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  <doc>
    <id>8190</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>569</pageFirst>
    <pageLast>579</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Singapore</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>2023-09-01</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">U-Shape Phenomenon with Gaussian Noise and Clipped Inputs</title>
    <abstract language="eng">Object detection and recognition is a core task in machine vision. Both the position and the rotation of the objects are of interest. Common algorithms for object recognition based on neural networks use a regression formulation and are aimed at changing environments. In industrial applications, the setting is different: One would like to determine with high accuracy, the pose of known objects. We are investigating a prototype application in this field, where we identify the rotation of an object in a plane. We use common network architectures and provide an overview of how well they are suited for this purpose. In addition, we compare our results with standard image processing algorithms, paying particular attention to the accuracy with metrics relevant in the industry. Moreover, an U-shape phenomenon with disturbed data occurred during the experiments, which is partially explained analytically.</abstract>
    <parentTitle language="eng">Proceedings of Eighth International Congress on Information and Communication Technology. - (ICICT 2023, London, Volume 3)</parentTitle>
    <identifier type="isbn">978-981-99-3042-5</identifier>
    <identifier type="doi">10.1007/978-981-99-3043-2_45</identifier>
    <enrichment key="opus.import.date">2025-06-03T21:49:53+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="OtherSeries">Lecture Notes in Networks and Systems ; 695</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Sebastian Bock</author>
    <author>Philipp Schwarz</author>
    <author>Martin G. Weiß</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Neural networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Pose estimation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>U-shape</value>
    </subject>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="DFGFachsystematik" number="1">Ingenieurwissenschaften</collection>
  </doc>
  <doc>
    <id>7302</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>7</volume>
    <type>article</type>
    <publisherName>Frontiers Media SA</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A data-driven approach for the partial reconstruction of individual human molar teeth using generative deep learning</title>
    <abstract language="eng">Background and objectiveDue to the high prevalence of dental caries, fixed dental restorations are regularly required to restore compromised teeth or replace missing teeth while retaining function and aesthetic appearance. The fabrication of dental restorations, however, remains challenging due to the complexity of the human masticatory system as well as the unique morphology of each individual dentition. Adaptation and reworking are frequently required during the insertion of fixed dental prostheses (FDPs), which increase cost and treatment time. This article proposes a data-driven approach for the partial reconstruction of occlusal surfaces based on a data set that comprises 92 3D mesh files of full dental crown restorations.MethodsA Generative Adversarial Network (GAN) is considered for the given task in view of its ability to represent extensive data sets in an unsupervised manner with a wide variety of applications. Having demonstrated good capabilities in terms of image quality and training stability, StyleGAN-2 has been chosen as the main network for generating the occlusal surfaces. A 2D projection method is proposed in order to generate 2D representations of the provided 3D tooth data set for integration with the StyleGAN architecture. The reconstruction capabilities of the trained network are demonstrated by means of 4 common inlay types using a Bayesian Image Reconstruction method. This involves pre-processing the data in order to extract the necessary information of the tooth preparations required for the used method as well as the modification of the initial reconstruction loss.ResultsThe reconstruction process yields satisfactory visual and quantitative results for all preparations with a root mean square error (RMSE) ranging from 0.02 mm to 0.18 mm. When compared against a clinical procedure for CAD inlay fabrication, the group of dentists preferred the GAN-based restorations for 3 of the total 4 inlay geometries.ConclusionsThis article shows the effectiveness of the StyleGAN architecture with a downstream optimization process for the reconstruction of 4 different inlay geometries. The independence of the reconstruction process and the initial training of the GAN enables the application of the method for arbitrary inlay geometries without time-consuming retraining of the GAN.</abstract>
    <parentTitle language="eng">Frontiers in Artificial Intelligence</parentTitle>
    <identifier type="issn">2624-8212</identifier>
    <identifier type="doi">10.3389/frai.2024.1339193</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Alexander Broll</author>
    <author>Martin Rosentritt</author>
    <author>Thomas Schlegl</author>
    <author>Markus Goldhacker</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>inlay restoration</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>dental prosthesis design</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>StyleGAN</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>digital dentistry</value>
    </subject>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
  <doc>
    <id>7244</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>10</pageNumber>
    <edition/>
    <issue/>
    <volume>145</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Generative deep learning approaches for the design of dental restorations: A narrative review</title>
    <abstract language="eng">Objectives:&#13;
This study aims to explore and discuss recent advancements in tooth reconstruction utilizing deep learning (DL) techniques. A review on new DL methodologies in partial and full tooth reconstruction is conducted.&#13;
&#13;
Data/Sources:&#13;
PubMed, Google Scholar, and IEEE Xplore databases were searched for articles from 2003 to 2023.&#13;
&#13;
Study selection:&#13;
The review includes 9 articles published from 2018 to 2023. The selected articles showcase novel DL approaches for tooth reconstruction, while those concentrating solely on the application or review of DL methods are excluded. The review shows that data is acquired via intraoral scans or laboratory scans of dental plaster models. Common data representations are depth maps, point clouds, and voxelized point clouds. Reconstructions focus on single teeth, using data from adjacent teeth or the entire jaw. Some articles include antagonist teeth data and features like occlusal grooves and gap distance. Primary network architectures include Generative Adversarial Networks (GANs) and Transformers. Compared to conventional digital methods, DL-based tooth reconstruction reports error rates approximately two times lower.&#13;
&#13;
Conclusions:&#13;
Generative DL models analyze dental datasets to reconstruct missing teeth by extracting insights into patterns and structures. Through specialized application, these models reconstruct morphologically and functionally sound dental structures, leveraging information from the existing teeth. The reported advancements facilitate the feasibility of DL-based dental crown reconstruction. Beyond GANs and Transformers with point clouds or voxels, recent studies indicate promising outcomes with diffusion-based architectures and innovative data representations like wavelets for 3D shape completion and inference problems.&#13;
&#13;
Clinical significance:&#13;
Generative network architectures employed in the analysis and reconstruction of dental structures demonstrate notable proficiency. The enhanced accuracy and efficiency of DL-based frameworks hold the potential to enhance clinical outcomes and increase patient satisfaction. The reduced reconstruction times and diminished requirement for manual intervention may lead to cost savings and improved accessibility of dental services.</abstract>
    <parentTitle language="eng">Journal of Dentistry</parentTitle>
    <identifier type="issn">0300-5712</identifier>
    <identifier type="doi">10.1016/j.jdent.2024.104988</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Alexander Broll</author>
    <author>Markus Goldhacker</author>
    <author>Sebastian Hahnel</author>
    <author>Martin Rosentritt</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Tooth reconstruction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Dental prosthesis design</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Digital dentistry</value>
    </subject>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="oaweg" number="">Hybrid Open Access - OA-Veröffentlichung in einer Subskriptionszeitschrift/-medium</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
  <doc>
    <id>4572</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue>Suppl. 2</issue>
    <volume>1</volume>
    <type>conferencepresentation</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Preoperative assessment of language dominance through combined resting-state and task-based functional magnetic resonance imaging</title>
    <abstract language="eng">Background: &#13;
Brain lesions in language-related cortical areas remain a challenge in the clinical routine. In recent years the resting-state fMRI (rs-fMRI) was shown to be a feasible method for preoperative language assessment. The aim of this study was to examine whether language-related resting-state components, which have been obtained using a data-driven independent-component-based identification algorithm, can be supportive in determining language dominance in the left or right hemisphere.&#13;
&#13;
Methods: &#13;
Twenty patients suffering from brain lesions close to supposed language relevant cortical areas were included. Rs-fMRI and task-based (tb-fMRI) were performed for the purpose of preoperative language assessment. Tb-fMRI included a verb generation task with an appropriate control condition (a syllable switching task) to decompose language critical and language supportive processes. Subsequently, the best fitting ICA component for the resting-state language network (RSLN) referential to general linear models (GLMs) of the tb-fMRI (including models with and without linguistic control conditions) was identified using an algorithm based on the Dice-index.&#13;
&#13;
Results: &#13;
The RSLNs associated with GLMs using a linguistic control condition led to significantly higher laterality indices than GLM baseline contrasts. LIs derived from GLM contrasts with and without control conditions alone did not differ significantly.&#13;
&#13;
Conclusion: &#13;
In general, the results suggest that determining language dominance in the human brain is feasible both with tb-fMRI and rs-fMRI, and in particular, the combination of both approaches yields a higher specificity in preoperative language assessment. Moreover, we can conclude that the choice of the language mapping paradigm is crucial for the mentioned benefits.</abstract>
    <parentTitle language="eng">Brain and Spine</parentTitle>
    <identifier type="doi">10.1016/j.bas.2021.100523</identifier>
    <enrichment key="opus.import.date">2022-06-30T19:37:23+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Christian Ott</author>
    <author>K. Rosengarth</author>
    <author>Christian Doenitz</author>
    <author>Julius Hoehne</author>
    <author>C. Wendl</author>
    <author>Frank Dodoo-Schittko</author>
    <author>Elmar Wolfgang Lang</author>
    <author>Nils Ole Schmidt</author>
    <author>Markus Goldhacker</author>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
  <doc>
    <id>2416</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>16</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>12</volume>
    <type>article</type>
    <publisherName>Frontiers Research Foundation</publisherName>
    <publisherPlace>Lausanne</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Frequency-Resolved Dynamic Functional Connectivity Reveals Scale-Stable Features of Connectivity-States</title>
    <abstract language="eng">Investigating temporal variability of functional connectivity is an emerging field in connectomics. Entering dynamic functional connectivity by applying sliding window techniques on resting-state fMRI (rs-fMRI) time courses emerged from this topic. We introduce frequency-resolved dynamic functional connectivity (frdFC) by means of multivariate empirical mode decomposition (MEMD) followed up by filter-bank investigations. In general, we find that MEMD is capable of generating time courses to perform frdFC and we discover that the structure of connectivity-states is robust over frequency scales and even becomes more evident with decreasing frequency. This scale-stability varies with the number of extracted clusters when applying k-means. We find a scale-stability drop-off from k = 4 to k = 5 extracted connectivity-states, which is corroborated by null-models, simulations, theoretical considerations, filter-banks, and scale-adjusted windows. Our filter-bank studies show that filter design is more delicate in the rs-fMRI than in the simulated case. Besides offering a baseline for further frdFC research, we suggest and demonstrate the use of scale-stability as a possible quality criterion for connectivity-state and model selection. We present first evidence showing that connectivity-states are both a multivariate, and a multiscale phenomenon. A data repository of our frequency-resolved time-series is provided.</abstract>
    <parentTitle language="eng">Frontiers in Human Neuroscience</parentTitle>
    <identifier type="doi">10.3389/fnhum.2018.00253</identifier>
    <identifier type="issn">1662-5161</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Markus Goldhacker</author>
    <author>Ana Maria Tomé</author>
    <author>Mark W. Greenlee</author>
    <author>Elmar Wolfgang Lang</author>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
  <doc>
    <id>5101</id>
    <completedYear/>
    <publishedYear>2016</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>16</pageLast>
    <pageNumber/>
    <edition/>
    <issue>6</issue>
    <volume>3</volume>
    <type>article</type>
    <publisherName>Society for Neuroscience</publisherName>
    <publisherPlace>Washington, DC</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Spatial mnemonic encoding: Theta power decreases and medial temporal lobe BOLD increases co-occur during the usage of the method of loci</title>
    <abstract language="eng">The method of loci is one, if not the most, efficient mnemonic encoding strategy. This spatial mnemonic combines the core cognitive processes commonly linked to medial temporal lobe (MTL) activity: spatial and associative memory processes. During such processes, fMRI studies consistently demonstrate MTL activity, while electrophysiological studies have emphasized the important role of theta oscillations (3–8 Hz) in the MTL. However, it is still unknown whether increases or decreases in theta power co-occur with increased BOLD signal in the MTL during memory encoding. To investigate this question, we recorded EEG and fMRI separately, while human participants used the spatial method of loci or the pegword method, a similarly associative but nonspatial mnemonic. The more effective spatial mnemonic induced a pronounced theta power decrease source localized to the left MTL compared with the nonspatial associative mnemonic strategy. This effect was mirrored by BOLD signal increases in the MTL. Successful encoding, irrespective of the strategy used, elicited decreases in left temporal theta power and increases in MTL BOLD activity. This pattern of results suggests a negative relationship between theta power and BOLD signal changes in the MTL during memory encoding and spatial processing. The findings extend the well known negative relation of alpha/beta oscillations and BOLD signals in the cortex to theta oscillations in the MTL.</abstract>
    <parentTitle language="eng">eNeuro</parentTitle>
    <identifier type="doi">10.1523/ENEURO.0184-16.2016</identifier>
    <identifier type="issn">2373-2822</identifier>
    <identifier type="pmid">28101523</identifier>
    <enrichment key="opus.import.date">2022-08-05T07:16:41+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Marie-Christin Fellner</author>
    <author>Gregor Volberg</author>
    <author>Maria Wimber</author>
    <author>Markus Goldhacker</author>
    <author>Mark W. Greenlee</author>
    <author>Simon Hanslmayr</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>EEG</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>fMRI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>memory encoding</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>method of loci</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>mnemonics</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>theta oscillations</value>
    </subject>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
  <doc>
    <id>2455</id>
    <completedYear/>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>91</pageFirst>
    <pageLast>99</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>151</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A multi-variate blind source separation algorithm</title>
    <abstract language="eng">Background and objective&#13;
&#13;
The study follows the proposal of decomposing a given data matrix into a product of independent spatial and temporal component matrices. A multi-variate decomposition approach is presented, based on an approximate diagonalization of a set of matrices computed using a latent space representation.&#13;
Methods&#13;
&#13;
The proposed methodology follows an algebraic approach, which is common to space, temporal or spatiotemporal blind source separation algorithms. More specifically, the algebraic approach relies on singular value decomposition techniques, which avoids computationally costly and numerically instable matrix inversion. The method is equally applicable to correlation matrices determined from second order correlations or by considering fourth order correlations.&#13;
Results&#13;
&#13;
The resulting algorithms are applied to fMRI data sets either to extract the underlying fMRI components or to extract connectivity maps from resting state fMRI data collected for a dynamic functional connectivity analysis. Intriguingly, our algorithm shows increased spatial specificity compared to common approaches, while temporal precision stays similar.&#13;
Conclusion&#13;
&#13;
The study presents a novel spatiotemporal blind source separation algorithm, which is both robust and avoids parameters that are difficult to fine tune. Applied on experimental data sets, the new method yields highly confined and focused areas with least spatial extent in the retinotopy case, and similar results in the dynamic functional connectivity analyses compared to other blind source separation algorithms. Therefore, we conclude that our novel algorithm is highly competitive and yields results, which are superior or at least similar to existing approaches.</abstract>
    <parentTitle language="deu">Computer Methods and Programs in Biomedicine</parentTitle>
    <identifier type="doi">10.1016/j.cmpb.2017.08.019</identifier>
    <identifier type="issn">1872-7565</identifier>
    <identifier type="issn">0169-2607</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Markus Goldhacker</author>
    <author>P. Keck</author>
    <author>A. Igel</author>
    <author>Elmar Wolfgang Lang</author>
    <author>Ana Maria Tomé</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Blind source separation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Independent component analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>fMRI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Resting state</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Retinotopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Spatio temporal</value>
    </subject>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
  <doc>
    <id>4828</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>31</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>article</type>
    <publisherName>Hindawi</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Brain Connectivity Studies on Structure-Function Relationships: A Short Survey with an Emphasis on Machine Learning</title>
    <abstract language="eng">This short survey reviews the recent literature on the relationship between the brain structure and its functional dynamics. Imaging techniques such as diffusion tensor imaging (DTI) make it possible to reconstruct axonal fiber tracks and describe the structural connectivity (SC) between brain regions. By measuring fluctuations in neuronal activity, functional magnetic resonance imaging (fMRI) provides insights into the dynamics within this structural network. One key for a better understanding of brain mechanisms is to investigate how these fast dynamics emerge on a relatively stable structural backbone. So far, computational simulations and methods from graph theory have been mainly used for modeling this relationship. Machine learning techniques have already been established in neuroimaging for identifying functionally independent brain networks and classifying pathological brain states. This survey focuses on methods from machine learning, which contribute to our understanding of functional interactions between brain regions and their relation to the underlying anatomical substrate.</abstract>
    <parentTitle language="eng">Computational intelligence and neuroscience</parentTitle>
    <identifier type="doi">10.1155/2021/5573740</identifier>
    <identifier type="pmid">34135951</identifier>
    <enrichment key="opus.import.date">2022-07-16T21:56:15+00:00</enrichment>
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    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Simon Wein</author>
    <author>Gustavo Deco</author>
    <author>Ana Maria Tomé</author>
    <author>Markus Goldhacker</author>
    <author>Wilhelm M. Malloni</author>
    <author>Mark W. Greenlee</author>
    <author>Elmar Wolfgang Lang</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Brain Mapping</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Brain/diagnostic imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Diffusion Tensor Imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Magnetic Resonance Imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Nerve Net/diagnostic imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Neural Pathways/diagnostic imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Structure-Activity Relationship</value>
    </subject>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
  <doc>
    <id>2472</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>194</pageFirst>
    <pageLast>197</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Hybridizing EMD with cICA for fMRI Analysis of Patient Groups</title>
    <abstract language="eng">Independent component analysis (ICA), as a data driven method, has shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is, that it is naturally not convenient for analysis of group studies. Therefore various techniques have been proposed in order to overcome this limitation of ICA. In this paper a novel ICA based work-flow for extracting resting state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used to generate reference signals in a data driven manner, which can be incorporated into a constrained version of ICA (cICA), what helps to overcome the inherent ambiguities. The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach. It is demonstrated that intrinsic modes, extracted by EMD, are suitable to serve as references for cICA to obtain typical resting state patterns, which are consistent over subjects. This novel processing pipeline makes it transparent for the user, how comparable activity patterns across subjects emerge, and also the trade-off between similarity across subjects and preserving individual features can be well adjusted and adapted for different requirements in the new work-flow.</abstract>
    <parentTitle language="eng">2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): 23-27 July 2019, Berlin, Germany</parentTitle>
    <identifier type="doi">10.1109/EMBC.2019.8856355</identifier>
    <identifier type="pmid">31945876</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>S. Wein</author>
    <author>Ana Maria Tomé</author>
    <author>Markus Goldhacker</author>
    <author>Mark W. Greenlee</author>
    <author>Elmar Wolfgang Lang</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>biomedical MRI</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>data analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>independent component analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>medical image processing</value>
    </subject>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
  <doc>
    <id>2471</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>10</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>14</volume>
    <type>article</type>
    <publisherName>frontiers</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Constrained ICA-EMD Model for Group Level fMRI Analysis</title>
    <abstract language="eng">Independent component analysis (ICA), being a data-driven method, has been shown to be a powerful tool for functional magnetic resonance imaging (fMRI) data analysis. One drawback of this multivariate approach is that it is not, in general, compatible with the analysis of group data. Various techniques have been proposed to overcome this limitation of ICA. In this paper, a novel ICA-based workflow for extracting resting-state networks from fMRI group studies is proposed. An empirical mode decomposition (EMD) is used, in a data-driven manner, to generate reference signals that can be incorporated into a constrained version of ICA (cICA), thereby eliminating the inherent ambiguities of ICA. The results of the proposed workflow are then compared to those obtained by a widely used group ICA approach for fMRI analysis. In this study, we demonstrate that intrinsic modes, extracted by EMD, are suitable to serve as references for cICA. This approach yields typical resting-state patterns that are consistent over subjects. By introducing these reference signals into the ICA, our processing pipeline yields comparable activity patterns across subjects in a mathematically transparent manner. Our approach provides a user-friendly tool to adjust the trade-off between a high similarity across subjects and preserving individual subject features of the independent components.</abstract>
    <parentTitle language="eng">Frontiers in Neuroscience</parentTitle>
    <identifier type="doi">10.3389/fnins.2020.00221</identifier>
    <identifier type="issn">1662-453X</identifier>
    <identifier type="issn">1662-4548</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Creative Commons - CC BY - Namensnennung 4.0 International</licence>
    <author>Simon Wein</author>
    <author>Ana Maria Tomé</author>
    <author>Markus Goldhacker</author>
    <author>Mark W. Greenlee</author>
    <author>Elmar Wolfgang Lang</author>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="oaweg" number="">Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
  <doc>
    <id>5102</id>
    <completedYear/>
    <publishedYear>2015</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>193</pageFirst>
    <pageLast>205</pageLast>
    <pageNumber/>
    <edition/>
    <issue>September</issue>
    <volume>253</volume>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>AMsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">EMDLAB: A toolbox for analysis of single-trial EEG dynamics using empirical mode decomposition</title>
    <abstract language="eng">Background: &#13;
Empirical mode decomposition (EMD) is an empirical data decomposition technique. Recently there is growing interest in applying EMD in the biomedical field.&#13;
&#13;
New method: &#13;
EMDLAB is an extensible plug-in for the EEGLAB toolbox, which is an open software environment for electrophysiological data analysis.&#13;
&#13;
Results: &#13;
EMDLAB can be used to perform, easily and effectively, four common types of EMD: plain EMD, ensemble EMD (EEMD), weighted sliding EMD (wSEMD) and multivariate EMD (MEMD) on EEG data. In addition, EMDLAB is a user-friendly toolbox and closely implemented in the EEGLAB toolbox.&#13;
&#13;
Comparison with existing methods: &#13;
EMDLAB gains an advantage over other open-source toolboxes by exploiting the advantageous visualization capabilities of EEGLAB for extracted intrinsic mode functions (IMFs) and Event-Related Modes (ERMs) of the signal.&#13;
&#13;
Conclusions: &#13;
EMDLAB is a reliable, efficient, and automated solution for extracting and visualizing the extracted IMFs and ERMs by EMD algorithms in EEG study.</abstract>
    <parentTitle language="eng">Journal of Neuroscience Methods</parentTitle>
    <identifier type="pmid">26162614</identifier>
    <identifier type="doi">10.1016/j.jneumeth.2015.06.020</identifier>
    <enrichment key="opus.import.date">2022-08-05T07:16:41+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">importuser</enrichment>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Markus Goldhacker</author>
    <author>Karema Al-Subari</author>
    <author>Saad Al-Baddai</author>
    <author>Ana Maria Tomé</author>
    <author>Rupert Faltermeier</author>
    <author>Elmar Wolfgang Lang</author>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othpublikationsherkunft" number="">Externe Publikationen</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="persons" number="goldhackerbics">Goldhacker, Markus (Prof. Dr.) - Brain-Computer Interface and Cognitive Systems Lab</collection>
  </doc>
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