Course detail

Science of happiness

FIT-IMHaAcad. year: 2024/2025

This course deals with the application of computational methods in the domain of mental health. Through this course, the students will not only learn to appreciate the importance of mental health and associated issues but they will also be empowered and will become confident to tackle them through assessment and intervention techniques. The course first introduces the biology of mental health as well as various issues, diseases and disorders associated with it. Then various signals are discussed that can be measured from the human body. The signals can be internal like coming from heart and brain or external like speech, handwriting etc. The students are taught to analyze these signals for the assessment of mental health. Finally, the course concludes by teaching intervention methods like biofeedback and neurofeedback that allows to deal and manage with mental health issues.

Language of instruction

English

Number of ECTS credits

5

Mode of study

Not applicable.

Guarantor

prof. Aamir Saeed Malik, Ph.D.

Department

Department of Computer Systems (UPSY)

Rules for evaluation and completion of the course

Midterm Exam, Project (implementation demo, presentation, report), Lab Assignments  

Controlled instruction

Project (implementation demo, presentation, report), computer lab assignments within due dates. The minimal number of points which can be obtained from the midterm exam is 10. Otherwise, no points will be assigned to a student. In the case of a reported barrier preventing the student to defend the project or solve a lab assignment, the student will be allowed to defend the project or solve the lab assignment on an alternative date. The minimal number of points which can be obtained from the project is 20. Otherwise, no points will be assigned to a student.   

 

Aims

  • To understand the foundations of mental health as well as various issues, diseases and disorders associated with it.
  • To comprehend the working of human body with emphasis on heart and brain.
  • To know how to measure the signals from human body including heart and brain.
  • To be able to analyze the signals for the assessment of mental health and associated issues like stress, anxiety, depression etc.
  • To be able to detect emotions through the analysis of signals from human speech, handwriting, face and textual input.
  • To be able to implement intervention techniques using biofeedback and neurofeedback.
  • To be able to appreciate the significance of mental health as well as the computational methods for detection, assessment and monitoring of mental health in both clinical and non-clinical domains.

Study aids

Not applicable.

Prerequisites and corequisites

Basic literature

Donald L. Schomer, Fernando Lopes da Silva (Eds.), Niedermeyer's Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, LWW, Sixth Edition, 2010, ISBN: 978-0781789424.
Erik Peper, Biofeedback Mastery: An Experiential Teaching and Self-Training, First Edition, 2009, ISBN: 9780984297900.
Ethan Meltzer, How to think like a neurologist, OUP US, First Edition.2022, ISBN: 0197576664.
F. Rieke, D. Warland, R. de Ruyter van Steveninck, and W. Bialek, Spikes: Exploring the Neural Code, MIT Press / Bradford Books, 1999, ISBN: 978-0262681087.
Jane De Burgh, Human Body: Understanding Anatomy (Mini Encyclopedia), Sterling Publishing, Second Edition, 2016, ISBN: 1782743774.
Kristin Wunderlin, 50 Things to know about depression and anxiety, Independently Published, First Edition, 2017, ISBN: 1520724845.
M. Bear, B. Connors, and M. Paradiso, Neuroscience: Exploring the Brain, Jones & Bartlett Learning, Fourth Edition, 2020, ISBN: 978-1284211283.
P. Dutta, A. Barman, Human emotion recognition from face images, Springer, First Edition, 2020, ISBN: 978-9811538827.
Ricardo Calix, Automated semantic understanding of human emotions in writing and speech, CreateSpace Independent Publishing Platform, First Edition, 2017, ISBN: 1542374626.
S. Majumder, Speech emotion recognition using machine learning, LAP Lambert Academic Publishing, First Edition, 2023, ISBN: 6206152979.
Thomas Pecherstorfer, Neurofeedback and HRV Biofeedback after Stroke, VDM Verlag, First Edition, 2009, ISBN: 3639209621.

Recommended reading

David Pilgrim, Key concepts in mental health, Sage Publications, Sixth Edition, 2022, ISBN: 1529603765.
Gernot Ernst, Heart Rate Variability, Springer, First Edition, 2013, ISBN: 1447143086.
Inna Khazan, Biofeedback and Mindfulness in Everyday Life, WW Norton & Co, First Edition, 2019, ISBN: 9780393712933.
Mike X. Cohen, Matlab for brain and cognitive scientists, MIT Press, First Edition, 2017, ISBN: 978-0262035828.
NEDU, Anatomy & Physiology Made Easy, NEDU LLC, First Edition, 2021, ISBN: 1952914167.
Nidal Kamel, Aamir S. Malik, EEG/ERP Analysis: Methods and Applications, CRC Press, First Edition, 2017, ISBN: 978-1138077089.
Rita Carter, The Brain Book, DK Publisher, First Edition, 2019, ISBN: 0241302250.
Thomas F. Collura, Technical Foundations of Neurofeedback, Routledge, First Edition, 2017, ISBN: 9781138051898.

Classification of course in study plans

  • Programme BIT Bachelor's 2 year of study, summer semester, elective
  • Programme BIT Bachelor's 2 year of study, summer semester, elective

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

prof. Aamir Saeed Malik, Ph.D.

Syllabus

  1. Introduction to mental health
  2. Working of human body
  3. Functioning of heart and brain
  4. Measurement of body signals
  5. Measurement of brain signals
  6. Subjective approach to assess mental health
  7. Emotions detection from facial expressions and speech
  8. Detecting emotions from handwriting and textual input
  9. Analyzing data for assessment of mental stress
  10. Detecting anxiety from body and brain signals
  11. Measuring signals to detect depression
  12. Biofeedback for reducing stress, anxiety and depression
  13. Neurofeedback for managing mental health

Laboratory exercise

13 hod., optionally

Teacher / Lecturer

prof. Aamir Saeed Malik, Ph.D.

Syllabus

  1. Record internal (heart, brain etc) signals from body 
  2. Analyze internal body signals using Matlab 
  3. Record external (speech, handwriting etc) signals 
  4. Analyze external body signals using Matlab 
  5. HRV based Biofeedback 
  6. Neurofeedback using frequency training 

Project

13 hod., optionally

Teacher / Lecturer

prof. Aamir Saeed Malik, Ph.D.

Syllabus

Every student will choose one project from a list of approved projects that are relevant for this course. The implementation, presentation and documentation of the project will be evaluated.