Electroencephalography (EEG) is the neurophysiologic measurement of the electrical activity from the brain. Recording of this activity can be done by placing the electrodes on the scalp. For EEG analysis, we must first remove some artifacts accuracy doing the measurement from the raw signals. So many commercial programs are developed for rejecting these artifacts. Finite Impulse Response (FIR) digital filters have widely used for being implemented in the software for analyzing EEG data, either ongoing activity or event-related potentials (ERP). Generally these require the extraction of the signal from a noisy background. The study shows that the signals filtered by FIR filter were good enough and ready for ERP analysis. The interesting ERP signal is being good to work and it helps the researchers to diagnose the signal ahead. However, the problem with broadband noise is still existed. The reduction of noise by wavelet de-noising technique was used to apply to these signals for removing this kind of artifact. The wavelets technique gives better results over the linear FIR technique. So the design of wavelet filter was developed in order to decrease broadband noise from waveforms. Furthermore the study has shown in this method and wavelet method to develop the best way for signal detection.
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