Directed EEG Functional Connectivity Features to Reveal Different Attention Indexes Using Hierarchical Clustering

Hadriana Iddas, Keiji Iramina

研究成果: ジャーナルへの寄稿学術誌査読

3 被引用数 (Scopus)


Functional connectivity related to familiarity has recently been investigated in the context of various stimuli (e.g., words, faces, pictures, music, and video). However, the directed functional connectivity patterns with different attention indexes as a response to familiar/unfamiliar stimuli remain unclear. In the current study, we employed the Directed Transfer Function (DTF) to estimate the information flow between brain areas. This method was reported to be practically robust to volume conduction. Furthermore, the hierarchical clustering approach was utilized to group subjects based on the attention index, i.e., the alpha/theta ratio of fronto-central (frontal to central and central to frontal) features. Three major findings were revealed from this study. First, all subjects had different attention indexes when they watched familiar/unfamiliar videos. Then, subjects were sorted into three groups: low index (LI), middle index (MI), and high index (HI). Second, a competition between two states (familiar/unfamiliar) showed that the information flows of familiar stimuli were greater than unfamiliar stimuli, which involved significant effects in the frontal, temporal, and parietal areas. Third, comparison between groups (LI/MI/HI) demonstrated that the frontal and central regions were the primary sources that distributed information flows to almost the whole brain, particularly during familiar conditions. This result indicates that these two regions may play an important role in attentional processing.

ジャーナルIEEE Access
出版ステータス出版済み - 2021

!!!All Science Journal Classification (ASJC) codes

  • コンピュータ サイエンス(全般)
  • 材料科学(全般)
  • 工学(全般)


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