The BrainLiner Platform for Exploring Time-Aligned Neurophysiological Data
-
1
ATR, Computational Neuroscience Laboratories, Japan
BrainLiner (http://brainliner.jp/) is a web-based platform for sharing and exploring time-aligned brain and behavioral data. Performing experiments and collecting data come with a large monetary and resource cost, thus it is an important responsibility to share data for both the advancement of science and the validation of results.
Whereas contemporary database and repository sites ignore or treat behavioral and experimental task data with a lower priority than brain activity data, BrainLiner aims to enable sharing of data to support modern, data-driven neuroscience by treating both brain activity and behavioral data with equal salience. A unified data format that aligns brain activity and behavioral data on a time axis, enables analysis and ease of use. Once time-aligned, the brain and behavioral data can then be used for further analyses, such as neural decoding. The format also is built around HDF5, allowing it to be compatible across programming languages and computing environments, and uses well-defined schema such that all the properties describing data (e.g., recording modality, sampling rate, etc.) have specific meanings that are machine-readable. This lends itself towards automated analyses across large volumes of data.
The standardized BrainLiner data format also enables BrainLiner to let users explore data from within the web browser. The BrainLiner Data Explorer allows users to preview data files within the browser, before investing their time to download the data and analyze it locally. The Data Explorer uses WebGL for fast viewing of high-resolution data in an interactive way.
A data-driven similarity search is also integrated into the Data Explorer. For data files containing electrocorticography (ECoG) or electroencephalography (EEG) recording data, ECoG and EEG channels are split into time windows and the pairwise similarity all the time windows within a file are calculated based on correlations between spectral powers. Time windows that are highly correlated are marked as similar and stored in an index for quick retrieval. In this way, queries for similar time windows of data can be executed within a short amount of time, allowing users to explore similar time windows within the Data Explorer, just by clicking and dragging to enter queries. This can help users to find patterns within a data file and determine if they are interested in downloading the file to study it further.
Acknowledgements
SRPBS, MEXT
Keywords:
Database Systems,
data sharing,
neuroinformatics,
machine learning applied to neuroscience,
data search
Conference:
Neuroinformatics 2015, Cairns, Australia, 20 Aug - 22 Aug, 2015.
Presentation Type:
Demo, not to be considered for oral presentation
Topic:
General neuroinformatics
Citation:
Takemiya
M,
Majima
K,
Tsukamoto
M and
Kamitani
Y
(2015). The BrainLiner Platform for Exploring Time-Aligned Neurophysiological Data.
Front. Neurosci.
Conference Abstract:
Neuroinformatics 2015.
doi: 10.3389/conf.fnins.2015.91.00026
Copyright:
The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers.
They are made available through the Frontiers publishing platform as a service to conference organizers and presenters.
The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated.
Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed.
For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions.
Received:
08 Apr 2015;
Published Online:
05 Aug 2015.
*
Correspondence:
Mr. Makoto Takemiya, ATR, Computational Neuroscience Laboratories, Souraku-gun, Kyoto, 619-0288, Japan, mtakemiya@gmail.com