Nasrin Rafiei - Department of Electrical and Electronics Engineering Shahrekord University, Shahrekord,Iran
Maryam Taghizadeh - Department of Electrical and Electronics Engineering Shahrekord University, Shahrekord,Iran
Amir Hossein ghaderi - Educational Sciences and Psychology University, Tabriz University, Tabriz,Iran

Wavelet transform was used to feature extraction of EEG signals during problem solving and rest state. Statistical features such as entropy, median, mean, energy, norm, variance and standard deviation were calculated in terms of detailed coefficients and the approximation coefficient of the last decomposition level. The EEG signals were recorded during (1) problem solving task and(2) rest state. EEGs on 3 midline electrodes Fz, Cz, Pz were analyzed. The features determined in the two conditions are clustered Using FCM. The results indicate wavelet transform is a usefull approach for determining brain activity in known frequency band considering to cognitive challenges

Electroencephalogram;Feature extraction;Discrete wavelet transform ;FCM