Abstract: A method for improving the signal to noise ratio of an EEG signal in which a wavelet packet decomposition having a plurality of levels is first applied to a time slice of the EEG signal. A default signal is set to the first wavelet packet and a default peak response is then calculated for the first wavelet node. An update signal is set to the default signal combined with another of the wavelet nodes and an update peak response signal is then calculated of the update signal. If the update peak response signal exceeds the default peak response, the default peak response is set equal to the update peak response and the default signal is set equal to the update signal. Otherwise, the value of the current node is set to zero which effectively eliminates the signal data of the current wavelet node. These steps are reiterated for all of the wavelet nodes and, thereafter, a composite waveform of the EEG signal is reconstructed from the non-zero wavelet nodes.

Abstract: A method for improving the signal to noise ratio of an EEG signal in which a wavelet packet decomposition having a plurality of levels is first applied to a time slice of the EEG signal. A default signal is set to the first wavelet packet and a default peak response is then calculated for the first wavelet node. An update signal is set to the default signal combined with another of the wavelet nodes and an update peak response signal is then calculated of the update signal. If the update peak response signal exceeds the default peak response, the default peak response is set equal to the update peak response and the default signal is set equal to the update signal. Otherwise, the value of the current node is set to zero which effectively eliminates the signal data of the current wavelet node. These steps are reiterated for all of the wavelet nodes and, thereafter, a composite waveform of the EEG signal is reconstructed from the non-zero wavelet nodes.