Classification of EEG Based BCI Signals Imagined Hand Closing and Opening


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YAVUZ E. , Aydemir Ö.

2017 40th International Conference on Telecommunications and Signal Processing (TSP), Barcelona, Spain, 5 - 07 July 2017, pp.1-4

  • Publication Type: Conference Paper / Full Text
  • City: Barcelona
  • Country: Spain
  • Page Numbers: pp.1-4

Abstract

Brain-computer interfaces allow people to manage
electronic devices such as computers without using their motor
nervous system. When the brain is in a function, nerve cells in the
brain communicate with each other with electrochemical
interactions. Electroencephalogram (EEG) signals are recorded
with the aid of electrodes during this function of the brain. These
signals enable interaction between people and electronic devices.
This interaction forms the basis of brain computer interface
(BCI) systems which facilitates lives of paralyzed patients who do
not have any problems with their cognitive functioning.
Therefore, for high-performance BCI systems, pre-processing
technique and classification method applied to these signals and
features extracted from these signals are crucial. In this study, we
studied a new EEG data set recorded from 29 people during
imagination of hand opening/closing movement. While movingaverage
filter was used a pre-processing technique, the features
were extracted by Hilbert Transform and Mean Derivative.
Afterwards, extracted features were classified by k-nearest
neighbor method. Average classification accuracy (CA) with preprocessing
was achieved 82.23%, which was 12.78% higher than
the average CA obtained by unprocessed EEG data set and
16.63% greater than the previous works reported in the
literature. The achieved results showed that the proposed method
has a great potential to be applied general with a highperformance
in general.