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Towards a Highly Accurate Mental Activity Detection by Electroencephalography Sensor Networks
KTH, School of Electrical Engineering (EES), Automatic Control.
2012 (English)Student paper other, 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The possibility to detect reliably human brain signals by small sensors can have

substantial impact in healthcare, training, and rehabilitation. This Master the-

sis studies Electroencephalography (EEG) wireless sensors, and the properties

of their signals. The main goal is to investigate the problem of data interpre-

tation accuracy. The measurements provided by small wireless EEG sensors

show high variability and high noises, which makes it dicult to interpret the

brain signals. The analysis is further exacerbated by the diculty in statistical

modeling of these signals. This work presents an attempt to a simple statistical

modeling of brain signals. Then, based on such a modeling, an optimal data

fusion rule of sensors readings is proposed so to reach a high accuracy in the

signal's interpretation. An experimental implementation of the data fusion by

real EEG wireless sensors is developed. The experimental results show that the

fusion rule provides an error probability of nearly 25% in detecting correctly

brain signals. It is concluded that substantial improvements have still to be

done to understand the statistical properties of signals and develop optimal

decision rules for the detection.

Place, publisher, year, edition, pages
2012. , 139 p.
EES Examensarbete / Master Thesis, XR-EE-RT 2012:004
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
URN: urn:nbn:se:kth:diva-98873OAI: diva2:539685
Available from: 2012-09-05 Created: 2012-07-04 Last updated: 2012-09-05Bibliographically approved

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