Non-invasive electroencephalography signals classification using rough neural network
C. Velayutham · International Journal of Computational Biology and Drug Design · 2015
This paper proposes a rough neural network (RNN) classification method for mental imagery, multi-class EEG data-set. The raw EEG potentials were first spatially filtered by means of a surface Laplacian. Then, every 62.5 ms (16 times per second), the power spectral density (PSD) in the band 8-30 Hz was estimated over the last second of data with a frequency resolution of 2 Hz for the eight centro-parietal channels C3, Cz, C4, CP1, CP2, P3, Pz and P4. As a result, an EEG sample is a 96-dimensional features vector. The features are normalised, and then the classification is performed using RNN. The experiment is performed using the BCI Competition III, Dataset V. The experimental results of RNN is compared with the back-propagation neural network algorithm. This paper obtains marginal improvement for the proposed RNN classification method in terms of classifying accuracy.