The book begins with a series of reviews on the use of MEG for clinical applications, the study of cognitive functions in various diseases, and one chapter focusing specifically on studies of memory with MEG. There are sections with chapters that describe source localization issues, the use of beamformers and dipole source methods, as well as phase-based analyses, and a step-by-step guide to using dipoles for epilepsy spike analyses. The book ends with a section describing new innovations in MEG systems, namely an on-line real-time MEG data acquisition system, novel applications for MEG research, and a proposal for a helium re-circulation system. With such breadth of topics, there will be a chapter that is of interest to every MEG researcher or clinician

The Use of EEG-fMRI Features for Characterizing Mental Disorders

Determining clinically relevant biomarkers of mental disorders for reliably indicating pathophysiological processes or predicting therapeutic responses remains a major challenge, despite decades of research. Identifying such biomarkers can help patients significantly improve their quality of life and alleviate their suffering. Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are non-invasive tools to investigate neurobiological mechanisms underlying mental disorders. Extracting and leveraging informative features from the high temporal resolution EEG and high spatial resolution fMRI may offer a more comprehensive understanding of brain spatial and temporal activities in health and disease. More importantly, this information can lead to a better understanding of the neurobiology of mental illness. This dissertation investigates the analyses and applications of extracting and combining informative features from EEG and fMRI, along with applying machine learning (ML) and computational methods for building biomarkers of mental illnesses. Several methodological challenges in the extraction of informative and reproducible features are also addressed. First, two types of EEG features obtained from resting state EEG-fMRI measurements were extracted: 1) broadband-multichannel EEG dynamical features, called EEG microstates (EEG-ms); and 2) heterogeneous, static EEG features. Using EEG features only, results elucidate that: 1) EEG-ms characteristics and information theoretical properties can successfully differentiate individuals with mood and anxiety disorders from healthy comparison subjects with potential applications for other clinical groups; and 2) heterogeneous static EEG features can successfully predict “brain aging,” noted here as BrainAGE from 468 EEG datasets, achieving a correlation of r=0.61 between predicted age and chronological age. Next, extracted EEG features were leveraged with fMRI to enhance the predictivity of BrainAGE and localizing the associated EEG-ms brain regions. More specifically, static EEG features were combined with resting state fMRI features to construct a multimodal BrainAGE predictor as a case study. Notably, it was found that EEG and fMRI contain a large portion of shared information about age, although each modality has its fingerprint of the aging process. The developed approach is a general purpose and be applied to predict other outcomes from brain imaging data. Similarly, EEG-ms features were integrated with fMRI to localize associated brain regions within fMRI space, revealing functional brain connectivity changes in individuals with mood and anxiety disorders as a case study. As a result, harnessing combined EEG-fMRI methods have enriched our knowledge some mental disorders and broadened our understanding of them with potential applications for other clinical groups and outcomes. Finally, this work evaluated the reproducibility and replication of EEG-ms analysis to address technical issues that have thus far been overlooked in the literature. In conclusion, the presented work describes technical methods developed to study and discover several clinically translatable biomarkers that can be reliably used to characterize various mental disorders

Reading the brain’s personality: using machine learning to investigate the relationships between EEG and depressivity

Electroencephalography (EEG) measures electrical signals on the scalp and can give information about processes near the surface of the brain (cortex). The goal of our research was to create models that predict depressivity (mapping to personality in general, not just sickness) and to find potential biomarkers in EEG data. First, to provide our models with cleaner EEG data, we designed a novel single-channel physiology-based eye blink artefact removal method and a mains power noise removal method. Then, we assessed two main machine learning model types (classification- and regression-based) with a total of eighteen sub-types to predict the depressivity of participants. The models were generated by combining four signal processing techniques with a) three classification techniques, and b) three regression techniques. The experimental results showed that both types of models perform well in depressivity prediction and one regression-based model (Reg-FFT-LSBoost) showed a significant depressivity prediction performance, especially for female group. More importantly, we found that a specific EEG frequency band (the gamma band) made major contributions to depressivity prediction. Apart from that, the alpha and beta band may make modest contributions. Specific locations (T7, T8, and C3) made major contributions to depressivity prediction. Frontal locations may also have some influence. We also found that the combination of both eye states’ EEG data showed a better depressivity prediction ability. Compared to the eyes closed data, the EEG data obtained from the state of eyes open were more suitable for assessing depressivity. In brief, the outcomes of this research provided the possibilities for translating the EEG data for depressivity measure. Furthermore, there are possibilities to extend the research to apply to other mental disorders’ prediction, such as anxiety

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy. This edition celebrates twenty years of uninterrupted and succesfully research in the field of voice analysis