TB151301 Advanced Research on Affective Data Science

Hiroshima University Syllabus

Japanese

Academic Year 2026Year School/Graduate School Graduate School of Biomedical and Health Sciences (Doctoral Course)

Lecture Code TB151301 Subject Classification Specialized Education

Subject Name 感性データサイエンス特別研究

Subject Name
（Katakana）カンセイデータサイエンストクベツケンキュウ

Subject Name in
English Advanced Research on Affective Data Science

Instructor CHIKAZOE JUNICHI,PHAM QUANG TRUNG

Instructor
(Katakana) チカゾエ　ジュンイチ,ファム　クアン　チュン

Campus Kasumi Semester/Term 1st-Year, First Semester, First Semester

Days, Periods, and Classrooms (1st) Inte

Lesson Style Experiment Lesson Style
(More Details) Face-to-face, Online (simultaneous interactive), Online (on-demand)

Lecture-based, Exercise-based, Discussion, Student presentations, In-class work, Programming

Credits 2.0 Class Hours/Week 　 Language of Instruction B : Japanese／English

Course Level 5 : Graduate Basic

Course Area（Area） 27 : Health Sciences

Course Area（Discipline） 01 : Medical Sciences

Eligible Students

Keywords Neuroscience, fMRI, Machine Learning, Sensation, Perception, Emotion, Affect, Deep Learning

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students)

Class Objectives
/Class Outline Students will conduct neuroscience research using machine learning techniques on human physiology data including fMRI data, with the ultimate goal of publishing a research paper.

Class Schedule lesson1 Individual Consultation (Progress review and goal setting)
lesson2 Foundation Building 1 (Literature review and study of theoretical frameworks)
lesson3 Foundation Building 2 (Literature review and study of theoretical frameworks)
lesson4 Foundation Building 3 (Literature review and study of theoretical frameworks)
lesson5 Preliminary Validation 1 (Pilot experiments and methodological fine-tuning)
lesson6 Preliminary Validation 2 (Pilot experiments and methodological fine-tuning)
lesson7 Preliminary Validation 3 (Pilot experiments and methodological fine-tuning)
lesson8 Progress Assessment I (Finalizing methodology)
lesson9 Data Collection 1 (Commencement of experiments and troubleshooting)
lesson10 Data Collection 2 (Commencement of experiments and troubleshooting)
lesson11 Data Collection 3 (Commencement of experiments and troubleshooting)
lesson12 Interim Review 1 (Initial data analysis and hypothesis reassessment)
lesson13 Interim Review 2 (Initial data analysis and hypothesis reassessment)
lesson14 Interim Review 3 (Initial data analysis and hypothesis reassessment)
lesson15 Progress Assessment II (Consolidation of tasks for the following term)

Text/Reference
Books,etc. The Elements of Statistical Learning（Hastie et al. ）
Functional Magnetic Resonance Imaging (Huettel et al.）

PC or AV used in
Class,etc. Text, Handouts, Microsoft Teams, Zoom

(More Details)

Learning techniques to be incorporated Discussions, Paired Reading, PBL (Problem-based Learning)/ TBL (Team-based Learning), Project Learning

Suggestions on
Preparation and
Review Students must acquire a solid understanding of the fundamentals of statistics and machine learning. Particular emphasis is placed on gaining the foundational knowledge necessary to avoid common pitfalls in machine learning applications.

It is essential to correctly understand typical issues such as overfitting and data leakage. Through the process of conducting fMRI experiments, students will learn how these issues can compromise research integrity and how to mitigate them.

Requirements

Grading Method Evaluation will be based on the student's research attitude and the depth of their understanding regarding statistics and machine learning.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2026Year	School/Graduate School	Graduate School of Biomedical and Health Sciences (Doctoral Course)
Lecture Code	TB151301	Subject Classification	Specialized Education
Subject Name	感性データサイエンス特別研究
Subject Name （Katakana）	カンセイデータサイエンストクベツケンキュウ
Subject Name in English	Advanced Research on Affective Data Science
Instructor	CHIKAZOE JUNICHI,PHAM QUANG TRUNG
Instructor (Katakana)	チカゾエ　ジュンイチ,ファム　クアン　チュン
Campus	Kasumi	Semester/Term	1st-Year, First Semester, First Semester
Days, Periods, and Classrooms	(1st) Inte
Lesson Style	Experiment	Lesson Style (More Details)	Face-to-face, Online (simultaneous interactive), Online (on-demand)
Lecture-based, Exercise-based, Discussion, Student presentations, In-class work, Programming
Credits	2.0	Class Hours/Week		Language of Instruction	B : Japanese／English
Course Level	5 : Graduate Basic
Course Area（Area）	27 : Health Sciences
Course Area（Discipline）	01 : Medical Sciences
Eligible Students
Keywords	Neuroscience, fMRI, Machine Learning, Sensation, Perception, Emotion, Affect, Deep Learning
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)
Class Objectives /Class Outline	Students will conduct neuroscience research using machine learning techniques on human physiology data including fMRI data, with the ultimate goal of publishing a research paper.
Class Schedule	lesson1 Individual Consultation (Progress review and goal setting) lesson2 Foundation Building 1 (Literature review and study of theoretical frameworks) lesson3 Foundation Building 2 (Literature review and study of theoretical frameworks) lesson4 Foundation Building 3 (Literature review and study of theoretical frameworks) lesson5 Preliminary Validation 1 (Pilot experiments and methodological fine-tuning) lesson6 Preliminary Validation 2 (Pilot experiments and methodological fine-tuning) lesson7 Preliminary Validation 3 (Pilot experiments and methodological fine-tuning) lesson8 Progress Assessment I (Finalizing methodology) lesson9 Data Collection 1 (Commencement of experiments and troubleshooting) lesson10 Data Collection 2 (Commencement of experiments and troubleshooting) lesson11 Data Collection 3 (Commencement of experiments and troubleshooting) lesson12 Interim Review 1 (Initial data analysis and hypothesis reassessment) lesson13 Interim Review 2 (Initial data analysis and hypothesis reassessment) lesson14 Interim Review 3 (Initial data analysis and hypothesis reassessment) lesson15 Progress Assessment II (Consolidation of tasks for the following term)
Text/Reference Books,etc.	The Elements of Statistical Learning（Hastie et al. ） Functional Magnetic Resonance Imaging (Huettel et al.）
PC or AV used in Class,etc.	Text, Handouts, Microsoft Teams, Zoom
(More Details)
Learning techniques to be incorporated	Discussions, Paired Reading, PBL (Problem-based Learning)/ TBL (Team-based Learning), Project Learning
Suggestions on Preparation and Review	Students must acquire a solid understanding of the fundamentals of statistics and machine learning. Particular emphasis is placed on gaining the foundational knowledge necessary to avoid common pitfalls in machine learning applications. It is essential to correctly understand typical issues such as overfitting and data leakage. Through the process of conducting fMRI experiments, students will learn how these issues can compromise research integrity and how to mitigate them.
Requirements
Grading Method	Evaluation will be based on the student's research attitude and the depth of their understanding regarding statistics and machine learning.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

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