80 research outputs found

    Design and Evaluation of a Hardware System for Online Signal Processing within Mobile Brain-Computer Interfaces

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    Brain-Computer Interfaces (BCIs) sind innovative Systeme, die eine direkte Kommunikation zwischen dem Gehirn und externen Geräten ermöglichen. Diese Schnittstellen haben sich zu einer transformativen Lösung nicht nur für Menschen mit neurologischen Verletzungen entwickelt, sondern auch für ein breiteres Spektrum von Menschen, das sowohl medizinische als auch nicht-medizinische Anwendungen umfasst. In der Vergangenheit hat die Herausforderung, dass neurologische Verletzungen nach einer anfänglichen Erholungsphase statisch bleiben, die Forscher dazu veranlasst, innovative Wege zu beschreiten. Seit den 1970er Jahren stehen BCIs an vorderster Front dieser Bemühungen. Mit den Fortschritten in der Forschung haben sich die BCI-Anwendungen erweitert und zeigen ein großes Potenzial für eine Vielzahl von Anwendungen, auch für weniger stark eingeschränkte (zum Beispiel im Kontext von Hörelektronik) sowie völlig gesunde Menschen (zum Beispiel in der Unterhaltungsindustrie). Die Zukunft der BCI-Forschung hängt jedoch auch von der Verfügbarkeit zuverlässiger BCI-Hardware ab, die den Einsatz in der realen Welt gewährleistet. Das im Rahmen dieser Arbeit konzipierte und implementierte CereBridge-System stellt einen bedeutenden Fortschritt in der Brain-Computer-Interface-Technologie dar, da es die gesamte Hardware zur Erfassung und Verarbeitung von EEG-Signalen in ein mobiles System integriert. Die Architektur der Verarbeitungshardware basiert auf einem FPGA mit einem ARM Cortex-M3 innerhalb eines heterogenen ICs, was Flexibilität und Effizienz bei der EEG-Signalverarbeitung gewährleistet. Der modulare Aufbau des Systems, bestehend aus drei einzelnen Boards, gewährleistet die Anpassbarkeit an unterschiedliche Anforderungen. Das komplette System wird an der Kopfhaut befestigt, kann autonom arbeiten, benötigt keine externe Interaktion und wiegt einschließlich der 16-Kanal-EEG-Sensoren nur ca. 56 g. Der Fokus liegt auf voller Mobilität. Das vorgeschlagene anpassbare Datenflusskonzept erleichtert die Untersuchung und nahtlose Integration von Algorithmen und erhöht die Flexibilität des Systems. Dies wird auch durch die Möglichkeit unterstrichen, verschiedene Algorithmen auf EEG-Daten anzuwenden, um unterschiedliche Anwendungsziele zu erreichen. High-Level Synthesis (HLS) wurde verwendet, um die Algorithmen auf das FPGA zu portieren, was den Algorithmenentwicklungsprozess beschleunigt und eine schnelle Implementierung von Algorithmusvarianten ermöglicht. Evaluierungen haben gezeigt, dass das CereBridge-System in der Lage ist, die gesamte Signalverarbeitungskette zu integrieren, die für verschiedene BCI-Anwendungen erforderlich ist. Darüber hinaus kann es mit einer Batterie von mehr als 31 Stunden Dauerbetrieb betrieben werden, was es zu einer praktikablen Lösung für mobile Langzeit-EEG-Aufzeichnungen und reale BCI-Studien macht. Im Vergleich zu bestehenden Forschungsplattformen bietet das CereBridge-System eine bisher unerreichte Leistungsfähigkeit und Ausstattung für ein mobiles BCI. Es erfüllt nicht nur die relevanten Anforderungen an ein mobiles BCI-System, sondern ebnet auch den Weg für eine schnelle Übertragung von Algorithmen aus dem Labor in reale Anwendungen. Im Wesentlichen liefert diese Arbeit einen umfassenden Entwurf für die Entwicklung und Implementierung eines hochmodernen mobilen EEG-basierten BCI-Systems und setzt damit einen neuen Standard für BCI-Hardware, die in der Praxis eingesetzt werden kann.Brain-Computer Interfaces (BCIs) are innovative systems that enable direct communication between the brain and external devices. These interfaces have emerged as a transformative solution not only for individuals with neurological injuries, but also for a broader range of individuals, encompassing both medical and non-medical applications. Historically, the challenge of neurological injury being static after an initial recovery phase has driven researchers to explore innovative avenues. Since the 1970s, BCIs have been at one forefront of these efforts. As research has progressed, BCI applications have expanded, showing potential in a wide range of applications, including those for less severely disabled (e.g. in the context of hearing aids) and completely healthy individuals (e.g. entertainment industry). However, the future of BCI research also depends on the availability of reliable BCI hardware to ensure real-world application. The CereBridge system designed and implemented in this work represents a significant leap forward in brain-computer interface technology by integrating all EEG signal acquisition and processing hardware into a mobile system. The processing hardware architecture is centered around an FPGA with an ARM Cortex-M3 within a heterogeneous IC, ensuring flexibility and efficiency in EEG signal processing. The modular design of the system, consisting of three individual boards, ensures adaptability to different requirements. With a focus on full mobility, the complete system is mounted on the scalp, can operate autonomously, requires no external interaction, and weighs approximately 56g, including 16 channel EEG sensors. The proposed customizable dataflow concept facilitates the exploration and seamless integration of algorithms, increasing the flexibility of the system. This is further underscored by the ability to apply different algorithms to recorded EEG data to meet different application goals. High-Level Synthesis (HLS) was used to port algorithms to the FPGA, accelerating the algorithm development process and facilitating rapid implementation of algorithm variants. Evaluations have shown that the CereBridge system is capable of integrating the complete signal processing chain required for various BCI applications. Furthermore, it can operate continuously for more than 31 hours with a 1800mAh battery, making it a viable solution for long-term mobile EEG recording and real-world BCI studies. Compared to existing research platforms, the CereBridge system offers unprecedented performance and features for a mobile BCI. It not only meets the relevant requirements for a mobile BCI system, but also paves the way for the rapid transition of algorithms from the laboratory to real-world applications. In essence, this work provides a comprehensive blueprint for the development and implementation of a state-of-the-art mobile EEG-based BCI system, setting a new benchmark in BCI hardware for real-world applicability

    Using spontaneous eye blink-related brain activity to investigate cognitive load during mobile map-assisted navigation

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    The continuous assessment of pedestrians’ cognitive load during a naturalistic mobile map-assisted navigation task is challenging because of limited experimental control over stimulus presentation, human-map-interactions, and other participant responses. To overcome this challenge, the present study takes advantage of navigators’ spontaneous eye blinks during navigation to serve as event markers in continuously recorded electroencephalography (EEG) data to assess cognitive load in a mobile map-assisted navigation task. We examined if and how displaying different numbers of landmarks (3 vs. 5 vs. 7) on mobile maps along a given route would influence navigators’ cognitive load during navigation in virtual urban environments. Cognitive load was assessed by the peak amplitudes of the blink-related fronto-central N2 and parieto-occipital P3. Our results show increased parieto-occipital P3 amplitude indicating higher cognitive load in the 7-landmark condition, compared to showing 3 or 5 landmarks. Our prior research already demonstrated that participants acquire more spatial knowledge in the 5- and 7-landmark conditions compared to the 3-landmark condition. Together with the current study, we find that showing 5 landmarks, compared to 3 or 7 landmarks, improved spatial learning without overtaxing cognitive load during navigation in different urban environments. Our findings also indicate a possible cognitive load spillover effect during map-assisted wayfinding whereby cognitive load during map viewing might have affected cognitive load during goal-directed locomotion in the environment or vice versa. Our research demonstrates that users’ cognitive load and spatial learning should be considered together when designing the display of future navigation aids and that navigators’ eye blinks can serve as useful event makers to parse continuous human brain dynamics reflecting cognitive load in naturalistic settings

    Action language processing in Parkinson’s disease: Characterization of neuro-oscillatory dynamics and linguistic performance

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    Human language capacity is based on temporally coordinated neural activity across distributed brain regions. Although the left hemispheric perisylvian cortex constitutes the core region of language processing, a network of additional sites is further involved. For example, in the healthy brain, semantic access to action concepts has been associated with increased neural activity within frontal motor areas. These findings are complemented by studies demonstrating impaired action language processing in patients with Parkinson's disease, a condition leading to impaired motor control. Therefore, both lines of inquiry suggest an involvement of sensorimotor brain regions in the semantic access to action concepts. However, as the neural underpinnings of the putative action language deficit in Parkinson's disease are unknown, the contribution of motor areas to this phenomenon remains unresolved. This study therefore aimed at resolving this question by characterizing neurophysiological and behavioral correlates of action language processing in patients with Parkinson's disease. For this purpose, two experiments were carried out. The goal of Experiment 1 was to compile and validate a data set of action pictures for the German language. This part of the study aimed at identifying psycholinguistic variables affecting naming latency in a picture naming task, allowing the selection of matched sets of stimuli in prospective studies. Experiment 2 built upon these data and employed an action naming task and high-density electroencephalography to characterize oscillatory patterns during action language production in both healthy participants and patients with Parkinson's disease. Specifically, this part of the study examined whether action language processing is accompanied with aberrant oscillatory patterns in the mu and beta frequency range over motor cortical areas in the parkinsonian state. Furthermore, the influence of dopaminergic medication on these patterns was assessed. In Experiment 1, a total of 283 freely available action pictures could be assembled and characterized. The principal variables affecting naming latency describe the agreement in responses across subjects: Less homogeneous response distributions were associated with longer reaction times. Furthermore, word frequency as well as the motor content of the pictures and responses were significant predictors of naming latency. Experiment 2 could not replicate the behavioral action naming deficit in patients with Parkinson's disease when compared to healthy participants. However, differential neurophysiological correlates of action naming were observed. In contrast to healthy subjects, a transient episode of beta hypersynchronization was present over central to frontal electrodes in Parkinson's disease patients off medication within 300 to 700 ms after stimulus presentation. Cluster-based permutation tests confirmed this difference in oscillatory power and by reconstructing the sources of neural activity it could be localized to the left pre- and postcentral cortex and to the right anterior temporal lobe. Furthermore, subsequent mu power suppression (from 800 ms onwards) was stronger in patients with Parkinson's disease than in healthy controls. The associations between psycholinguistic variables and naming latency found in Experiment 1 were largely consistent with action naming normative studies carried out in other languages. The data set of 283 action pictures may therefore constitute a valuable resource for future psycholinguistic investigations of action language processing. In Experiment 2, behavioral results were not in keeping with a specific action language deficit in patients with Parkinson's disease, which stands in contrast to prior studies. However, patients included in this study attained a higher level of education as those examined in earlier reports, potentially compensating the hypothesized deficit. On the neurophysiological level though, exaggerated beta power in Parkinson's disease patients showed a spatiotemporal pattern which may reflect aberrant semantic access to action concepts grounded in the motor system: Differential neural activity was partly observed during a previously established time frame for semantic processing and located to brain regions that have been associated with access to action concepts, including the sensorimotor cortex. In conclusion, this study established a methodological basis for further psycholinguistic studies on action language processing by validating a normative action picture data set for the German language. By applying this data set in an action naming task and recording high density electroencephalography in Parkinson's disease patients and healthy controls, neurophysiological correlates of action language processing were examined. While behavioral results were not in keeping with a hypothesized action naming deficit, differential oscillatory activity in the beta frequency range suggests a contribution of the motor system to altered semantic processing of action concepts in patients with Parkinson's disease

    Human spatial navigation in the digital era: Effects of landmark depiction on mobile maps on navigators’ spatial learning and brain activity during assisted navigation

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    Navigation was an essential survival skill for our ancestors and is still a fundamental activity in our everyday lives. To stay oriented and assist navigation, our ancestors had a long history of developing and employing physical maps that communicated an enormous amount of spatial and visual information about their surroundings. Today, in the digital era, we are increasingly turning to mobile navigation devices to ease daily navigation tasks, surrendering our spatial and navigational skills to the hand-held device. On the flip side, the conveniences of such devices lead us to pay less attention to our surroundings, make fewer spatial decisions, and remember less about the surroundings we have traversed. As navigational skills and spatial memory are related to adult neurogenesis, healthy aging, education, and survival, scientists and researchers from multidisciplinary fields have made calls to develop a new account of mobile navigation assistance to preserve human navigational abilities and spatial memory. Landmarks have been advocated for special attention in developing cognitively supportive navigation systems, as landmarks are widely accepted as key features to support spatial navigation and spatial learning of an environment. Turn-by-turn direction instructions without reference to surrounding landmarks, such as those provided by most existing navigation systems, can be one of the reasons for navigators’ spatial memory deterioration during assisted navigation. Despite the benefit of landmarks in navigation and spatial learning, long-standing literature on cognitive psychology has pointed out that individuals have only a limited cognitive capacity to process presented information for a task. When the learning items exceed learners’ capacity, the performance may reach a plateau or even drop. This leads to an unexamined yet important research question on how to visualize landmarks on a mobile map to optimize navigators’ cognitive resource exertion and thus optimize their spatial learning. To investigate this question, I leveraged neuropsychological and hypothesis-driven approaches and investigated whether and how different numbers of landmarks depicted on a mobile map affected navigators’ spatial learning, cognitive load, and visuospatial encoding. Specifically, I set out a navigation experiment in three virtual urban environments, in which participants were asked to follow a given route to a specific destination with the aid of a mobile map. Three different numbers of landmarks—3, 5, and 7—along the given route were selected based on cognitive capacity literature and presented to 48 participants during map-assisted navigation. Their brain activity was recorded both during the phase of map consultation and during that of active locomotion. After navigation in each virtual city, their spatial knowledge of the traversed routes was assessed. The statistical results revealed that spatial learning improved when a medium number of landmarks (i.e., five) was depicted on a mobile map compared to the lowest evaluated number (i.e., three) of landmarks, and there was no further improvement when the highest number (i.e., seven) of landmarks were provided on the mobile map. The neural correlates that were interpreted to reflect cognitive load during map consultation increased when participants were processing seven landmarks depicted on a mobile map compared to the other two landmark conditions; by contrast, the neural correlates that indicated visuospatial encoding increased with a higher number of presented landmarks. In line with the cognitive load changes during map consultation, cognitive load during active locomotion also increased when participants were in the seven-landmark condition, compared to the other two landmark conditions. This thesis provides an exemplary paradigm to investigate navigators’ behavior and cognitive processing during map-assisted navigation and to utilize neuropsychological approaches to solve cartographic design problems. The findings contribute to a better understanding of the effects of landmark depiction (3, 5, and 7 landmarks) on navigators’ spatial learning outcomes and their cognitive processing (cognitive load and visuospatial encoding) during map-assisted navigation. Of these insights, I conclude with two main takeaways for audiences including navigation researchers and navigation system designers. First, the thesis suggests a boundary effect of the proposed benefits of landmarks in spatial learning: providing landmarks on maps benefits users’ spatial learning only to a certain extent when the number of landmarks does not increase cognitive load. Medium number (i.e., 5) of landmarks seems to be the best option in the current experiment, as five landmarks facilitate spatial learning without taxing additional cognitive resources. The second takeaway is that the increased cognitive load during map use might also spill over into the locomotion phase through the environment; thus, the locomotion phase in the environment should also be carefully considered while designing a mobile map to support navigation and environmental learning

    High Frequency Physiological Data Quality Modelling in the Intensive Care Unit

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    Intensive care medicine is a resource intense environment in which technical and clinical decision making relies on rapidly assimilating a huge amount of categorical and timeseries physiologic data. These signals are being presented at variable frequencies and of variable quality. Intensive care clinicians rely on high frequency measurements of the patient's physiologic state to assess critical illness and the response to therapies. Physiological waveforms have the potential to reveal details about the patient state in very fine resolution, and can assist, augment, or even automate decision making in intensive care. However, these high frequency time-series physiologic signals pose many challenges for modelling. These signals contain noise, artefacts, and systematic timing errors, all of which can impact the quality and accuracy of models being developed and the reproducibility of results. In this context, the central theme of this thesis is to model the process of data collection in an intensive care environment from a statistical, metrological, and biosignals engineering perspective with the aim of identifying, quantifying, and, where possible, correcting errors introduced by the data collection systems. Three different aspects of physiological measurement were explored in detail, namely measurement of blood oxygenation, measurement of blood pressure, and measurement of time. A literature review of sources of errors and uncertainty in timing systems used in intensive care units was undertaken. A signal alignment algorithm was developed and applied to approximately 34,000 patient-hours of simultaneously collected electroencephalography and physiological waveforms collected at the bedside using two different medical devices

    Brain-Computer Interface

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    Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems

    Intelligent Biosignal Analysis Methods

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    This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others

    Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

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    This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective

    Recent Applications in Graph Theory

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    Graph theory, being a rigorously investigated field of combinatorial mathematics, is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations have made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further. This volume is a small yet unique contribution to graph theory applications and modeling with graphs. The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks

    Brain Computer Interfaces and Emotional Involvement: Theory, Research, and Applications

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    This reprint is dedicated to the study of brain activity related to emotional and attentional involvement as measured by Brain–computer interface (BCI) systems designed for different purposes. A BCI system can translate brain signals (e.g., electric or hemodynamic brain activity indicators) into a command to execute an action in the BCI application (e.g., a wheelchair, the cursor on the screen, a spelling device or a game). These tools have the advantage of having real-time access to the ongoing brain activity of the individual, which can provide insight into the user’s emotional and attentional states by training a classification algorithm to recognize mental states. The success of BCI systems in contemporary neuroscientific research relies on the fact that they allow one to “think outside the lab”. The integration of technological solutions, artificial intelligence and cognitive science allowed and will allow researchers to envision more and more applications for the future. The clinical and everyday uses are described with the aim to invite readers to open their minds to imagine potential further developments
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