Publikation
A Dataflow-Based Mobile Brain Reading System on Chip with Supervised Online Calibration
Hendrik Wöhrle; Johannes Teiwes; Mario Michael Krell; Elsa Andrea Kirchner; Frank Kirchner
In: Congress Proceedings (http://www.neurotechnix.org/). International Congress on Neurotechnology, Electronics and Informatics (NEUROTECHNIX-2013), September 18-20, Vilamoura, Portugal, SCITEPRESS Digital Library, 9/2013.
Zusammenfassung
Brain activity is more and more used for innovative applications like Brain Computer Interfaces (BCIs). However,
in order to be able to use the brain activity, the related psychophysiological data has to be processed and
analyzed with sophisticated signal processing and machine learning methods. Usually these methods have to
be calibrated with subject-specific data before they can be used. Since future systems that implement these
methods need to be portable to be applied more flexible tight constraints regarding size, power consumption
and computing time have to be met. Field Programmable Gate Arrays (FPGAs) are a promising solution,
which are able to meet all the constraints at the same time. Here, we present an FPGA-based mobile system
for signal processing and classification. In addition to other systems, it is able to be calibrated and adapt at
runtime, which makes the acquisition of training data unnecessary.