Computer Code Representation through Natural Language Processing for fMRI Data Analysis

Jaeyoon Kim; Una May O'Reilly; Junhee Seok

doi:10.1109/ICAIIC54071.2022.9722644

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 proceeding › Conference contribution

2 Citations (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 language	English
Title of host publication	4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	184-187
Number of pages	4
ISBN (Electronic)	9781665458184
DOIs	https://doi.org/10.1109/ICAIIC54071.2022.9722644
Publication status	Published - 2022
Event	4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Jeju lsland, Korea, Republic of Duration: 2022 Feb 21 → 2022 Feb 24

Publication series

Name	4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings

Conference

Conference	4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022
Country/Territory	Korea, Republic of
City	Jeju lsland
Period	22/2/21 → 22/2/24

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

Access to Document

10.1109/ICAIIC54071.2022.9722644

Cite this

Kim, J., O'Reilly, U. M., & Seok, J. (2022). Computer Code Representation through Natural Language Processing for fMRI Data Analysis. In 4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings (pp. 184-187). (4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAIIC54071.2022.9722644

Computer Code Representation through Natural Language Processing for fMRI Data Analysis. / Kim, Jaeyoon; O'Reilly, Una May; Seok, Junhee.
4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. p. 184-187 (4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kim, J, O'Reilly, UM & Seok, J 2022, Computer Code Representation through Natural Language Processing for fMRI Data Analysis. in 4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings. 4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 184-187, 4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022, Jeju lsland, Korea, Republic of, 22/2/21. https://doi.org/10.1109/ICAIIC54071.2022.9722644

Kim J, O'Reilly UM, Seok J. Computer Code Representation through Natural Language Processing for fMRI Data Analysis. In 4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. p. 184-187. (4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings). doi: 10.1109/ICAIIC54071.2022.9722644

Kim, Jaeyoon ; O'Reilly, Una May ; Seok, Junhee. / Computer Code Representation through Natural Language Processing for fMRI Data Analysis. 4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 184-187 (4th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2022 - Proceedings).

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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. ",

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Computer Code Representation through Natural Language Processing for fMRI Data Analysis

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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