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稳态视觉诱发电位及其在视觉选择性注意研究中的应用
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作者 陈晓宇 程子健 +2 位作者 胡成谧 梁腾飞 刘强 《科学通报》 EI CAS CSCD 北大核心 2020年第24期2601-2614,共14页
以固定频率发生周期性变化的视觉刺激信号进入大脑后,将会诱发一系列与之频率相同的周期性脑电位,这个电位叫做稳态视觉诱发电位(steady-state visual evoked potentials,SSVEP).SSVEP广泛应用于脑机接口和人类认知研究中.相同频率下,SS... 以固定频率发生周期性变化的视觉刺激信号进入大脑后,将会诱发一系列与之频率相同的周期性脑电位,这个电位叫做稳态视觉诱发电位(steady-state visual evoked potentials,SSVEP).SSVEP广泛应用于脑机接口和人类认知研究中.相同频率下,SSVEP的振幅高低与视觉注意资源分配具有相关性,因此在视觉选择性注意研究中常常使用SSVEP作为表征注意分配的电生理指标.以SSVEP为指标进行视觉选择性注意研究时,主要的应用手段是频率标记.频率标记是指让被标记刺激发生特定频率的周期性变化,从而诱发与之频率相同的SSVEP,并以每个刺激诱发的SSVEP的振幅作为注意资源分配水平的指标.根据研究目的不同,在频率标记的基础上进一步发展出了快速周期性视觉刺激范式和随机运动点阵范式用于视觉注意的研究.视觉选择性注意中,SSVEP适用于基于空间的注意和基于特征的注意研究.今后使用SSVEP对视觉选择性注意进行研究时,可以试图增加如情绪诱发、奖励和惩罚、工作记忆表征等影响视觉选择性注意的研究变量,也可以以SSVEP为指标,建立基于特征的注意和基于空间的注意之间的联系.此外,脑机接口研究中开发的针对SSVEP的算法也许可引入视觉选择性注意研究中. 展开更多
关键词 稳态视觉诱发电位 视觉选择性注意 频率标记 注意资源
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An Efficient Hybrid DSMC/MD Algorithm for Accurate Modeling of Micro Gas Flows
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作者 tengfei liang Wenjing Ye 《Communications in Computational Physics》 SCIE 2014年第1期246-264,共19页
Aiming at simulating micro gas flows with accurate boundary conditions,an efficient hybrid algorithm is developed by combining the molecular dynamics(MD)method with the direct simulation Monte Carlo(DSMC)method.The ef... Aiming at simulating micro gas flows with accurate boundary conditions,an efficient hybrid algorithm is developed by combining the molecular dynamics(MD)method with the direct simulation Monte Carlo(DSMC)method.The efficiency comes from the fact that the MD method is applied only within the gas-wall interaction layer,characterized by the cut-off distance of the gas-solid interaction potential,to resolve accurately the gas-wall interaction process,while the DSMC method is employed in the remaining portion of the flow field to efficiently simulate rarefied gas transport outside the gas-wall interaction layer.A unique feature about the present scheme is that the coupling between the two methods is realized by matching the molecular velocity distribution function at the DSMC/MD interface,hence there is no need for one-toone mapping between a MD gas molecule and a DSMC simulation particle.Further improvement in efficiency is achieved by taking advantage of gas rarefaction inside the gas-wall interaction layer and by employing the“smart-wall model”proposed by Barisik et al.The developed hybrid algorithm is validated on two classical benchmarks namely 1-D Fourier thermal problem and Couette shear flow problem.Both the accuracy and efficiency of the hybrid algorithm are discussed.As an application,the hybrid algorithm is employed to simulate thermal transpiration coefficient in the free-molecule regime for a system with atomically smooth surface.Result is utilized to validate the coefficients calculated from the pure DSMC simulation with Maxwell and Cercignani-Lampis gas-wall interaction models. 展开更多
关键词 Rarefied gas flows surface effect multi-scale methods
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