Moving Towards a Hardware Implementation of the Independent Component Analysis for Brain Computer Interfaces

Abstract

Brain computer interface (BCI) systems implement a communication path between human users and the external environment by translating physiological signals directly acquired from the brain into commands toward external peripherals. A lot of protocols have been implemented in the BCI field and a lot of analytical techniques and algorithms on the signals have been tested to improve the reliability of the information extracted from signals and then the performances of BCI systems. Independent component analysis (ICA) revealed to be a useful tool for analyzing data as it allows the separation of the signals in some independent sources which carry information about the different components of the signals themselves. However ICA is computationally expensive and some efforts should be done in order to maximize its results in terms of time spent for the analysis. A hardware implementation is now discussed which makes the ICA more useful for the online analysis typical of BCI systems.