| Academic Year |
2026Year |
School/Graduate School |
Graduate School of Advanced Science and Engineering (Master's Course) Division of Advanced Science and Engineering Informatics and Data Science Program |
| Lecture Code |
WSN23801 |
Subject Classification |
Specialized Education |
| Subject Name |
生体信号情報処理特論 |
Subject Name
（Katakana） |
セイタイシンゴウジョウホウショリトクロン |
Subject Name in
English |
Advanced Biomedical Signal Processing |
| Instructor |
FURUI AKIRA |
Instructor
(Katakana) |
フルイ アキラ |
| Campus |
Higashi-Hiroshima |
Semester/Term |
1st-Year, First Semester, 2Term |
| Days, Periods, and Classrooms |
(2T) Weds1-4 |
| Lesson Style |
Lecture |
Lesson Style
(More Details) |
Face-to-face, Online (simultaneous interactive), Online (on-demand) |
| |
| Credits |
2.0 |
Class Hours/Week |
4 |
Language of Instruction |
B
:
Japanese／English |
| Course Level |
6
:
Graduate Advanced
|
| Course Area（Area） |
25
:
Science and Technology |
| Course Area（Discipline） |
02
:
Information Science |
| Eligible Students |
|
| Keywords |
|
| 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 |
This course covers the measurement, processing, and recognition of biosignals and their engineering applications in academia and industry. Various biosignal information, such as electroencephalograms (EEG), electromyograms (EMG), and electrocardiograms (ECG), can be measured from the human body. Recognizing patterns from these signals is expected to lead to practical applications in the real world. To achieve this, the use of appropriate signal processing, probabilistic modeling, and artificial intelligence techniques including deep learning is essential. This course provides an overview of specific challenges in these related fields and the latest research topics for addressing them. |
| Class Schedule |
Lecture 1: Course Introduction and Guidance
Lecture 2: Measurement Principles and Preprocessing of Biosignals
Lecture 3: Signal Analysis and Feature Extraction
Lecture 4: Report Assignment on Lecture 3
Lecture 5: Probabilistic Modeling and Bayesian Learning
Lecture 6: Report Assignment on Lecture 5
Lecture 7: Machine Learning for Biosignal Recognition
Lecture 8: Report Assignment on Lecture 7
Lecture 9: Cross-Subject Adaptation
Lecture 10: Report Assignment on Lecture 9
Lecture 11: Continual Learning
Lecture 12: Report Assignment on Lecture 11
Lecture 13: Applied Developments
Lecture 14: Report Assignment on Lecture 13
Lecture 15: Course Summary
Assessment through periodic report assignment. |
Text/Reference
Books,etc. |
None |
PC or AV used in
Class,etc. |
Text, Handouts, Microsoft Teams, Microsoft Forms |
| (More Details) |
|
| Learning techniques to be incorporated |
|
Suggestions on
Preparation and
Review |
Lecture materials will be distributed. Reviewing them before and after each lecture will help deepen your understanding of the content. |
| Requirements |
|
| Grading Method |
Students will be evaluated based on periodic report assignments. |
| 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. |