Classification of Single Trial EEG Signals by a Combined Principal + Independent Component Analysis and Probabilistic Neural Network Approach

In this paper, an attempt is made to classify the EEG signals of letter imagery tasks using a combined independent component analysis and probabilistic neural network. The role of the principal/independent component analysis is to mitigate the effect of EOG artifacts within each single-trial EEG pattern. Experimental results show an overall performance improvement of around in terms of the pattern classification accuracy, in comparison with the LPC spectral analysis which is commonly employed in speech recognition tasks

Genotypic and environmental variation in cadmium, chromium, arsenic, nickel, and lead concentrations in rice grains

Genotypic and environmental variation in Cd, Cr, As, Ni and Pb concentrations of grains, and the relationships between these heavy metals and Fe, Zn were investigated using 9 rice genotypes grown in 6 locations for two successive years. Significant genotypic variation was detected in the five heavy metal concentrations in grains, indicating the possibility to reduce the concentration of these heavy metals in grains through breeding approach. The environmental effect varied with metal, with Pb and Ni having greater variation than the other three metals. There was significant genotype-environment (location) interaction of the concentrations of all five heavy metals in grains, suggesting the importance of cultivar choice in producing rice with low heavy metal concentrations in grains for a given location. Correlation analysis showed that Cd and As, Cr and Ni, and As and Pb concentrations in rice grains were closely associated, and that Ni concentration in grains was negatively correlated with Zn concentration