@inproceedings{630, abstract = {Background: Standards have become available to share semantically encoded vital parameters from medical devices, as required for example by personal healthcare records. Standardised sharing of biosignal data largely remains open. Objectives: The goal of this work is to explore available biosignal file format and data exchange standards and profiles, and to conceptualise end-To-end solutions. Methods: The authors reviewed and discussed available biosignal file format standards with other members of international standards development organisations (SDOs). Results: A raw concept for standards based acquisition, storage, archiving and sharing of biosignals was developed. The GDF format may serve for storing biosignals. Signals can then be shared using FHIR resources and may be stored on FHIR servers or in DICOM archives, with DICOM waveforms as one possible format. Conclusion: Currently a group of international SDOs (e.g. HL7, IHE, DICOM, IEEE) is engaged in intensive discussions. This discussion extends existing work that already was adopted by large implementer communities. The concept presented here only reports the current status of the discussion in Austria. The discussion will continue internationally, with results to be expected over the coming years.}, author = {Sauermann, Stefan and David, Veronika and Schlögl, Alois and Egelkraut, Reinhard and Frohner, Matthias and Pohn, Birgit and Urbauer, Philipp and Mense, Alexander}, isbn = {978-161499758-0}, location = {Vienna, Austria}, pages = {356 -- 362}, publisher = {IOS Press}, title = {{Biosignals standards and FHIR: The way to go}}, doi = {10.3233/978-1-61499-759-7-356}, volume = {236}, year = {2017}, } @inproceedings{12903, author = {Schlögl, Alois and Stadlbauer, Stephan}, booktitle = {AHPC16 - Austrian HPC Meeting 2016}, location = {Grundlsee, Austria}, pages = {37}, publisher = {VSC - Vienna Scientific Cluster}, title = {{High performance computing at IST Austria: Modelling the human hippocampus}}, year = {2016}, } @article{1350, abstract = {The hippocampal CA3 region plays a key role in learning and memory. Recurrent CA3–CA3 synapses are thought to be the subcellular substrate of pattern completion. However, the synaptic mechanisms of this network computation remain enigmatic. To investigate these mechanisms, we combined functional connectivity analysis with network modeling. Simultaneous recording fromup to eight CA3 pyramidal neurons revealed that connectivity was sparse, spatially uniform, and highly enriched in disynaptic motifs (reciprocal, convergence,divergence, and chain motifs). Unitary connections were composed of one or two synaptic contacts, suggesting efficient use of postsynaptic space. Real-size modeling indicated that CA3 networks with sparse connectivity, disynaptic motifs, and single-contact connections robustly generated pattern completion.Thus, macro- and microconnectivity contribute to efficient memory storage and retrieval in hippocampal networks.}, author = {Guzmán, José and Schlögl, Alois and Frotscher, Michael and Jonas, Peter M}, journal = {Science}, number = {6304}, pages = {1117 -- 1123}, publisher = {American Association for the Advancement of Science}, title = {{Synaptic mechanisms of pattern completion in the hippocampal CA3 network}}, doi = {10.1126/science.aaf1836}, volume = {353}, year = {2016}, } @article{2230, abstract = {Intracellular electrophysiological recordings provide crucial insights into elementary neuronal signals such as action potentials and synaptic currents. Analyzing and interpreting these signals is essential for a quantitative understanding of neuronal information processing, and requires both fast data visualization and ready access to complex analysis routines. To achieve this goal, we have developed Stimfit, a free software package for cellular neurophysiology with a Python scripting interface and a built-in Python shell. The program supports most standard file formats for cellular neurophysiology and other biomedical signals through the Biosig library. To quantify and interpret the activity of single neurons and communication between neurons, the program includes algorithms to characterize the kinetics of presynaptic action potentials and postsynaptic currents, estimate latencies between pre- and postsynaptic events, and detect spontaneously occurring events. We validate and benchmark these algorithms, give estimation errors, and provide sample use cases, showing that Stimfit represents an efficient, accessible and extensible way to accurately analyze and interpret neuronal signals.}, author = {Guzmán, José and Schlögl, Alois and Schmidt Hieber, Christoph}, issn = {1662-5196}, journal = {Frontiers in Neuroinformatics}, number = {FEB}, publisher = {Frontiers Research Foundation}, title = {{Stimfit: Quantifying electrophysiological data with Python}}, doi = {10.3389/fninf.2014.00016}, volume = {8}, year = {2014}, } @article{1890, abstract = {To search for a target in a complex environment is an everyday behavior that ends with finding the target. When we search for two identical targets, however, we must continue the search after finding the first target and memorize its location. We used fixation-related potentials to investigate the neural correlates of different stages of the search, that is, before and after finding the first target. Having found the first target influenced subsequent distractor processing. Compared to distractor fixations before the first target fixation, a negative shift was observed for three subsequent distractor fixations. These results suggest that processing a target in continued search modulates the brain's response, either transiently by reflecting temporary working memory processes or permanently by reflecting working memory retention.}, author = {Körner, Christof and Braunstein, Verena and Stangl, Matthias and Schlögl, Alois and Neuper, Christa and Ischebeck, Anja}, journal = {Psychophysiology}, number = {4}, pages = {385 -- 395}, publisher = {Wiley-Blackwell}, title = {{Sequential effects in continued visual search: Using fixation-related potentials to compare distractor processing before and after target detection}}, doi = {10.1111/psyp.12062}, volume = {51}, year = {2014}, } @article{493, abstract = {The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community. As experienced already in prior competitions not only scientists from the narrow field of BCI compete, but scholars with a broad variety of backgrounds and nationalities. They include high specialists as well as students.The goals of all BCI competitions have always been to challenge with respect to novel paradigms and complex data. We report on the following challenges: (1) asynchronous data, (2) synthetic, (3) multi-class continuous data, (4) sessionto-session transfer, (5) directionally modulated MEG, (6) finger movements recorded by ECoG. As after past competitions, our hope is that winning entries may enhance the analysis methods of future BCIs.}, author = {Tangermann, Michael and Müller, Klaus and Aertsen, Ad and Birbaumer, Niels and Braun, Christoph and Brunner, Clemens and Leeb, Robert and Mehring, Carsten and Miller, Kai and Müller Putz, Gernot and Nolte, Guido and Pfurtscheller, Gert and Preissl, Hubert and Schalk, Gerwin and Schlögl, Alois and Vidaurre, Carmen and Waldert, Stephan and Blankertz, Benjamin}, journal = {Frontiers in Neuroscience}, publisher = {Frontiers Research Foundation}, title = {{Review of the BCI competition IV}}, doi = {10.3389/fnins.2012.00055}, volume = {6}, year = {2012}, } @article{2954, abstract = {Spontaneous postsynaptic currents (PSCs) provide key information about the mechanisms of synaptic transmission and the activity modes of neuronal networks. However, detecting spontaneous PSCs in vitro and in vivo has been challenging, because of the small amplitude, the variable kinetics, and the undefined time of generation of these events. Here, we describe a, to our knowledge, new method for detecting spontaneous synaptic events by deconvolution, using a template that approximates the average time course of spontaneous PSCs. A recorded PSC trace is deconvolved from the template, resulting in a series of delta-like functions. The maxima of these delta-like events are reliably detected, revealing the precise onset times of the spontaneous PSCs. Among all detection methods, the deconvolution-based method has a unique temporal resolution, allowing the detection of individual events in high-frequency bursts. Furthermore, the deconvolution-based method has a high amplitude resolution, because deconvolution can substantially increase the signal/noise ratio. When tested against previously published methods using experimental data, the deconvolution-based method was superior for spontaneous PSCs recorded in vivo. Using the high-resolution deconvolution-based detection algorithm, we show that the frequency of spontaneous excitatory postsynaptic currents in dentate gyrus granule cells is 4.5 times higher in vivo than in vitro.}, author = {Pernia-Andrade, Alejandro and Goswami, Sarit and Stickler, Yvonne and Fröbe, Ulrich and Schlögl, Alois and Jonas, Peter M}, journal = {Biophysical Journal}, number = {7}, pages = {1429 -- 1439}, publisher = {Biophysical Society}, title = {{A deconvolution based method with high sensitivity and temporal resolution for detection of spontaneous synaptic currents in vitro and in vivo}}, doi = {10.1016/j.bpj.2012.08.039}, volume = {103}, year = {2012}, } @article{490, abstract = {BioSig is an open source software library for biomedical signal processing. The aim of the BioSig project is to foster research in biomedical signal processing by providing free and open source software tools for many different application areas. Some of the areas where BioSig can be employed are neuroinformatics, brain-computer interfaces, neurophysiology, psychology, cardiovascular systems, and sleep research. Moreover, the analysis of biosignals such as the electroencephalogram (EEG), electrocorticogram (ECoG), electrocardiogram (ECG), electrooculogram (EOG), electromyogram (EMG), or respiration signals is a very relevant element of the BioSig project. Specifically, BioSig provides solutions for data acquisition, artifact processing, quality control, feature extraction, classification, modeling, and data visualization, to name a few. In this paper, we highlight several methods to help students and researchers to work more efficiently with biomedical signals. }, author = {Schlögl, Alois and Vidaurre, Carmen and Sander, Tilmann}, journal = {Computational Intelligence and Neuroscience}, publisher = {Hindawi Publishing Corporation}, title = {{BioSig: The free and open source software library for biomedical signal processing}}, doi = {10.1155/2011/935364}, volume = {2011}, year = {2011}, } @inbook{14983, abstract = {This chapter tackles a difficult challenge: presenting signal processing material to non-experts. This chapter is meant to be comprehensible to people who have some math background, including a course in linear algebra and basic statistics, but do not specialize in mathematics, engineering, or related fields. Some formulas assume the reader is familiar with matrices and basic matrix operations, but not more advanced material. Furthermore, we tried to make the chapter readable even if you skip the formulas. Nevertheless, we include some simple methods to demonstrate the basics of adaptive data processing, then we proceed with some advanced methods that are fundamental in adaptive signal processing, and are likely to be useful in a variety of applications. The advanced algorithms are also online available [30]. In the second part, these techniques are applied to some real-world BCI data.}, author = {Schlögl, Alois and Vidaurre, Carmen and Müller, Klaus-Robert}, booktitle = {Brain-Computer Interfaces}, editor = {Graimann, Bernhard and Pfurtscheller, Gert and Allison, Brendan}, isbn = {9783642020902}, issn = {1612-3018}, pages = {331--355}, publisher = {Springer}, title = {{Adaptive Methods in BCI Research - An Introductory Tutorial}}, doi = {10.1007/978-3-642-02091-9_18}, year = {2010}, }