Computer Code Representation through Natural Language Processing for fMRI Data Analysis

Jaeyoon Kim, Una May O'Reilly, Junhee Seok

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

There are many attempts to analyze the relationship between functional magnetic resonance imaging (fMRI) data and text stimuli representation in cognitive neuroscience research. Because programming codes are exemplary text stimuli, appropriate code representation for neuroscience research has been actively studied. In this paper, we focus on representing python code for fMRI research through natural language processing (NLP) techniques. We collect 7, 893 python codes of 23 question types from a code competition website and build three different models based on sequence-to-sequence, bag-of-words, and bigram representation. The model is evaluated to classify the types of questions. Finally, the model is applied to classify 108 python codes which were used for a cognitive neuroscience study of fMRI. We are looking forward to analyzing fMRI data with the proposed code representation for understanding how the human brain is active.

Original languageEnglish
Title of host publication4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages184-187
Number of pages4
ISBN (Electronic)9781665458184
DOIs
Publication statusPublished - 2022
Event4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Jeju lsland, Korea, Republic of
Duration: 2022 Feb 212022 Feb 24

Publication series

Name4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings

Conference

Conference4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022
Country/TerritoryKorea, Republic of
CityJeju lsland
Period22/2/2122/2/24

Bibliographical note

Funding Information:
ACKNOWLEDGEMENT This research was supported by the MOTIE (Ministry of Trade, Industry, and Energy) in Korea, under the Fostering Global Talents for Innovative Growth Program (P0008749) supervised by the Korea Institute for Advancement of Technology (KIAT) and National Research Foundation of Korea (NRF-2019R1A2C1084778).

Publisher Copyright:
© 2022 IEEE.

Keywords

  • bag-of-words
  • bigram
  • cognitive neuroscience
  • computer code representation
  • Functional magnetic resonance imaging
  • natural language processing
  • sequence-to-sequence

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Electrical and Electronic Engineering

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