Kinesiology, Vol. 45. No. 1., 2013.
Preliminary communication
Classification of wavelet transformed eeg signals with neural network for imagined mental and motor tasks
Martina Tolić
; Faculty of Electrical Engineering, University J.J. Strossmayer in Osijek, Croatia
Franjo Jović
; Faculty of Electrical Engineering, University J.J. Strossmayer in Osijek, Croatia
Abstract
Brain-computer interfaces (BCI) are devices that enable communication between a computer and humans by using brain activity as input signals. Brain imaging technology used in a BCI system is usually electroencephalography (EEG). In order to properly interpret brain activity, acquired signals from the brain have to be classified correctly. In this paper EEG signals are transformed by means of discrete wavelet transform. Thus the obtained signal features are used as inputs for a neural network classifier that should separate five different sets of EEG signals representing various mental tasks. Mean classification accuracy for the recognition of all five tasks was 90.75% and mean classification accuracy for the recognition of two tasks (baseline and any other mental task) was 99.87%. The same procedure was also used on the motor imagery dataset. A mean classification accuracy of 68.21% suggests alternative methods of feature extraction for motor imagery tasks.
Keywords
brain-computer interface; mental tasks; motor imagery; independent component analysis; discrete wavelet transform
Hrčak ID:
104591
URI
Publication date:
30.6.2013.
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