MATLAB - Simulink - Stateflow

Angermann, Anne; Beuschel, Michael; Rau, Martin; Wohlfarth, Ulrich

doi:10.1515/9783110484892

Cited by 6 publications

(5 citation statements)

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“…Source EEG and fNIRS data reconstruction in source space were performed using Brainstorm software [55] and custom Matlab scripts [2]. For EEG a multiple-layer head model (Boundary Element Method-BEM) and an MRI template available in BrainStorm (Colin27) were used to build a realistic head model (forward model) by the OpenMEEG tool [21], which takes the different geometry and conductivity characteristics of the head tissues into account.…”

Section: Signal Reconstructionmentioning

confidence: 99%

Investigating the Interaction Between EEG and fNIRS: A Multimodal Network Analysis of Brain Connectivity

Blanco,

Koba,

Crimi

2023

Preprint

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The brain is a complex system with functional and structural networks. Different neuroimaging methods have their strengths and limitations, depending on the signals they measure. Combining techniques like electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) techniques has gained interest, but understanding how the information derived from these modalities is related remains an exciting open question. Successful integration of these modalities requires a sophisticated mathematical framework that goes beyond simple comparative analyses. The multilayer network model has emerged as a promising approach. This study is an extended version of the conference paper “Resting State Brain Connectivity Analysis from EEG and FNIRS Signals” [5]. In this study, we explored the brain network properties obtained from EEG and fNIRS data using graph analysis. Additionally, we adopted the multilayer network model to evaluate the benefits of combining multiple modalities compared to using a single modality. A small-world network structure was observed in the rest, right motor imagery, and left motor imagery tasks in both modalities. We found that EEG captures faster changes in neural activity, thus providing a more precise estimation of the timing of information transfer between brain regions in RS. fNIRS provides insights into the slower hemodynamic responses associated with longer-lasting and sustained neural processes in cognitive tasks. The multilayer approach outperformed unimodal analyses, offering a richer understanding of brain function. Complementarity between EEG and fNIRS was observed, particularly during tasks, as well as a certain level of redundancy and complementarity between the multimodal and the unimodal approach, which is dependent on the modality and on the specific brain state. Overall, the results highlight differences in how EEG and fNIRS capture brain network topology in RS and tasks and emphasize the value of integrating multiple modalities for a comprehensive view of brain connectivity and function.

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Section: Signal Reconstructionmentioning

confidence: 99%

Investigating the Interaction Between EEG and fNIRS: A Multimodal Network Analysis of Brain Connectivity

Blanco,

Koba,

Crimi

2023

Preprint

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“…Source EEG and fNIRS data reconstruction were performed using Brainstorm software [24] and custom Matlab scripts [2]. For EEG a multiple-layer head model (Boundary Element Method-BEM) and an MRI template (MNI-ICBM152) were used to build a realistic head model (forward model) by the OpenMEEG tool, which takes into account the different geometry and conductivity characteristics of the head tissues.…”

Section: Signals Reconstructionmentioning

confidence: 99%

Resting State Brain Connectivity analysis from EEG and FNIRS signals

Blanco¹,

Koba²,

Crimi³

2023

Preprint

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Contemporary neuroscience is highly focused on the synergistic use of machine learning and network analysis. Indeed, network neuroscience analysis intensively capitalizes on clustering metrics and statistical tools. In this context, the integrated analysis of functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) provides complementary information about the electrical and hemodynamic activity of the brain. Evidence supports the mechanism of the neurovascular coupling mediates brain processing. However, it is not well understood how the specific patterns of neuronal activity are represented by these techniques. Here we have investigated the topological properties of functional networks of the resting-state brain between synchronous EEG and fNIRS connectomes, across frequency bands, using source space analysis, and through graph theoretical approaches. We observed that at global-level analysis small-world topology network features for both modalities. The edge-wise analysis pointed out increased inter-hemispheric connectivity for oxy-hemoglobin compared to EEG, with no differences across the frequency bands. Our results show that graph features extracted from fNIRS can reflect both short- and long-range organization of neural activity, and that is able to characterize the large-scale network in the resting state. Further development of integrated analyses of the two modalities is required to fully benefit from the added value of each modality. However, the present study highlights that multimodal source space analysis approaches can be adopted to study brain functioning in healthy resting states, thus serving as a foundation for future work during tasks and in pathology, with the possibility of obtaining novel comprehensive biomarkers for neurological diseases.

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“…Model parts that violate any safety concerns can then be localized and improved, resulting in an iterative development process. Examples of such simulation and analysis tools include Matlab/Simulink [3], Ptolemy II [41], and AADLSim [8]. Most of these simulation frameworks focus on rich specification languages, problems orthogonal to functional safety (e.g., uncertainty or performance), and are tied to specific modeling languages.…”

Section: Introductionmentioning

confidence: 99%

“…An important challenge is the gap between design model and the actual implementation that is simulated. Most of the aforementioned approaches already work with detailed enough design models amenable to simulation (e.g., Matlab/Simulink [3]). However, this puts a lot of burden onto the early development of such systems due to high complexity in the modeling phase.…”

Section: Introductionmentioning

confidence: 99%

Runtime Verification of Correct-by-Construction Driving Maneuvers

Kittelmann

Runge

Bordis

et al. 2022

Lecture Notes in Computer Science

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Cyber-physical systems play an increasingly vital role in our everyday lives by leveraging technology to mitigate human error. These systems are inherently safety-critical, which requires the highest standards in quality assurance. Therefore, designing safe behaviors for these systems in a manageable fashion and maximizing trust early on by formally verifying them against a formal specification mandates a software engineering process that prioritizes appropriate abstractions in the early design phase. However, even if models are formally verified at design time, their appropriateness in the real world stills needs to be validated at runtime, as specifications are usually incomplete. In this work, we introduce a methodology for refining verified cyber-physical systems modeled by hybrid mode automata to executable source code amenable for runtime verification. In particular, we employ ArchiCorC, which lifts the correctness-by-construction paradigm for programs to component-based architectures, and comes with facilities for code generation. Subsequent simulations of the executable and verified maneuvers allow to validate their initial requirements in a diverse set of scenarios.

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MATLAB - Simulink - Stateflow

Cited by 6 publications

References 0 publications

Investigating the Interaction Between EEG and fNIRS: A Multimodal Network Analysis of Brain Connectivity

Investigating the Interaction Between EEG and fNIRS: A Multimodal Network Analysis of Brain Connectivity

Resting State Brain Connectivity analysis from EEG and FNIRS signals

Runtime Verification of Correct-by-Construction Driving Maneuvers

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