Advertisement
Published in:
2019 | OriginalPaper | Chapter
Examination of the Brain Areas Related to Cognitive Performance During the Stroop Task Using Deep Neural Network
Authors : Tomohiro Nishikawa, Yushi Hashimoto, Kosei Minami, Keiichi Watanuki, Kazunori Kaede, Keiichi Muramatsu
Published in: Advances in Affective and Pleasurable Design
Publisher: Springer International Publishing
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
Abstract
To examine brain areas related to the cognitive load condition during the Stroop task, we proposed a method using a Deep Neural Network (DNN). We acquired cerebral blood flow data in congruent and incongruent tasks by near-infrared spectroscopy (NIRS) equipped with 22 ch. The data were used to train a DNN, and the influence of each factor on the output was evaluated. Our DNN model consists of independent input layers for each channel of NIRS, as well as fully-connected hidden layers and output layers. Our results suggest that the medial prefrontal cortex (focusing on cognition) and the left inferior frontal gyrus (focusing on language processing) were involved in the cognitive load during the Stroop task. These results in the Stroop task were consistent. Therefore, the proposed method’s utility was confirmed.