Enhancing P300 Wave of BCI Systems Via Negentropy in Adaptive Wavelet Denoising

Brian computer interface (bci) is a direct communication pathway between the brain and an external device. bcis are often aimed at assisting, augmenting or repairing human cognitive or sensory?motor functions. electroencephalogram (eeg) separation into target and non?target ones, based on presence of p300 signal, is a difficult task mainly due to their natural low signal to noise ratio. in this paper, a new algorithm is introduced to enhance eeg signals and improve their signal to noise ratio. our denoising method is based on multi?resolution analysis via independent component analysis fundamentals. we have suggested combination of negentropy as a feature of signal and sub?band information from wavelet transform. the proposed method is finally tested with dataset from bci competition 2003, and has given results that compare favorably.