Detail předmětu

Brain Computer Interface

FIT-BRIaAk. rok: 2025/2026

Jazyk výuky

angličtina

Počet kreditů

5

Garant předmětu

prof. Aamir Saeed Malik, Ph.D.

Zajišťuje ústav

Ústav počítačových systémů (UPSY)

Pravidla hodnocení a ukončení předmětu

Základní literatura

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.

Doporučená literatura

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.

Zařazení předmětu ve studijních plánech

  • Program MIT-EN magisterský navazující 0 ročník, zimní semestr, volitelný

  • Program MITAI magisterský navazující

    specializace NSEC , 0 ročník, zimní semestr, volitelný
    specializace NISY do 2020/21 , 0 ročník, zimní semestr, volitelný
    specializace NNET , 0 ročník, zimní semestr, volitelný
    specializace NMAL , 0 ročník, zimní semestr, volitelný
    specializace NCPS , 0 ročník, zimní semestr, volitelný
    specializace NHPC , 0 ročník, zimní semestr, volitelný
    specializace NVER , 0 ročník, zimní semestr, volitelný
    specializace NIDE , 0 ročník, zimní semestr, volitelný
    specializace NISY , 0 ročník, zimní semestr, volitelný
    specializace NEMB do 2023/24 , 0 ročník, zimní semestr, volitelný
    specializace NSPE , 0 ročník, zimní semestr, volitelný
    specializace NEMB , 0 ročník, zimní semestr, volitelný
    specializace NBIO , 0 ročník, zimní semestr, volitelný
    specializace NSEN , 0 ročník, zimní semestr, volitelný
    specializace NVIZ , 0 ročník, zimní semestr, volitelný
    specializace NGRI , 0 ročník, zimní semestr, volitelný
    specializace NADE , 0 ročník, zimní semestr, volitelný
    specializace NISD , 0 ročník, zimní semestr, volitelný
    specializace NMAT , 0 ročník, zimní semestr, volitelný

Typ (způsob) výuky

 

Přednáška

26 hod., nepovinná

Vyučující / Lektor

prof. Aamir Saeed Malik, Ph.D.

Osnova

  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).
 

Laboratorní cvičení

8 hod., nepovinná

Vyučující / Lektor

Ing. Barbora Blašková
Muhammad Asad Zaheer
Yasir Hussain

Osnova

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

Projekt

18 hod., nepovinná

Vyučující / Lektor

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

Osnova

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.