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close this section of the library Electroencephalography | Mathematical models. Brain-computer interfaces. Computational intelligence.


View the PDF document EEG signal classification and its application to brain computer interface systems using computational intelligence techniques
Author: Kumar, Shiu
Institution: The University of the South Pacific
Award: Ph.D.
Subject: Electroencephalography | Mathematical models. Brain-computer interfaces. Computational intelligence.
Date: 2019
Call No.: Pac QP 376 .5 .K86 2019
BRN: 470356
Copyright:10-20% of this thesis may be copied without the authors written permission

Abstract: Brain computer interface (BCI) has become one of the hot research topics in the field of machine learning and pattern recognition. It has been mostly used for applications such as medical for diagnosis of seizure, rehabilitation, and entertainment. For BCI applications, electroencephalography (EEG) signal has become the fundamental way of communicating to a computer. EEG signals are usually acquired using non-invasive sensors as they are cheap and affordable, portable, and do not require surgery. Non-invasive EEG sensors are placed around the scalp, which records changes in the electrical activities of the brain. However, signals recorded using non-invasive sensors have low signal to noise ratio (SNR).
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