N. Fuad and M. N. Taib and R. Jailani and M. E. Marwan
Brainwave Classification for Brain Balancing Index (BBI) via 3D EEG Model Using kNN Technique
1298 - 1302
2014
8
8
International Journal of Electrical and Computer Engineering
https://publications.waset.org/pdf/9998975
https://publications.waset.org/vol/92
World Academy of Science, Engineering and Technology
In this paper, the comparison between kNearest Neighbor (kNN) algorithms for classifying the 3D EEG model in brain balancing is presented. The EEG signal recording was conducted on 51 healthy subjects. Development of 3D EEG models involves preprocessing of raw EEG signals and construction of spectrogram images. Then, maximum PSD values were extracted as features from the model. There are three indexes for balanced brain; index 3, index 4 and index 5. There are significant different of the EEG signals due to the brain balancing index (BBI). Alphaα (8–13 Hz) and betaβ (13–30 Hz) were used as input signals for the classification model. The kNN classification result is 88.46 accuracy. These results proved that kNN can be used in order to predict the brain balancing application.
Open Science Index 92, 2014