Print

2023　Biological Signal Processing and Its Application to Medicine and Engineering

Font size  SML

Register update notification mail Add to favorite lecture list
Academic unit or major
Graduate major in Artificial Intelligence
Instructor(s)
Yoshimura Natsue 
Class Format
Lecture    (HyFlex)
Media-enhanced courses
Day/Period(Room No.)
Tue5-6(G1-106(G112))  Fri5-6(G1-106(G112))  
Group
-
Course number
ART.T554
Credits
2
Academic year
2023
Offered quarter
4Q
Syllabus updated
2023/8/30
Lecture notes updated
-
Language used
English
Access Index

Course description and aims

By the end of this course, one will learn about different types of biological signals and their signal processing methods, as well as recent trends in applying the results as medical diagnostics and engineering interfaces. In particular, many topics using brain activity signals will be covered and students will learn to understand the differences in extractable information depending on measurement and processing methods, and how such information can contribute to medicine and society.

Student learning outcomes

The goal is to learn the types of biological signals and signal processing methods, to understand the contents of the obtainable information, and to acquire thinking that can be used for medical care and society.

Keywords

time-series signal processing, neuroscience, biomedical engineering, machine learning, and brain-machine interfaces

Competencies that will be developed

✔ Specialist skills Intercultural skills Communication skills ✔ Critical thinking skills Practical and/or problem-solving skills

Class flow

Lecture materials (slides) and exercises will be used to explain the types and mechanisms of biological signals and examples of their application in medicine and engineering. In workshop sessions, students will experience with biosignal-based interface demonstrations, lectures by experts, or presentations on research papers they have searched on their own.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Introduction Overview of biological signals and understanding of the aims of this lecture. Determine workshop schedule assignments and literature for students' presentations.
Class 2 Biosignals Understand the mechanism of generation and measurement of biological signals such as electrocardiogram, electromyogram, and electroencephalogram
Class 3 Physiological indices (autonomic nervous system） Understand the information represented by heart rate, blood pressure, pupil diameter, muscle contraction, perspiration, and peripheral skin temperature.
Class 4 Electrocardiogram (ECG) Understand the concepts of machine learning that are necessary to make biological signals useful for medical or engineering applications.
Class 5 Electromyogram (EMG) Understand how to measure EMG signals and the interfaces based on them for medical and engineering applications.
Class 6 Hemodynamic response signals (fMRI) Understand the measurement principles of functional magnetic resonance imaging and their medical and engineering applications.
Class 7 Neuronal signals and electroencephalogram (EEG) Understand the functional overview of each brain region through findings from neurons and EEG measurements.
Class 8 Signal preprocessing and feature extraction Understand the preprocessing and feature extraction methods commonly used to extract information from neurons and EEG signals using machine learning.
Class 9 Extraction of motion information Understand examples of using EEG, neuronal activity, and fMRI to extract motor information.
Class 10 Extraction of language information Understand examples of using EEG, neuronal activity, and fMRI to extract language information.
Class 11 Brain-machine interfaces Understand brain-machine interfaces using basic phenomena derived from EEG such as P300, SSVEP, etc.
Class 12 Workshop 1 Demonstration of biological signal-based interfaces or lectures by an expert
Class 13 Workshop 2 Students introduce peer-reviewed papers related to this course and discuss them.
Class 14 Workshop 3 Students introduce peer-reviewed papers related to this course and discuss them.

Out-of-Class Study Time (Preparation and Review)

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.

Textbook(s)

None. Lecture materials will be given in the class.

Reference books, course materials, etc.

None.

Assessment criteria and methods

Reports and output from the exercises and workshops.

Related courses

  • XCO.T489 ： Fundamentals of Artificial Intelligence
  • XCO.T487 ： Fundamentals of Data Science
  • XEG.G301 ： Statistics for Data Science
  • LAS.I131 ： Basics of Data Science and Artificial Intelligence

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Must be able to prepare a PC or an environment with Matlab and Python and know their basic operations.

Other

Exercises are sometimes carried out using Google Colaboratory. Students are required to get Google accounts and to get ready for using functions of "file upload/download" in Google Drive.

Page Top