Laser speckle contrast imaging is a technique used for imaging blood flow without scanning. Though several studies have attempted to combine spatial and temporal statistics of laser speckle images for reducing image noise as well as preserving acceptable spatiotemporal resolution, the statistical accuracy of these spatiotemporal methods has not been thoroughly compared. Through numerical simulation and animal experiments, this study investigates the changes in the mean speckle contrast values and the relative noise of the speckle contrast images computed by these methods with various numbers of frames and spatial windows. The simulation results show that the maximum relative error of the mean speckle contrast computed by the spatiotemporal laser speckle contrast analysis (STLASCA) method, in which the speckle contrast images are computed by analyzing the 3-D spatiotemporal speckle image cube, is approximately 5%, while it is higher than 13% for other methods. Changes in the mean speckle contrast values and the relative noise computed by these methods for animal experiment data are consistent with the simulation results. Our results demonstrate that STLASCA achieves more accurate speckle contrast, and suggest that STLASCA most effectively utilizes the number of pixels, thus achieving maximized speckle contrast, and thereby maximizing the variation of the laser speckle contrast image.

Ear-electroencephalography (ear-EEG) using electrodes placed above hairless areas around ears is a convenient and comfortable method for signal recording in practical applications of steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI). However, due to the constraint of electrode distribution behind the ear, the amplitude of SSVEP in ear-EEG signals is relatively low, which hinders the application of ear-EEG in SSVEP-based BCI. This study was aimed to improve the performance of ear-EEG in SSVEP-based BCI through re-implementing a compact convolutional neural network (EEGNet) with ensemble learning. We first evaluated the feasibility of applying widely used EEGNet models with different kernel numbers to decode SSVEP in ear-EEG signals. Then we applied an ensemble learning strategy to combine EEGNet models with different kernel numbers to improve the classification of ear-EEG signals. The ear-EEG data was from an open dataset, which acquired three sessions of SSVEP data induced by three flicker stimuli from eleven subjects. The average accuracy of EEGNet with ensemble learning for ear-EEG signals in cross-session validations at 1 s window length was 81.12% (from session 1 to session 2) and 81.74% (from session 1 to session 3), which significantly outperformed canonical correlation analysis (CCA). In addition, the network visualization indicated that EEGNet extracted features related to stimulation frequencies. The results showed promise for accurate classification of SSVEP in ear-EEG signals using deep learning models with strategies, helping to promote the SSVEP based BCI from laboratory to practical application.

Laser speckle contrast analysis (LASCA) is a non-invasive, full-field optical technique that produces two-dimensional map of blood flow in biological tissue by analyzing speckle images captured by CCD camera. Due to the heavy computation required for speckle contrast analysis, video frame rate visualization of blood flow which is essentially important for medical usage is hardly achieved for the high-resolution image data by using the CPU (Central Processing Unit) of an ordinary PC (Personal Computer). In this paper, we introduced GPU (Graphics Processing Unit) into our data processing framework of laser speckle contrast imaging to achieve fast and high-resolution blood flow visualization on PCs by exploiting the high floating-point processing power of commodity graphics hardware. By using GPU, a 12-60 fold performance enhancement is obtained in comparison to the optimized CPU implementations.

We developed a dual-wavelength laser speckle imaging system using a single industrial-grade color CCD camera with Bayer filters to simultaneously image changes in blood flow, blood volume, and oxygenation. One frame of a color image recorded with dual-wavelength laser illumination provides not only the intensity fluctuation of the speckle pattern, but also the dual-wavelength optical reflectance signal. The method was validated using a tissue phantom and cuff ischemia experiments in the human arm. This system achieves complete time synchronization, unlike conventional time-sharing systems. Compared with a multicamera system, it also avoids the problem of image registration and can be less expensive.

The tissue optical clearing technique shows great potential in optical diagnosis and therapy. However, short-term and long-term effects of optical clearing agents on blood vessels, which are relevant to the safety of clinical applications, have not been clarified. We used laser speckle contrast imaging to monitor the changes in blood vessels in chick chorioallantoic membrane (CAM) after application of glycerol or glucose. The changes in morphology of vessels and blood flow velocity were measured. Long-term effects on blood vessels were investigated by observing the function and the development of blood vessels. The results show that glycerol reduces the local blood flow velocity and constricts and even blocks vessels quickly. At 2 days, the blood flow velocity is recovered to different extents, and new blood vessels develop but are fewer in number. Glucose induces slow changes in blood flow or vessels. However, most blood vessels are blocked, and no new blood vessel develops at 2 days. The effects depend on the dosage of agents, including volume and concentration, and decrease with the dosage of agents. Therefore, short-term effects of glucose on blood vessels are slighter than those of glycerol, but long-term effects of glucose are greater.