What Should We Learn From... Brain Signal Analytics?

It is widely envisioned that brain data analytics will play a major role over the next decade in revealing the brain’s functional architecture and operational principles. Traditionally, such brain data has been analyzed from the approach of signal or image processing, but these outcomes likely were published in such journals as Human Brain Mapping and NeuroImage, outside the IEEE Signal Processing Society (SPS). The goal of the Special Issue of SPM on Brain Signal Analytics May 2016 is to bring together a diverse, but complementary set of tutorial/review articles demonstrating the importance of incorporating signal processing strategies into the advances in neuroimaging techniques, data analytics, and modeling for brain function. With this cluster, the editors would like to showcase the inherently interdisciplinary nature of brain mapping research and the intriguing related signal processing questions. This will bring engineers, physicists, and neurobiologists together to better understand brain data, encourage future brain data analytics and modeling submissions to IEEE SPS journals, and alert the signal processing community to the exciting potential of brain mapping—an area of growing significance.

This cluster comprises five articles from research groups in the United States, Canada, and Europe, includes researchers ranging from engineering to medicine, and provides a broad view of the recent advances regarding two important research directions in brain mapping: 1) brain connectivity and multimodal imaging and 2) multimodal data fusion. In addition to recent technical advances and developments, all of these articles present interesting medical applications and provide an outlook of future challenges in building a comprehensive understanding of brain mapping.

The editors believe the articles selected for this cluster offer a snapshot of the latest research and showcase the importance of signal processing in the exciting field of brain mapping and brain data analytics. The editors hope that this collection will help introduce more signal processing researchers into this area to accelerate its advancement.