Feature Extraction of Electroencephalography Signals Using Fast Fourier Transform

This article discusses a method within the area of brain-computer interface. The proposed method is to use the features extracted from the Electroencephalograph signal and a three hidden-layer artificial neural network to map the brain signal features to the computer cursor movement. The evaluated features are the root mean square and the average power spectrum. The empirical evaluation using 200 records taken from 2003 BCI Competition dataset shows that the current approach can accurately classify a simple cursor movement within 92.5% accuracy in a short computation time.

Study on Fast Algorithm for SNR and Method for Threshold Confirmation

To reduce the amount of computing resources, a fast algorithm of the average power spectrum and signal-to-noise ratio was presented based on rigorous derivation of the formula. Also, it proved the rule gained from computational experiments. Besides, a method called fitting-optimization to determine the classification threshold value was proposed. It improves the accuracy by about 7% for human gene.

RIMAPS and Varlogram Analysis of Barley Leaf Surfaces

It is a common practice to use microscopic images to describe the differences observed between plant tissues. The images illustrate the taxonomic characteristics of the studied species. In this work we introduce a quantitative method for conducting these analyses utilizing digitized images obtained via scanning electron microscopy (SEM) of barley leaf surfaces. The topography of the leaf surfaces of a narrow-leaf mutant and its wild type mother line was characterized, see figure 1, using the Rotated Image with Maximum Average Power Spectrum (RIMAPS) technique and the Variogram method. Spectra resulting from RIMAPS analysis allow us to identify the specimens and to distinguish between the adaxial or the abaxial side of the leaf. These results are complemented by obtaining the typical scale lengths that characterize the abaxial surfaces of both the mutant and the mother line barley leaves.

A Method to Estimate Fried's Seeing Parameter from a Time Series of Arbitrary Resolved Structures Imaged Through the Atmosphere

One of the major fields of application of speckle-interferometric and speckle-imaging techniques is the photometry of astronomical objects exhibiting structure smaller than the seeing limit. The accuracy of the photometry depends critically on the accuracy to which the modulation transfer function (MTF), that describes the atmospheric-telescopic attenuation of the Fourier amplitudes of the object under consideration, is known. The estimation of the effective MTF is especially difficult when no known reference object is within the field of view.A method is presented that allows to estimate the effective MTF from the observation of arbitrary structure with the use of FRIED-KORFF theory. The ratio of the squared modulus of the average Fourier transform and the average power spectrum serves as an estimator for the FRIED parameter r0. To a first approximation, this ratio is independent from the observed object. Additionally, the behaviour of the ratio in regions beyond the seeing limit in the Fourier plane may be analyzed to obtain an estimate of the speckle interferometry signal-to-noise ratio. The basic concept of the ratio method will be described, its accuracy will be discussed. First results on the application of the ratio method to observations of solar granulation will be presented.