Automatic brain state classification system using double channel of EEG signal from rat brain

In this paper, we aimed to develop a simple technique to classify brain states of rats, including Active, Inactive, REM, and NREM. Two EEG signals (from frontal and parietal cortices) were recorded to create EEG spectrums. The EEG spectrums created by Fast Fourier Transform (FFT) were separated into two sets; training set for the brain state model creation and testing set for experiment. The training set of each brain states which are manually classified as one of four possible brain state models by medical experts was created in terms of spectral mean and standard deviation. A basic method measuring similarity between testing and brain state model spectrums was based on normal distribution model. The results showed that the best classification of our proposed technique was found in NREM state with 95.86%. However, the classification result of inactive state was 73.33%, and overall average accuracy of all brain states was 87.76%.