Abstract
The modern developments in neural science like neuroimaging and neuro sensing technologies have increase the size of neurological data, rate of neurological data generation, and variation in neuroscience data. These are vital role players for “Neuroscience Big data”. For statistically informative datasets in terms of size, with greater time scale and colossal number of attributes, the Neuroscience community for research can develop varied type of experiments using such data [1]. With the development of many data driven research techniques, the understanding for complex neurological disorder can be advance. It will also help in the field of brain networks with model long term effects of brain injury. Tools for neuroinformatics data processing and analysing are available but they are not capable to bring about huge volume of neuroscience data, which makes it hard for researchers to advance their work due to lack of capably control over this available data. So in this paper we have analysed mainly three big data techniques like map reduce, spark and pig to check their most suitability in the field of neuroscience test. Analysis shows that out of this three methods NeuroPigPen method explained in [1] is the best to deal with the challenges raised by large-scale electrophysiological signal data.
Authors
