Abstract
A new research methodology named Brain Computer Interface (BCI) studies novel human-computer interactions; by means of BCI electronics devices, paralyzed patients are able to interact with the environment using no muscular contractions. This technique provides an external electronics support to all persons with severe motor disabilities, by acquiring in continuous mode the electroencephalogram (EEG) signals and operating some processing to control a computer or other domotics devices. Patients are so allowed to control external devices or to communicate simple messages through the computer, just concentrating their attention either on codified movements or on a letter or icon on a digital keyboard. The use of a customized and optimized spatial filtering technique embedded in the BCI system, based on the detection of the Electroencephalographic activity, improves the accuracy of BCI system itself, thanks to the explicit separation of the signal activity of interest from artefact signals. In this chapter, after an overview of the state-of-the-art research on BCI systems, the spatial filtering problem in EEG signals acquisition will be illustrated. In particular, a spatial filtering algorithm, known as ICA (Independent Component Analysis) and its application will be discussed. Finally, the design and implementation of an embedded system for EEG signals acquisition and real-time processing for BCI applications will be presented. The system is based onto a very performing and reconfigurable hardware platform. Moreover ICA algorithm has been implemented for noise reduction and artifacts removal.
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Palumbo, A. et al. (2010). An Embedded System for EEG Acquisition and Processing for Brain Computer Interface Applications. In: Lay-Ekuakille, A., Mukhopadhyay, S.C. (eds) Wearable and Autonomous Biomedical Devices and Systems for Smart Environment. Lecture Notes in Electrical Engineering, vol 75. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15687-8_7
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DOI: https://doi.org/10.1007/978-3-642-15687-8_7
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