The brain state of motor imagery is reflected in the causal information of functional near-infrared spectroscopy.

Background: Brain-computer interface (BCI) is a promising neurorehabilitation strategy for ameliorating post-stroke function disorders. Physiological changes in the brain, such as functional near-infrared spectroscopy (fNIRS) dedicated to exploring cerebral circulatory responses during neurological rehabilitation tasks, are essential for gaining insights into neurorehabilitation mechanisms. However, the relationship between the neurovascular responses in different brain regions under rehabilitation tasks remains unknown.

A P300 Brain-Computer Interface Paradigm Based on Electric and Vibration Simple Command Tactile Stimulation.

This paper proposed a novel tactile-stimuli P300 paradigm for Brain-Computer Interface (BCI), which potentially targeted at people with less learning ability or difficulty in maintaining attention. The new paradigm using only two types of stimuli was designed, and different targets were distinguished by frequency and spatial information. The classification algorithm was developed by introducing filters for frequency bands selection and conducting optimization with common spatial pattern (CSP) on the tactile evoked EEG signals.

Calibration and measurement analysis of a cloud particle detection system based on polarization detection.

The phase state information of cloud water is important for the airborne measurement of the microphysical properties of a cloud. A cloud particle detection system based on polarization detection, which can be used to detect the size and phase state of cloud particles for particle diameters of less than 50 µm, was developed by detecting the energy of the forward scattering and the depolarization of backscattered light. The sensitive area was calculated through the width and depth of the field of view of the laser beam.