计算机科学
相似性(几何)
脑磁图
人工智能
模式识别(心理学)
脑电图
计算
语音识别
算法
图像(数学)
心理学
精神科
作者
Li Su,Elisabeth Fonteneau,William D. Marslen‐Wilson,Nikolaus Kriegeskorte
摘要
Time resolved imaging techniques, such as MEG and EEG, are unique in their ability to reveal the rich dynamic spatiotemporal patterning of neural activities. Here we propose a technique based on spatiotemporal searchlight Representational Similarity Analysis (RSA) of combined MEG and EEG (EMEG) data to directly analyze the multivariate pattern of information flow across the brain. This novel technique can recognize fine-grained dynamic neural computations both in space and in time. A prime example of such neural computations is our ability to understand spoken words in real time. A computational approach to these processes is suggested by the Cohort Model of spoken-word recognition. Here we show how spatiotemporal searchlight RSA applied to source estimations of EMEG data can provide insights into the neural correlates of the cohort model within bilateral front temporal brain regions.
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