Course detail

Brain Computer Interface

FIT-BRIaAcad. year: 2025/2026

Brain-Computer Interfaces relate to technologies that allow the recording of the brain neural activity. This consequently facilitates a direct communication between the brain and the object to be directed (controlled) through the analysis and interpretation of brain neural activity. The applications can be clinical as well as non-clinical in nature. Clinical applications may deal with treating brain related disorders like motor dysfunction (for example, a person may find it difficult to walk). Non-clinical applications may deal with virtual reality games, controlling other objects using brain waves etc. All these applications are continuously expanding because of advancements in BCI technology.

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)

Entry knowledge

Not applicable.

Rules for evaluation and completion of the course

Project (implementation demo, presentation, report), Final Exam

Project (implementation demo, presentation, report), computer lab assignments within due dates. The minimal number of points which can be obtained from the final exam is 20. Otherwise, no points will be assigned to a student. If a reported barrier prevents the student from defending the project, the student will be allowed to defend the project on an alternative date. The minimal number of points which can be obtained from the project is 24 . Otherwise, no points will be assigned to a student

Aims

  • To understand the principles of working of brain and basic concepts of brain computer interface.
  • To be able to design and implement a brain computer interface and record the neural activity.
  • To be able to analyze and interpret the neural activity and use it for various applications.

Students will be able to design and utilize brain computer interfaces (BCI) for recording brain neural activity. They will be able to analyze and interpret the neural activity for clinical applications like controlling a wheelchair and non-clinical applications like controlling the movements of a character in a game.

Understanding the brain neural activity and its utilization for directing (controlling) some external activity.

 

 

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Aamir S. Malik, Hafeezullah Amin, Designing EEG Experiments for Studying the Brain: Design Code and Example Datasets, Academic Press, First edition, 2017, ISBN: 978-0128111406.
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.
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.
Guido Dornhege, Toward brain-computer interfacing, MIT Press, First Edition, 2008, ISBN: 978-0262042444.
Jonathan Wolpaw, Elizabeth Winter Wolpaw, Brain Computer Interfaces: Principles and practice, Oxford University Press, First Edition, 2012, ISBN: 978-0195388855.
M. Bear, B. Connors, and M. Paradiso, Neuroscience: Exploring the Brain, Jones & Bartlett Learning, Fourth Edition, 2020, ISBN: 978-1284211283.
Mike X. Cohen, Analyzing neural time series data: Theory and practice, MIT Press, First Edition, 2014, 978-0262019873.

Recommended reading

Christoph Guger, Brendan Z. Allison, Michael Tangermann (eds.), Brain-Computer Interface Research: A State-of-the-Art Summary 9, Springer, First Edition, 2021, ISBN: 978-3030604592.
Jonathan Wolpaw, Elizabeth Winter Wolpaw, Brain Computer Interfaces: Principles and practice, Oxford University Press, First Edition, 2012, ISBN: 978-0195388855.
M. Bear, B. Connors, and M. Paradiso, Neuroscience: Exploring the Brain, Jones & Bartlett Learning, Fourth Edition, 2020, ISBN: 978-1284211283.
Mike X. Cohen, Matlab for brain and cognitive scientists, MIT Press, First Edition, 2017, ISBN: 978-0262035828.
Nidal Kamel, Aamir S. Malik, EEG/ERP Analysis: Methods and Applications, CRC Press, First Edition, 2017, ISBN: 978-1138077089.
Rajesh P. N. Rao, Brain-Computer Interfacing: An Introduction, Cambridge University Press, First edition, 2013, ISBN: 978-0521769419.
Ramsey N.F. and Millán J.d.R. (eds.), Brain-Computer Interfaces (Handbook of Clinical Neurology Series), Elsevier, First Edition, 2020, ISBN: 978-0444639349.

Classification of course in study plans

  • Programme MIT-EN Master's 0 year of study, winter semester, elective

  • Programme MITAI Master's

    specialization NSEC , 0 year of study, winter semester, elective
    specialization NISY up to 2020/21 , 0 year of study, winter semester, elective
    specialization NNET , 0 year of study, winter semester, elective
    specialization NMAL , 0 year of study, winter semester, elective
    specialization NCPS , 0 year of study, winter semester, elective
    specialization NHPC , 0 year of study, winter semester, elective
    specialization NVER , 0 year of study, winter semester, elective
    specialization NIDE , 0 year of study, winter semester, elective
    specialization NISY , 0 year of study, winter semester, elective
    specialization NEMB , 0 year of study, winter semester, elective
    specialization NSPE , 0 year of study, winter semester, elective
    specialization NEMB , 0 year of study, winter semester, elective
    specialization NBIO , 0 year of study, winter semester, elective
    specialization NSEN , 0 year of study, winter semester, elective
    specialization NVIZ , 0 year of study, winter semester, elective
    specialization NGRI , 0 year of study, winter semester, elective
    specialization NADE , 0 year of study, winter semester, elective
    specialization NISD , 0 year of study, winter semester, elective
    specialization NMAT , 0 year of study, winter semester, elective

Type of course unit

 

Lecture

26 hod., optionally

Teacher / Lecturer

prof. Aamir Saeed Malik, Ph.D.

Syllabus

  1. Introduction to working of brain: This topic will introduce the various brain (anatomical) structures (like frontal, temporal lobes etc) and the functioning of the brain in terms of communication through neurons.
  2. Brain neural activity (EEG, ERP): The concepts of Electroencephalogram (EEG) and Event Related Potential (ERP) will be discussed in details as they are the foundation for brain computer interfaces (BCI).
  3. Introduction to BCI - technologies, components and types: Various BCI technologies will be discussed including FNIR, TDCS and various stimuli. Further, the components (amplifier, sensor etc) of BCI technologies will be introduced.
  4. Recording of brain neural activity: This is the most critical step in BCI as any BCI activity depends on the quality of the data captured from the scalp. Various montages like 10-20 system, references (like ear lobe) and other data capturing steps (like ensuring good contact etc) will be elaborated.
  5. Identifying & rectifying artefacts: The data captured from the scalp includes various physiological artefacts (like eye movements etc) as well as non-physiological artefacts (like line noise etc). It will be taught on how to identify and rectify these artefacts.
  6. Source localization techniques: It is important to know the origin of source in the brain - where the signal is being produced. Various inverse methods (like LORETA etc) will be introduced to teach the source localization from EEG signals.
  7. Feature Extraction for BCI: This topic will introduce EEG data analysis for feature extraction in time domain (like entropy etc), frequency domain (like spectral analysis etc) and time-frequency analysis using wavelet transform.
  8. Connectivity for BCI: The concept of brain networks (like resting state network etc) will be introduced and corresponding connectivity measures will be discussed. Both the functional as well as effective connectivity will be taught.
  9. Microstates for BCI: The advantage of EEG is its temporal resolution. The method of microstates will be taught that exploits the temporal resolution by finding stable brain states between 30 to 100ms.
  10. Using machine learning for BCI: The application of machine learning in BCI will be taught with respect to the various features extracted (like microstates, connectivity etc). In addition, the potential as well as the limitations of deep learning in BCI will be discussed.
  11. Clinical (medical) applications of BCI: Various clinical applications (like controlling a wheel chair, moving a prosthetic limb etc) will be introduced during this lecture.
  12. Non-Clinical (non-medical) applications of BCI: Various non-clinical applications (like controlling characters in a video game, flying a quadcopter etc) will be introduced.
  13. Future of BCI: The final lecture of the course will discuss the latest trends in BCI as well as the future applications of BCI in various fields (like rescue services, aviation, freight etc).

Laboratory exercise

8 hod., optionally

Teacher / Lecturer

Ing. Barbora Blašková
Muhammad Asad Zaheer
Yasir Hussain

Syllabus

  1. Record EEG signals
  2. EEGLab Demonstration
  3. ERP experiment (AEP, VEP, MMN)
  4. Controlling an object

Project

18 hod., optionally

Teacher / Lecturer

prof. Aamir Saeed Malik, Ph.D.
Muhammad Asad Zaheer
Yasir Hussain

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.