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Updated: May 22, 2026
Assessment and Communication for People with Disorders of Consciousness
Published on: August 1, 2017
Enhancing P300 Wave of BCI Systems Via Negentropy in Adaptive Wavelet Denoising
Z Vahabi1, R Amirfattahi, Ar Mirzaei
1Digital Signal Processing Research Lab, Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran E-mail: z.vahabi@ec.iut.ac.ir , fattahi,mirzaei@cc.iut.ac.ir.
Abstract:
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. EEG separation into target and non-target ones based on presence of P300 signal is of 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 SNR. Our denoising method is based on multi-resolution analysis via Independent Component Analysis (ICA) Fundamentals. We have suggested combination of negentropy as a feature of signal and subband information from wavelet transform. The proposed method is finally tested with dataset from BCI Competition 2003 and gives results that compare favorably.
