A Novel Approach to Decode Covert Spatial Attention Using SSVEP and Single-Frequency Phase-Coded Stimuli.

This paper investigates for the first time the use of single-frequency phase-coded stimuli to detect covert visuo-spatial attention (CVSA) with steady-state visual evoked potentials (SSVEP). Two 15Hz pattern-onset stimulations were encoded with opposite phases and simultaneously presented on a LCD monitor. The effects of attending each stimulus on the amplitudes and phases of the evoked SSVEPs across the visual cortex are explored. A real-time CVSA classification experiment was simulated offline with 9 BCI-naive subjects, achieving an average classification accuracy of 88.4 ± 8% SE. Our results are, to our knowledge, the first report that CVSA can be decoded with SSVEP using single-frequency phase-coded stimuli. This opens opportunities for attention-tracking applications with largely increased number of targets.

Measuring light transport properties using speckle patterns as structured illumination.

The measurement of light absorption and scattering properties of biological materials has several diagnostic and therapeutic applications. We can measure these properties for skin without contact using structured illumination and imaging. However, building simple handheld devices remains challenging due to motion artefacts and moving targets. To overcome this limitation, we project random speckle patterns instead of discrete spatial frequencies on the target. Since random patterns are spatially broadband, they capture more information per image, enabling frame-by-frame analysis. In this paper, we describe the statistics of objective speckles and demonstrate how the optical system is designed for spatially bandlimited illumination. Next, we use a diverse set of liquid tissue phantom to validate the method. We successfully demonstrate that a calibrated instrument can independently predict the two primary light transport properties of a homogeneous turbid system. This work is a starting point for analysing skin and other heterogeneous biological media in the future.

A generalized Laplacian of Gaussian filter for blob detection and its applications.

In this paper, we propose a generalized Laplacian of Gaussian (LoG) (gLoG) filter for detecting general elliptical blob structures in images. The gLoG filter can not only accurately locate the blob centers but also estimate the scales, shapes, and orientations of the detected blobs. These functions can be realized by generalizing the common 3-D LoG scale-space blob detector to a 5-D gLoG scale-space one, where the five parameters are image-domain coordinates (x, y), scales (σ(x), σ(y)), and orientation (θ), respectively. Instead of searching the local extrema of the image's 5-D gLoG scale space for locating blobs, a more feasible solution is given by locating the local maxima of an intermediate map, which is obtained by aggregating the log-scale-normalized convolution responses of each individual gLoG filter. The proposed gLoG-based blob detector is applied to both biomedical images and natural ones such as general road-scene images. For the biomedical applications on pathological and fluorescent microscopic images, the gLoG blob detector can accurately detect the centers and estimate the sizes and orientations of cell nuclei. These centers are utilized as markers for a watershed-based touching-cell splitting method to split touching nuclei and counting cells in segmentation-free images. For the application on road images, the proposed detector can produce promising estimation of texture orientations, achieving an accurate texture-based road vanishing point detection method. The implementation of our method is quite straightforward due to a very small number of tunable parameters.