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  • 1.
    Hammarberg, Björn
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Signals and Systems Group.
    Forster, C
    Torebjörk, E
    Parameter estimation of human nerve C-fibers using matched filtering and multiple hypothesis tracking2002In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 49, no 4Article in journal (Refereed)
  • 2. Hammarberg (Hansson), Björn
    et al.
    Stålberg, Erik
    Uppsala University, Medicinska vetenskapsområdet, Faculty of Medicine, Department of Neuroscience.
    Novel ideas for fast muscle action potential simulations using the line source model2004In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 51, no 11, p. 1888-1897Article in journal (Refereed)
    Abstract [en]

    Using a signal processing approach, we analyze the line source model for muscle action potential (AP) modeling. We show that the original model presents a tradeoff between violating the Nyquist criterion on one hand and using a discretization frequency that is unnecessarily high with respect to the bandwidth of the generated AP on the other. Here, we present an improved line source model that, compared to the original, allows a lower discretization frequency while retaining the accuracy by simply introducing a continuous-time anti-aliasing filter. Moreover, a transfer function form of the transmembrane current is presented that promote the use of sophisticated signal processing methods on these type of signals. Both continuous-time and discrete-time models are presented. We also address and analyze the implications of the finite length of the muscle fibers. Including this in the model is straightforward, owing to the convolutional form of the line source model, and is manifested by a simple transformation of the associated weighting function. AP modeling is discussed for the three different electrode models: the concentric needle electrode, the single fiber electrode, and the macro electrode. The presented model is suitable for modeling large motor units, where both accuracy and computational efficiency are important factors. To simplify the selection of the discretization interval, we derive what we call the cumulative cutoff frequency that provides an estimate of the required Nyquist frequency.

  • 3.
    Kopel, Rotem
    et al.
    Univ Geneva, Fac Med, CH-1211 Geneva, Switzerland.;Ecole Polytech Fed Lausanne, Inst Bioengn, CH-1015 Lausanne, Switzerland..
    Emmert, Kirsten
    Univ Geneva, Fac Med, CH-1211 Geneva, Switzerland..
    Scharnowski, Frank
    Univ Zurich, Neurosci Ctr Zurich, Psychiat Univ Hosp, Zurich, Switzerland.;Univ Zurich, Zurich Ctr Integrat Human Physiol, Zurich, Switzerland..
    Haller, Sven
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology. Univ Hosp Freiburg, Dept Neuroradiol, Freiburg, Germany..
    Van De Ville, Dimitri
    Univ Geneva, Fac Med, CH-1211 Geneva, Switzerland.;Ecole Polytech Fed Lausanne, Inst Bioengn, CH-1015 Lausanne, Switzerland..
    Distributed Patterns of Brain Activity Underlying Real-Time fMRI Neurofeedback Training2017In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 64, no 6, p. 1228-1237Article in journal (Refereed)
    Abstract [en]

    Neurofeedback (NF) based on real-time functional magnetic resonance imaging (rt-fMRI) is an exciting neuroimaging application. In most rt-fMRI NF studies, the activity level of a single region of interest (ROI) is provided as a feedback signal and the participants are trained to up or down regulate the feedback signal. NF training effects are typically analyzed using a confirmatory univariate approach, i.e., changes in the target ROI are explained by a univariate linear modulation. However, learning to self-regulate the ROI activity through NF is mediated by distributed changes across the brain. Here, we deploy a multivariate decoding model for assessing NF training effects across the whole brain. Specifically, we first explain the NF training effect by a posthoc multivariate model that leads to a pattern of coactivation based on 90 functional atlas regions. We then use cross validation to reveal the set of brain regions with the best fit. This novel approach was applied to the data from a rt-fMRI NF study where the participants learned to down regulate the auditory cortex. We found that the optimal model consisted of 16 brain regions whose coactivation patterns best described the training effect over the NF training days. Cross validation of the multivariate model showed that it generalized across the participants. Interestingly, the participants could be clustered into two groups with distinct patterns of coactivation, potentially reflecting different NF learning strategies. Overall, our findings revealed that multiple brain regions are involved in learning to regulate an activity in a single ROI, and thus leading to a better understanding of the mechanisms underlying NF training.

  • 4.
    Kårsnäs, Andreas
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Strand, Robin
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Multimodal histological image registration using locally rigid transforms2014In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531Article in journal (Other academic)
    Abstract [en]

    Evaluating multimodal histological images is animportant task within cancer diagnosis and research. Newmethods are currently under development, such as multiplexingand destaining/restaining protocols, but comparing data fromconsecutive monomodal sections is still the most common methodfor acquiring multimodal data. To allow for comparison of con-secutive sections, registration of the sections is needed. Becauseof the spatial distance between the sections as well as non-uniform deformations, due to mechanical and chemical stressduring handling and staining, this is not a trivial task. Inthis paper, we confirm that deformable transforms outperformlinear transforms when it comes to registration quality. However,large deformations can result in a poor viewing experience forthe pathologist when evaluating the slides, as local structuresare distorted and may look unnatural. The deformations alsoaffect measurements made on the deformed image. We presenta method for locally approximating the global deformabletransform with a rigid transform, and we introduce a gradeof rigidity term that enables a trade-off between registrationquality and measurement distortion. We use a strategy of dividingthe registration in an offline and online step, which gives usthe possibility to perform the approximation in real-time. Thisability offers the viewer with the possibility to quickly switchbetween a view that has optimal registration and a view wheremeasurements are not distorted and where structures ”lookright”. To facilitate further research within the subject, wepresent a registration tool that provides an intuitive interfacefor making comparisons between global deformable transformsand locally rigid approximations with varying degree of rigidity.

  • 5. Roxhed, Niclas
    et al.
    Samel, Björn
    Nordquist, Lina
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Cell Biology, Integrativ Fysiologi.
    Griss, Patrick
    Stemme, Göran
    Painless drug delivery through microneedle-based transdermal patches featuring active infusion2008In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 55, no 3, p. 1063-1071Article in journal (Refereed)
    Abstract [en]

    This paper presents the first microneedle-based transdermal patch with integrated active dispensing functionality. The electrically controlled system consists of a low-cost dosing and actuation unit capable of controlled release of liquid in the microliter range at low flow-rates and minimally invasive, side-opened, microneedles. The system was successfully tested in vivo by insulin administration to diabetic rats. Active infusion of insulin at 2 mul/h was compared to passive, diffusion-driven, delivery. Continuous active infusion caused significantly higher insulin concentrations in blood plasma. After a 3-h delivery period, the insulin concentration was five times larger compared to passive delivery. Consistent with insulin concentrations, actively administered insulin resulted in a significant decrease of blood glucose levels. Additionally, insertion and liquid injection was verified on human skin. This study shows the feasibility of a patch-like system with on-board liquid storage and dispensing capability. The proposed device represents a first step towards painless and convenient administration of macromolecular drugs such as insulin or vaccines.

  • 6. Xie, Yao
    et al.
    Guo, Bin
    Xu, Luzhou
    Li, Jian
    Stoica, Petre
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Multistatic adaptive microwave imaging for early breast cancer detection2006In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 53, no 8, p. 1647-1657Article in journal (Refereed)
    Abstract [en]

    We propose a new multistatic adaptive microwave imaging (MAMI) method for early breast cancer detection. MAMI is a two-stage robust Capon beamforming (RCB) based image formation algorithm. MAMI exhibits higher resolution, lower sidelobes, and better noise and interference rejection capabilities than the existing approaches. The effectiveness of using MAMI for breast cancer detection is demonstrated via a simulated 3-D breast model and several numerical examples.

  • 7. Xu, L
    et al.
    Stoica, Peter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
    Li, Jian
    Bressler, S
    Shao, X
    Ding, M
    ASEO: a method for the simultaneous estimation of single-trial event-related potentials and ongoing brain activities2009In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 56, no 1, p. 111-121Article in journal (Refereed)
    Abstract [en]

    Cognitive functions are often studied by recording electric potentials from the brain over repeated presentations of a sensory stimulus or repeated performance of a motor action. Each repetition is called a trial. Recent work has demonstrated that contrary to the traditional view, the event-related potential (ERP) can vary from trial to trial and the background ongoing activity often contains rich information about the cognitive state of the brain. Based on such a variable signal plus ongoing activity model, an iterative parameter estimation method is proposed in which both the single-trial parameters of the ERP and the autoregressive representation of the ongoing activity are obtained simultaneously. This technique, referred to as the analysis of single-trial ERP and ongoing activities method, is first tested on simulation examples, and then applied to the local field potential recordings from monkeys performing a visuomotor task.

  • 8.
    Öberg, Tommy
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Electronics.
    A circuit for contact monitoring in electrocardiography1982In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. BME-29, no 5Article in journal (Refereed)
1 - 8 of 8
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