Naslov
Classification of Wavelet Transformed EEG Signals with Neural Network for Imagined Mental and Motor Tasks

Autori
Tolić, Martina ; Jović, Franjo

Izvornik
Kinesiology : international journal of fundamental and applied kinesiology (1331-1441) 45 (2013), 1; 130-138

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
brain-computer interface; mental tasks; motor imagery; independent component analysis; discrete wavelet transform

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Biologija, Računarstvo

POVEZANOST RADA