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A technique proposed for the automatic detection of spikes in electroencephalograms (EEG). The important features of the raw EEG data were extracted using two methods: Wavelet transform and energy estimation. This data was normalized and given as input to the neural network, which was trained using back propagation algorithm. Energy estimation was used as an amplitude threshold parameter. The wavelet transform (WT) is a powerful tool for multi-resolution analysis of non-stationary signal asdoi:10.5897/ijps09.036 fatcat:tuc52oz43nduhhmf7qwoe5oyx4