The impulsive electroencephalograph (EEG) noises in evoked potential (EP) signals is very strong, usually with a heavy tail and infinite variance characteristics like the acceleration noise impact, hypoxia and etc., as shown in other special tests. The noises can be described by a stable distribution model. In this paper, Wigner-Ville distribution (WVD) and pseudo Wigner-Ville distribution (PWVD) time-frequency distribution based on the fractional lower order moment are presented to be improved. We got fractional lower order WVD (FLO-WVD) and fractional lower order PWVD (FLO-PWVD) time-frequency distribution which could be suitable for a stable distribution process. We also proposed the fractional lower order spatial time-frequency distribution matrix (FLO-STFM) concept. Therefore, combining with time-frequency underdetermined blind source separation (TF-UBSS), we proposed a new fractional lower order spatial time-frequency underdetermined blind source separation (FLO-TF-UBSS) which can work in a stable distribution environment. We used the FLO-TF-UBSS algorithm to extract EPs. Simulations showed that the proposed method could effectively extract EPs in EEG noises, and the separated EPs and EEG signals based on FLO-TF-UBSS were almost the same as the original signal, but blind separation based on TF-UBSS had certain deviation. The correlation coefficient of the FLO-TF-UBSS algorithm was higher than the TF-UBSS algorithm when generalized signal-to-noise ratio (GSNR) changed from 10 dB to 30 dB and a varied from 1. 06 to 1. 94, and was approximately e- qual to 1. Hence, the proposed FLO-TF-UBSS method might be better than the TF-UBSS algorithm based on second order for extracting EP signal under an EEG noise environment.
Algorithms ; Electroencephalography ; Evoked Potentials ; Humans ; Models, Theoretical ; Signal Processing, Computer-Assisted

Evoked potentials (EPs) have been widely used to quantify neurological system properties. Traditional EP analysis has been developed under the condition that the background noises in EP are Gaussian distributed. Recently some researches indicate that electroencephalogram (EEG) is non-guassian in some especial conditions. Alpha stable distribution can model impulsive EEG in especial experimentation such as acceleration bump and devoid oxygen. In this paper, blind signals separation based on covariations is analyzed and discussed by the nonexistence of the finite second or higher order statistic. The simulation experimental results show that the method has good performance to separate Evoked potentials (EPs) from fractional lower order alpha stable distribution noise.
Algorithms ; Artifacts ; Brain ; physiology ; Computer Simulation ; Electroencephalography ; methods ; Evoked Potentials ; physiology ; Humans ; Signal Processing, Computer-Assisted

Traditional EP analysis is developed under the condition that the background noises in EP are Gaussian distributed. Alpha stable distribution, a generalization of Gaussian, is better for modeling impulsive noises than Gaussian distribution in biomedical signal processing. Conventional blind separation and estimation method of evoked potentials is based on second order statistics (SOS). In this paper, we propose a new algorithm based on minimum dispersion criterion and Givens matrix. The simulation experiments show that the proposed new algorithm is more robust than the conventional algorithm.
Algorithms ; Artifacts ; Brain ; physiology ; Electroencephalography ; methods ; Evoked Potentials ; physiology ; Humans ; Normal Distribution ; Signal Processing, Computer-Assisted