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Method for EEG signals pattern recognition in embedded systems

Method for EEG signals pattern recognition in embedded systems

Kawala-Janik, Aleksandra, Pelc, Mariusz and Podpora, Michal (2015) Method for EEG signals pattern recognition in embedded systems. Elektronika ir Elektrotechnika, 21 (3). pp. 3-9. ISSN 1392-1215 (Print), 2029-5731 (Online) (doi:https://doi.org/10.5755/j01.eee.21.3.9918)

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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.

Item Type: Article
Uncontrolled Keywords: Brain-computer interaction, Control, Embedded systems, Signal processing
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Related URLs:
Last Modified: 20 Nov 2017 10:08
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/13794

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