Form Properties of Moving Targets Bias Smooth Pursuit Target Selection in Monkeys / 神经科学通报·英文版

During natural viewing, we often recognize multiple objects, detect their motion, and select one object as the target to track. It remains to be determined how such behavior is guided by the integration of visual form and motion perception. To address this, we studied how monkeys made a choice to track moving targets with different forms by smooth pursuit eye movements in a two-target task. We found that pursuit responses were biased toward the motion direction of a target with a hole. By computing the relative weighting, we found that the target with a hole exhibited a larger weight for vector computation. The global hole feature dominated other form properties. This dominance failed to account for changes in pursuit responses to a target with different forms moving singly. These findings suggest that the integration of visual form and motion perception can reshape the competition in sensorimotor networks to guide behavioral selection.

A new method based on sparse component decomposition to remove MRI artifacts in the continuous EEG recordings / 生物医学工程学杂志

How to effectively remove the magnetic resonance imaging (MRI) artifacts in the electroencephalography (EEG) recordings, when EEG and functional magnetic resonance imaging (FMRI) are simultaneous recorded, is a challenge for integration of EEG and FMRI. According to the temporal-spatial difference between MRI artifacts and EEG, a new method based on sparse component decomposition in the mixed over-complete dictionary is proposed in this paper to remove MR artifacts. A mixed over-complete dictionary (MOD) of waveletes and discrete cosine which can exhibit the temporal-spatial discrepancy between MRI artificats and EEG is constructed first, and then the signals are separated by learning in this MOD with matching pursuit (MP) algorithm. The method is applied to the MRI artifacts corrupted EEG recordings and the decomposition result shows its validation.