Data compression of EEG signals for artificial neural network classification

Brain – Computer interface (BCI) systems require intensive signal processing in order to form control signals for electronic devices. The majority of BCI systems work by reading and interpreting cortically evoked electro-potentials across the scalp via an electro-encephalogram (EEG). Feature extraction and classification are the main tasks in EEG signal processing. In this paper, we propose method to compress EEG data using discrete cosine transform (DCT). DCT takes correlated input data and concentrates its energy in just first few transform coefficients. This method is used as feature extraction step and allows reducing data size without losing important information. For classification we are using feed forward artificial neural network. Experimental results show that our proposed method does not lose the important information. We conclude that the method can be successfully used for the feature extraction. DOI: http://dx.doi.org/10.5755/j01.itc.42.3.1986