Hierarchical Self-organizing Maps of NIRS and EEG Signals for Recognition of Brain States
Hierarchical Self-organizing Maps of NIRS and EEG Signals for Recognition of Brain States
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用于识别大脑状态的 NIRS 和 EEG 信号的分层自组织图
DOI：
10.1007/978-3-319-39601-9_30
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Carl K. Chang
中科院分区：
文献类型：
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作者：
Katsunori Oyama;Kaoru Sakatani;Hua Ming;Carl K. Chang
利用近红外光谱和脑电信号对大脑活动进行时间数据挖掘的最新进展使我们能够以更高的分辨率识别大脑状态。然而，大脑状态并不总是彼此不同，而且在时间粒度上经常不同。本文回顾了Dennett的三个层次的立场，即设计两个自组织映射(SOM)的DIKW模型，该模型有助于识别具有更细粒度的大脑状态的层次结构。实验结果表明，通过对不同级别的SOM应用不同的训练数据，可以准确地识别出不同级别的两种大脑状态。
Recent advances in temporal data mining of brain activity with NIRS and EEG signals allow us to recognizebrain statesin higher resolution. However, brain states are not always distinct from each other and often differ in temporal granularity. This paper revisits Dennett’s three levels of stance, the DIKW model for the design of two self-organizing maps (SOMs), which contributes to recognition of a hierarchy of brain states with finer granularities. The experimental results show that two brain states at different levels can be accurately identified by applying different training data for each level of SOM.