选择性注意脑电成分的新模型及其应用(英文)

提出了一个新的选择性注意脑电信号分解模型WGVVua =,并应用总体最小二乘法(TLS)求解该模型方程。根据TLS求得的G和W,注意效应被分解成了两个方面,注意引起的脑神经活动W和注意对非注意情况脑活动的门限调控作用G。将该模型应用于空间注意ERP数据,进一步提出了选择性注意的一个新的理论框架,即在刺激后的200 ms内出现的调制放大(G>1.0)和新活动成分(W)主要起改善信噪比的作用,而在200 ms后出现的调制抑制(G<1.0)和新活动成分(W)则旨在对选择性增强的信号成分的特定认知处理。

A New Signal Model of Selective Attention Evoked Potential and Its Primary Application

Presented is an analytic signal modelWGVVua = with aV and uVcorresponding to the event-related potentials (ERPs) evoked by attended and unattended visual stimuli, respectively. A total least square algorithm is utilized to get the G and W, then the effect of attention is decomposed into two aspects: attention induced activities (W) and a gain control (G) of unattended. Applied to a spatial attention ERP data, an integrated concept of selective attention is suggested that the early gain amplification (G>1.0) and newly involved neuron activities (W) before 200 ms after the stimulus onset mainly aim at obtaining an improved signal/noise ratio, the late gain supression (G<1.0) and newly involved neuron activities (W) after 200 ms aim at specific cognitive processing of the selectively enhanced signal.