886 research outputs found
Computational Testing for Automated Preprocessing 2 : Practical Demonstration of a System for Scientific Data-Processing Workflow Management for High-Volume EEG
Existing tools for the preprocessing of EEG data provide a large choice of methods to suitably prepare and analyse a given dataset. Yet it remains a challenge for the average user to integrate methods for batch processing of the increasingly large datasets of modern research, and compare methods to choose an optimal approach across the many possible parameter configurations. Additionally, many tools still require a high degree of manual decision making for, e.g., the classification of artifacts in channels, epochs or segments. This introduces extra subjectivity, is slow, and is not reproducible. Batching and well-designed automation can help to regularize EEG preprocessing, and thus reduce human effort, subjectivity, and consequent error. The Computational Testing for Automated Preprocessing (CTAP) toolbox facilitates: (i) batch processing that is easy for experts and novices alike; (ii) testing and comparison of preprocessing methods. Here we demonstrate the application of CTAP to high-resolution EEG data in three modes of use. First, a linear processing pipeline with mostly default parameters illustrates ease-of-use for naive users. Second, a branching pipeline illustrates CTAP's support for comparison of competing methods. Third, a pipeline with built-in parameter-sweeping illustrates CTAP's capability to support data-driven method parameterization. CTAP extends the existing functions and data structure from the well-known EEGLAB toolbox, based on Matlab, and produces extensive quality control outputs. CTAP is available under MIT open-source licence from https://github.com/bwrc/ctap.Peer reviewe
A Comparison of Neuroelectrophysiology Databases
As data sharing has become more prevalent, three pillars - archives,
standards, and analysis tools - have emerged as critical components in
facilitating effective data sharing and collaboration. This paper compares four
freely available intracranial neuroelectrophysiology data repositories: Data
Archive for the BRAIN Initiative (DABI), Distributed Archives for
Neurophysiology Data Integration (DANDI), OpenNeuro, and Brain-CODE. These
archives provide researchers with tools to store, share, and reanalyze
neurophysiology data though the means of accomplishing these objectives differ.
The Brain Imaging Data Structure (BIDS) and Neurodata Without Borders (NWB) are
utilized by these archives to make data more accessible to researchers by
implementing a common standard. While many tools are available to reanalyze
data on and off the archives' platforms, this article features Reproducible
Analysis and Visualization of Intracranial EEG (RAVE) toolkit, developed
specifically for the analysis of intracranial signal data and integrated with
the discussed standards and archives. Neuroelectrophysiology data archives
improve how researchers can aggregate, analyze, distribute, and parse these
data, which can lead to more significant findings in neuroscience research.Comment: 25 pages, 8 figures, 1 tabl
brainlife.io: A decentralized and open source cloud platform to support neuroscience research
Neuroscience research has expanded dramatically over the past 30 years by
advancing standardization and tool development to support rigor and
transparency. Consequently, the complexity of the data pipeline has also
increased, hindering access to FAIR data analysis to portions of the worldwide
research community. brainlife.io was developed to reduce these burdens and
democratize modern neuroscience research across institutions and career levels.
Using community software and hardware infrastructure, the platform provides
open-source data standardization, management, visualization, and processing and
simplifies the data pipeline. brainlife.io automatically tracks the provenance
history of thousands of data objects, supporting simplicity, efficiency, and
transparency in neuroscience research. Here brainlife.io's technology and data
services are described and evaluated for validity, reliability,
reproducibility, replicability, and scientific utility. Using data from 4
modalities and 3,200 participants, we demonstrate that brainlife.io's services
produce outputs that adhere to best practices in modern neuroscience research
Data sharing in neuroimaging research
Significant resources around the world have been invested in neuroimaging studies of brain function and disease. Easier access to this large body of work should have profound impact on research in cognitive neuroscience and psychiatry, leading to advances in the diagnosis and treatment of psychiatric and neurological disease. A trend toward increased sharing of neuroimaging data has emerged in recent years. Nevertheless, a number of barriers continue to impede momentum. Many researchers and institutions remain uncertain about how to share data or lack the tools and expertise to participate in data sharing. The use of electronic data capture (EDC) methods for neuroimaging greatly simplifies the task of data collection and has the potential to help standardize many aspects of data sharing. We review here the motivations for sharing neuroimaging data, the current data sharing landscape, and the sociological or technical barriers that still need to be addressed. The INCF Task Force on Neuroimaging Datasharing, in conjunction with several collaborative groups around the world, has started work on several tools to ease and eventually automate the practice of data sharing. It is hoped that such tools will allow researchers to easily share raw, processed, and derived neuroimaging data, with appropriate metadata and provenance records, and will improve the reproducibility of neuroimaging studies. By providing seamless integration of data sharing and analysis tools within a commodity research environment, the Task Force seeks to identify and minimize barriers to data sharing in the field of neuroimaging
Data Sharing in Neuroimaging Research
Significant resources around the world have been invested in neuroimaging studies of brain function and disease. Easier access to this large body of work should have profound impact on research in cognitive neuroscience and psychiatry, leading to advances in the diagnosis and treatment of psychiatric and neurological disease. A trend toward increased sharing of neuroimaging data has emerged in recent years. Nevertheless, a number of barriers continue to impede momentum. Many researchers and institutions remain uncertain about how to share data or lack the tools and expertise to participate in data sharing. The use of electronic data capture (EDC) methods for neuroimaging greatly simplifies the task of data collection and has the potential to help standardize many aspects of data sharing. We review here the motivations for sharing neuroimaging data, the current data sharing landscape, and the sociological or technical barriers that still need to be addressed. The INCF Task Force on Neuroimaging Datasharing, in conjunction with several collaborative groups around the world, has started work on several tools to ease and eventually automate the practice of data sharing. It is hoped that such tools will allow researchers to easily share raw, processed, and derived neuroimaging data, with appropriate metadata and provenance records, and will improve the reproducibility of neuroimaging studies. By providing seamless integration of data sharing and analysis tools within a commodity research environment, the Task Force seeks to identify and minimize barriers to data sharing in the field of neuroimaging
Ubiquitous Integration and Temporal Synchronisation (UbilTS) framework : a solution for building complex multimodal data capture and interactive systems
Contemporary Data Capture and Interactive Systems (DCIS) systems are tied in with various
technical complexities such as multimodal data types, diverse hardware and software
components, time synchronisation issues and distributed deployment configurations. Building
these systems is inherently difficult and requires addressing of these complexities before the
intended and purposeful functionalities can be attained. The technical issues are often
common and similar among diverse applications.
This thesis presents the Ubiquitous Integration and Temporal Synchronisation (UbiITS)
framework, a generic solution to address the technical complexities in building DCISs. The
proposed solution is an abstract software framework that can be extended and customised to
any application requirements. UbiITS includes all fundamental software components,
techniques, system level layer abstractions and reference architecture as a collection to enable
the systematic construction of complex DCISs.
This work details four case studies to showcase the versatility and extensibility of UbiITS
framework’s functionalities and demonstrate how it was employed to successfully solve a
range of technical requirements. In each case UbiITS operated as the core element of each
application. Additionally, these case studies are novel systems by themselves in each of their
domains. Longstanding technical issues such as flexibly integrating and interoperating
multimodal tools, precise time synchronisation, etc., were resolved in each application by
employing UbiITS. The framework enabled establishing a functional system infrastructure in
these cases, essentially opening up new lines of research in each discipline where these
research approaches would not have been possible without the infrastructure provided by the
framework. The thesis further presents a sample implementation of the framework on a
device firmware exhibiting its capability to be directly implemented on a hardware platform.
Summary metrics are also produced to establish the complexity, reusability, extendibility,
implementation and maintainability characteristics of the framework.Engineering and Physical Sciences Research Council (EPSRC) grants - EP/F02553X/1, 114433 and 11394
Evidence combination for incremental decision-making processes
The establishment of a medical diagnosis is an incremental process highly fraught with uncertainty. At each step of this painstaking process, it may be beneficial to be able to quantify the uncertainty linked to the diagnosis and steadily update the uncertainty estimation using available sources of information, for example user feedback, as they become available. Using the example of medical data in general and EEG data in particular, we show what types of evidence can affect discrete variables such as a medical diagnosis and build a simple and computationally efficient evidence combination model based on the Dempster-Shafer theory
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