IEICE Technical Committee Submission System
Conference Paper's Information
Online Proceedings
[Sign in]
Tech. Rep. Archives
 Go Top Page Go Previous   [Japanese] / [English] 

Paper Abstract and Keywords
Presentation 2021-01-21 13:30
[Invited Lecture] Individual representation of creativity using large-scale brain dataset
Takeshi Ogawa (ATR)
Abstract (in Japanese) (See Japanese page) 
(in English) Thanks to development of machine learning methods based on large-scale data, it has made huge impacts on not only image and natural language processing, but also processing of neuroimaging data. For example, Human Connectome Project in U.S.A has been collecting more than thousands of brain data with demographic data including scores of cognitive functions and sharing them as open-public data. Therefore, researchers of methodology or statistics have abundantly reported advanced analysis methods. In particular, functional connectivity calculated from brain activity at rest represents not only age and sex, but also cognitive functions and psychiatric disorders. Recently, interactions of several networks (central executive network, default mode network and saliency network) represent individual creative cognition. In general, data of functional connectivity is high dimension, therefore, it often occurs over-fitting problem. To avoid this problem, data of functional connectivity should be assumed sparseness. In this study, I examined connectome-based prediction model which combined feature extractions machine learning with data-driven manner to illustrate brain network which represented the cognitive functions associated with creativity. I conducted several machine learning methods which assumed sparseness for large-scale brain data. In addition, I prepared several parcellation methods to optimize feature extraction methods. I would like to discuss feasibility of prediction model for the cognitive functions based on results of the connectome-based prediction models.
Keyword (in Japanese) (See Japanese page) 
(in English) Machine learning / Creativity / Large-scale data / Brain network marker / Functional connectivity / / /  
Reference Info. IEICE Tech. Rep.
Paper #  
Date of Issue  
ISSN  
Download PDF

Conference Information
Committee CQ  
Conference Date 2021-01-21 - 2021-01-21 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Special Workshop for 30th Anniversary of Technical Committee on Communication Quality 
Paper Information
Registration To CQ 
Conference Code 2021-01-CQ 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Individual representation of creativity using large-scale brain dataset 
Sub Title (in English)  
Keyword(1) Machine learning  
Keyword(2) Creativity  
Keyword(3) Large-scale data  
Keyword(4) Brain network marker  
Keyword(5) Functional connectivity  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Takeshi Ogawa  
1st Author's Affiliation Advanced Telecommunications Research Institute International (ATR)
2nd Author's Name  
2nd Author's Affiliation ()
3rd Author's Name  
3rd Author's Affiliation ()
4th Author's Name  
4th Author's Affiliation ()
5th Author's Name  
5th Author's Affiliation ()
6th Author's Name  
6th Author's Affiliation ()
7th Author's Name  
7th Author's Affiliation ()
8th Author's Name  
8th Author's Affiliation ()
9th Author's Name  
9th Author's Affiliation ()
10th Author's Name  
10th Author's Affiliation ()
11th Author's Name  
11th Author's Affiliation ()
12th Author's Name  
12th Author's Affiliation ()
13th Author's Name  
13th Author's Affiliation ()
14th Author's Name  
14th Author's Affiliation ()
15th Author's Name  
15th Author's Affiliation ()
16th Author's Name  
16th Author's Affiliation ()
17th Author's Name  
17th Author's Affiliation ()
18th Author's Name  
18th Author's Affiliation ()
19th Author's Name  
19th Author's Affiliation ()
20th Author's Name  
20th Author's Affiliation ()
Speaker Author-1 
Date Time 2021-01-21 13:30:00 
Presentation Time 15 minutes 
Registration for CQ 
Paper #  
Volume (vol) vol. 
Number (no)  
Page  
#Pages  
Date of Issue  


[Return to Top Page]

[Return to IEICE Web Page]


The Institute of Electronics, Information and Communication Engineers (IEICE), Japan