BioSig: The Free and Open Source Software Library for Biomedical Signal Processing

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

Studies in Health Technology and Informatics

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

TSDF: A simple yet comprehensive, unified data storage and exchange format standard for digital biosensor data in health applications

Digital sensors are increasingly being used to monitor the change over time of physiological processes in biological health and disease, often using wearable devices. This generates very large amounts of digital sensor data, for which, a consensus on a common storage, exchange and archival data format standard, has yet to be reached. To address this gap, we propose Time Series Data Format (TSDF): a unified, standardized format for storing all types of physiological sensor data, across diverse disease areas. We pose a series of format design criteria and review in detail current storage and exchange formats. When judged against these criteria, we find these current formats lacking, and propose a very simple, intuitive standard for both numerical sensor data and metadata, based on raw binary data and JSON-format text files, for sensor measurements/timestamps and metadata, respectively. By focusing on the common characteristics of diverse biosensor data, we define a set of necessary and sufficient metadata fields for storing, processing, exchanging, archiving and reliably interpreting, multi-channel biological time series data. Our aim is for this standardized format to increase the interpretability and exchangeability of data, thereby contributing to scientific reproducibility in studies where digital biosensor data forms a key evidence base

Stimfit: Quantifying electrophysiological data with Python

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

Neo: an object model for handling electrophysiology data in multiple formats

Neuroscientists use many different software tools to acquire, analyze and visualize electrophysiological signals. However, incompatible data models and file formats make it difficult to exchange data between these tools. This reduces scientific productivity, renders potentially useful analysis methods inaccessible and impedes collaboration between labs. A common representation of the core data would improve interoperability and facilitate data-sharing. To that end, we propose here a language-independent object model, named “Neo,” suitable for representing data acquired from electroencephalographic, intracellular, or extracellular recordings, or generated from simulations. As a concrete instantiation of this object model we have developed an open source implementation in the Python programming language. In addition to representing electrophysiology data in memory for the purposes of analysis and visualization, the Python implementation provides a set of input/output (IO) modules for reading/writing the data from/to a variety of commonly used file formats. Support is included for formats produced by most of the major manufacturers of electrophysiology recording equipment and also for more generic formats such as MATLAB. Data representation and data analysis are conceptually separate: it is easier to write robust analysis code if it is focused on analysis and relies on an underlying package to handle data representation. For that reason, and also to be as lightweight as possible, the Neo object model and the associated Python package are deliberately limited to representation of data, with no functions for data analysis or visualization. Software for neurophysiology data analysis and visualization built on top of Neo automatically gains the benefits of interoperability, easier data sharing and automatic format conversion; there is already a burgeoning ecosystem of such tools. We intend that Neo should become the standard basis for Python tools in neurophysiology.EC/FP7/269921/EU/Brain-inspired multiscale computation in neuromorphic hybrid systems/BrainScaleSDFG, 103586207, GRK 1589: Verarbeitung sensorischer Informationen in neuronalen SystemenBMBF, 01GQ1302, Nationaler Neuroinformatik Knote

Electroencephalographic Signal Processing and Classification Techniques for Noninvasive Motor Imagery Based Brain Computer Interface

In motor imagery (MI) based brain-computer interface (BCI), success depends on reliable processing of the noisy, non-linear, and non-stationary brain activity signals for extraction of features and effective classification of MI activity as well as translation to the corresponding intended actions. In this study, signal processing and classification techniques are presented for electroencephalogram (EEG) signals for motor imagery based brain-computer interface. EEG signals have been acquired placing the electrodes following the international 10-20 system. The acquired signals have been pre-processed removing artifacts using empirical mode decomposition (EMD) and two extended versions of EMD, ensemble empirical mode decomposition (EEMD), and multivariate empirical mode decomposition (MEMD) leading to better signal to noise ratio (SNR) and reduced mean square error (MSE) compared to independent component analysis (ICA). EEG signals have been decomposed into independent mode function (IMFs) that are further processed to extract features like sample entropy (SampEn) and band power (BP). The extracted features have been used in support vector machines to characterize and identify MI activities. EMD and its variants, EEMD, MEMD have been compared with common spatial pattern (CSP) for different MI activities. SNR values from EMD, EEMD and MEMD (4.3, 7.64, 10.62) are much better than ICA (2.1) but accuracy of MI activity identification is slightly better for ICA than EMD using BP and SampEn. Further work is outlined to include more features with larger database for better classification accuracy

BioPyC, an Open-Source Python Toolbox for Offline Electroencephalographic and Physiological Signals Classification

International audienceResearch on brain–computer interfaces (BCIs) has become more democratic in recent decades, and experiments using electroencephalography (EEG)-based BCIs has dramatically increased. The variety of protocol designs and the growing interest in physiological computing require parallel improvements in processing and classification of both EEG signals and bio signals, such as electrodermal activity (EDA), heart rate (HR) or breathing. If some EEG-based analysis tools are already available for online BCIs with a number of online BCI platforms (e.g., BCI2000 or OpenViBE), it remains crucial to perform offline analyses in order to design, select, tune, validate and test algorithmsbefore using them online. Moreover, studying and comparing those algorithms usually requires expertise in programming, signal processing and machine learning, whereas numerous BCI researchers come from other backgrounds with limited or no training in such skills. Finally, existing BCI toolboxes are focused on EEG and other brain signals but usually do not include processing tools for other bio signals. Therefore, in this paper, we describe BioPyC, a free, open-source and easy-to-use Python platform for offline EEG and biosignal processing and classification. Based on an intuitive and well-guided graphical interface, four main modules allow the user to follow the standard steps of the BCI process without any programming skills: (1) reading different neurophysiological signal data formats, (2) filtering and representing EEG and bio signals, (3) classifying them, and (4) visualizing and performing statistical tests on the results. We illustrate BioPyC use on four studies, namely classifying mental tasks, the cognitive workload, emotions and attention states from EEG signals

Extraction of ECGs for twin pregnancies

The wellbeing of a fetus or fetuses can be monitored by the fetal heart rate (fHR). There are several proposed methods for fHR monitoring; these include fetal phonocardiography (fPCG), fetal cardiography (fCTG) and fetal magnetocardiogram (fMCG). Although, according to the research reviewed, none of these methods are ideal for monitoring or estimating fHR. The fPCG method is highly sensitive to noise and can only be used late in the pregnancy. With fCTG, the ultrasound transducer used for measuring the fHR needs to be properly aligned, otherwise the maternal heart rate (mHR) can be recorded instead of the fHR. In addition, the ultrasound high frequency exposure is not completely proven to be safe for the fetus. fMCG can detect fHR very accurately in comparison to the other methods but the method is unwieldy and expensive; thus not widely used in a clinical environment. Therefore, there is a need for technology which would be able to provide more information about the cardiac health of a fetus, delivered in a cost-effective, streamlined manner. Based on the research reviewed and captured within this dissertation, non-invasive fetal electrocardiography (fECG) has been identified as a promising fetal cardiac monitoring method and if researched further, has the potential to become the next mainstream approach for monitoring fetal health. Within this dissertation, the fECG extraction methods have been explored and the findings captured. The research revealed that the fECG method can be used from early stages of pregnancy (20 weeks gestational age onwards). It is relatively low cost and does not necessarily require a highly skilled user. Continuous monitoring is also possible. The main challenge identified when using the non-invasive fECG extraction method is poor Signal-to-Noise Ratio (SNR) of the fECG signal on the abdominal signal which consists of fECG, maternal ECG (mECG) and noise. Eleven different fECG extraction methods were tested as part of this dissertation. The extraction methods were based on Adaptive Methods (AM), Template Subtraction (TS)or Blind Source Separation (BSS). Synthetic test signals were used for the testing the methods. The test signals included five different noise levels across seven different single pregnancy physiological cases and one twin pregnancy case. Each recording included 34 channels (32 abdominal and two maternal reference channels). For single pregnancy cases all of the extraction methods were able to extract the fECG from the test signals with varying degrees of success. Overall, the BSS-JADE method was the top performing method for single pregnancy cases getting a median F1 score of 99.85%. Furthermore, the twin pregnancy case was tested using BSS methods. The BSS FastICA algorithm using symmetric approach was the top performing method for the twin pregnancy case receiving a median F1 score of 99.93%

Interface cérebro-computador não invasiva baseada em OpenVibe

O Objectivo desta dissertação é desenvolver uma Interface Cérebro – Computador (ICC) não invasiva baseada em sinais de Electroencefalograma, no ambiente computacional OpenVibe. Os sistemas ICC congregam várias áreas distintas de conhecimento. Dessa forma, foi necessário efectuar um estudo do estado de arte, de forma a conhecer o ponto presente relativo ás várias variáveis que contribuem para o desempenho global dos sistemas deste tipo. Procedeu-se a uma descrição do ambiente computacional OpenVibe e do driver para o hardware de aquisição de EEG utilizado na aquisição de dados. Foram efectuados dois paradigmas de ICC, a Imaginação Motora e a P300 implementados primeiro offline com dados fornecidos para o OpenVibe e segundo em online com aquisição e processamento em tempo - real. Os resultados dos testes são apresentados para os dados fornecidos e adquiridos e são relativos ao funcionamento do sistema em geral.The objective of this dissertation is to develop a Brain - Computer Interface (BCI) based on non-invasive signs of Electroencephalogram in the computing environment OpenVibe. Systems BCI congregate various distinct knowledge areas. Of this form, necessary was going to effectuate a state-owned study of art, of form various variable what contribute for the global performance of the systems gave type to the point know present relative ace. One proceeded to a description of the ambient computational OpenVibe and the driver for the EEG hardware used in the acquisition of data. We made two paradigms of BCI Motor Imagination and P300 implemented first with offline data to OpenVibe and second in online acquisition and processing in real - time. The upshots of the tests are presented for the supplied and acquired data and are relative to the functioning of the system generally