Method for EEG Signals Pattern Recognition in Embedded Systems

Aleksandra Kawala-Janik, Mariusz Pelc, Michal Podpora

Abstract


The rapid increase of many disorders, such as stroke, amyotrophic lateral sclerosis (ALS) or various other spinal cord injuries, strongly affects the society. This results in growing need for the improvement of communication methods in order to enable quick and efficient interaction with the environment, where in some particularly difficult cases this may be the only possible communication way. Therefore Brain-Computer Interfaces (BCI) seem to be an excellent solution not only for the, above mentioned - severe cases, but also for non-disabled, healthy users. The main purpose for the research presented in this paper was to invent easy, but efficient method for the analysis of the EEG signals and its implementation for the control purpose. As the implementation of EEG signals in BCI systems has become recently more and more popular within the last few years, lots of similar solutions have been developed. The method developed by the authors of this paper presents an innovative approach in analysis of the electroencephalographic signals. The proposed method is novel not only because of its efficiency, but also because of the choice of the applied equipment. The signal processing method was implemented on an embedded platform, so all the limitations of the embedded systems had to be taken into consideration. The proposed solution also enables customisation of the analysing criteria by using a threshold function in order to enable adaptation for various specific applications. In the carried out study only signals with limited information have been processed. The invented method is based on basic mathematical operations only. Neither filtering nor sophisticated signal processing methods were used.

DOI: http://dx.doi.org/10.5755/j01.eee.21.3.9918


Keywords


Brain-computer interaction; control; embedded systems; signal processing.

Full Text: PDF

Refbacks

  • There are currently no refbacks.


Print ISSN: 1392-1215
Online ISSN: 2029-5731