The BrainLiner Platform for Sharing and Searching Time-Aligned Neurophysiological Data
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1
ATR, Computational Neuroscience Laboratories, Japan
BrainLiner (http://brainliner.jp/) is a platform for sharing time-aligned brain and behavioral data. BrainLiner and other databases support sharing brain activity data for the purposes of data re-use and scientific replication and verifiability. However, many other databases treat and store brain activity and behavioral data separately. For example, stimulus time course and motion capture are often stored in separate files from electrode data. This makes it cumbersome to analyze the neural representation of properties related to the task.
BrainLiner focuses on supporting contemporary data-driven neuroscience approaches, such as neural decoding, where the statistical relationship between brain activity and behavior can be used for practical applications such as uncovering the structure of information representation in the brain. As such, BrainLiner places equal emphasis on behavioral data as on brain activity data.
The data on BrainLiner are stored in a standardized file format. The explicit representation of brain activity and behavioral data that are time-aligned and represented in a standardized data format allows our platform to automatically process data in many ways. Users can preview our standardized file format from within a web browser, getting a glimpse at not only brain activity, but also information about the task or behavior of subjects. In addition, we can also perform more advanced analyses, such as data similarity search (Figure 1), directly in the browser.
Our data similarity search demonstrates the utility of using a common file format throughout a database. Our search method maps the similarity of time windows within a single data file. The search uses information about the modality of data to index only electrocorticography (ECoG) or electroencephalography (EEG) files. Indexed files are split into time windows and then the pairwise similarity between all time windows are calculated using an unsupervised method. This results in a large number of similarity scores. For efficiency, these values are then quantized by keeping only time windows that are correlated with p values of less than 0.05. Indices representing spans of similar time windows are then stored in a sparse index. This has the advantage of being able to respond to a user’s query in a very short time frame.
Keywords:
data sharing,
neuroinformatics,
Web Applications,
data search,
web services
Conference:
Neuroinformatics 2014, Leiden, Netherlands, 25 Aug - 27 Aug, 2014.
Presentation Type:
Demo, not to be considered for oral presentation
Topic:
Infrastructural and portal services
Citation:
Takemiya
M,
Majima
K,
Tsukamoto
M and
Kamitani
Y
(2014). The BrainLiner Platform for Sharing and Searching Time-Aligned Neurophysiological Data.
Front. Neuroinform.
Conference Abstract:
Neuroinformatics 2014.
doi: 10.3389/conf.fninf.2014.18.00016
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Received:
04 Apr 2014;
Published Online:
04 Jun 2014.
*
Correspondence:
Mr. Makoto Takemiya, ATR, Computational Neuroscience Laboratories, Souraku-gun, Kyoto, 619-0288, Japan, mtakemiya@gmail.com
Dr. Kei Majima, ATR, Computational Neuroscience Laboratories, Souraku-gun, Kyoto, 619-0288, Japan, majimajimajimajima@gmail.com
Dr. Mitsuaki Tsukamoto, ATR, Computational Neuroscience Laboratories, Souraku-gun, Kyoto, 619-0288, Japan, mitsuaki@atr.jp
Dr. Yukiyasu Kamitani, ATR, Computational Neuroscience Laboratories, Souraku-gun, Kyoto, 619-0288, Japan, kamitani@i.kyoto-u.ac.jp