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Due to the excellent localization characteristic of the wavelet transform both in time domain and frequency domain, the traditional Fourier filter can hardly attain the similar performance, a extraction method of visual evoked potentials based on wavelet packet and wavelet neural network is presented, which can extract of visual evoked potentials in single training sample from the background noise of electro encephalogram in favor of studying the changes between the single sample response. The information is probably related with the different function, appearance and pathology of the brain. The experiment result proves that this method is not restricted by the signal-to-noise ratio of evoked potential and electroencephalograph and even can recognize instantaneous event under the condition of lower signal-to-noise ratio, as well as recognize more easily the samples that evoked evident response. Therefore, more evident average evoked response could be achieved by denoising the signals obtained through averaging out the samples that can evoke evident responses than denoising the average of original signals. In addition, averaging methodology can dramatically reduce the number of record samples needed, thus avoiding the effect of behavior change during the recording process. This methodology pays attention to the differences among single training sample and also accomplishes the extraction of visual evoked potentials from single training sample. As a result, system speed and accuracy could be improved to a great extent if this methodology is applied to brain-computer interface system based on evoked responses.

Extraction of Visual Evoked Potentials Based on Wavelet Packet and Wavelet Neural Network