New Bulgarian University > Center for Cognitive Science > Preparatory Program > Course Description

COG560 Brain Electrical Signals Related to Cognitive Processes
  1. Aims:
    Description of the paradigms for obtaining the human EEG patterns related to cognitive processes, methods and algorithms for their measurements, processing and analysis.

  2. Objectives:

    Some kind of cognitive processes related to internal motivation and volition (e.g. expectancy of an event, intention end preparation of a voluntary action) and mental tasks performance are accompanied by specific patterns of the brain electrical activity. Widely spread concept is that the electrophysiological correlates of the cognitive processes are slow-wave, trend-like, scalp-recorded brain electrical signals (BES), i.e. Contingent Negative Variation, Late Positive Complex, Slow Waves and the Readiness Potentials. However, besides the slow wave-forms, short-term transitions have been revealed recently that could be interpreted as boundaries between different states of the brain activity during cognitive task performance.

    The course is aimed at exhibiting the current "stage of art" for objective estimation of cognitive processes by BES. The modern technology for determination of the characteristics of all these patterns is described. Pitfalls in their direct interpretation as features of the cognitive processes are discussed as well.

    On completion of the course students should be able to:
    1. choose a strategy for recording and measuring appropriate patterns of BES related to a chosen cognitive task;
    2. use the basic statistical methods for estimation of significant changes in BES characteristics;
    3. be acquainted with the basic ideas and methods of non-linear (chaotic) dynamics and their current application to BES analysis.


  3. Learning strategies:
    The main strategy is to come from general overview to deep understanding. The main topics are presented in three stages.

    The 1st stage: the lectures represent a general description of the basic ideas, classical BES patterns (slow-wave transitions -ST) and unsolved problems. It includes first 5 lectures. The students are dealing with guided reading. Before each next lecture a short tutor-led discussion is performed. The stage finishes with individual preparation of a short overview on selected topic.

    The 2nd stage: the main BES patterns are described again, but in the scope of measuring and data processing. Students participate in real experiment with BES data collection.

    The 3rd stage is directed to the practice, i.e. student's participation in the real and/or simulated study of BES, including computer data processing.

    * The activity in 3rd stage is distributed between topics in the 2nd stage.

  4. Overall duration and format:
    One semester (15 weeks), 15 lectures (2 hours'lecture/week) and 3 practical exercises (seminars) in laboratory.

  5. Credit hours: 3.

  6. Lecturer: David Popivanov, Ph.D.

  7. Literature:
    Barrett, G., Shibasaki, H. and Neshige, R.,

    Babloyantz, A.,

    Basar, E., Basar-Eroglu, C., and Roschke, J.,

    Bonnet, M., Requin, J., and Stelmach, G. E.,

    Box, G.E.P., and Jenkins, G.M.,

    Cerutti, S., Chiarenza, G., Liberati, D., Liberati, P., and Pavesi, G.,

    Childers, D.G., Aunon, J.I., and McGillem, C.D.,

    Decke, L., Grozinger, B., and Kornhuber, H.H.,

    Fried, I., Katz, A., McCarthy, G., Sass, K.J., Williamson, P., Spenser, S.S., and Spenser, D.D.,

    Gevins, A.S. and Cutillo, B.A.,

    Goodman, D. and Kelso, J.A.S.,

    Haider, M., Crollknapp, E., and Ganglberger, J.A.,

    Hashimoto, S., Gemba, H., and Sasaki, K.,

    Jambu, M.,

    Kalaska, J.F. and Crammond, D.J.,

    Klapp, S.T.,

    Kashyap, R.L. and Rao, A.R.,

    Kristeva, R., Cheyne, D., Lang, W., Lindinger, G., and Deecke, L.,

    Lepine, D., Glencross, D., and Requin, J.,

    Libet, B.,

    Lurito, J.T., Georgakopoulos, T., and Georgopoulos, A.P.,

    MacKay, W.A. and Bonnet, M.,

    Mayer-Kress, G. and Holzfuss, J.,

    Pfurtscheller, G.,

    Pijn, J.P., Van Neerven, J., Noest, A., Lopes da Silva, F.H.,

    Popivanov, D.,

    Popivanov, D.,

    Popivanov, D., Mineva, A., and Dushanova, J.,

    Pritchard, W.S. and Duke, D.W.,

    Rapp, P.E., Bashore, T.R., Martinerie, J.M., Albano, A.M., et al.,

    Rosenbaum, D.A.,

    Ruchkin, D.S. and Sutton, S.,

    Ruchkin, D.S., Sutton, S., Mahhafey, M., and Glaser, J.,

    Ruchkin, D.S., Johnson, R., Jr., Canoune, H., and Ritter, W.,

    Saltzberg, B., Burton, W.D., Skinner, J.E.,

    Skinner, J.E., Molnar, M.,

    Tarkka, I.M. and Hallet, M.,

    Vaz, F., DeOliviera, P.G., and Principe, J.C.,

    Walenstein, G.V., Nash, A.J.,


  8. Course outline:
    The course includes 15 lectures and 3 laboratory practical exercises, grouped in 3 parts as follows:


  9. Main Topics:

    Stage 1:

    Topic 1: Introductory overview on the EEG, evoked potentials and ST. Definitions. Signal, noise and cognitive task. Paradigm and pattern. Recording sites and data collection, equipment requirements.
    Required reading: Lecture
    Additional reading:


    Topic 2: Contingent negative variation (CNV). Meaning, paradigms, leading sites. measurements, reliability. electrogenesis. Psychological aspects. Effect of maturation, drugs. Interpretation of typical mixed effects (e.g. with RP, LPC).
    Required reading:

    Additional reading:


    Topic 3: Late Positive Complex (LPC). P300, N200, P3a, P3b, N400. Paradigms. Typical experimental setup. Meaning, reliability, overlapping effects. Cognitive aspects. Psychological aspects. Age effect.
    Required reading:

    Additional reading:


    Topic 4: Readiness potential (RP). Meaning, paradigms. Voluntary movement preparation. Measurement and reliability. Averaging vs. single-trial approach. Electrogenesis. Motor commands. Experimental set up. Conscious and unconscious phase. Relation with movement parameters - holistic vs. parametric concept.
    Required reading:

    Additional reading:


    Topic 5: Slow waves (SW). Discussion on the effects of overlapping. Comparison between RP, CNV, SW and LPC. Ruchkin's approach. General discussion on the effects of the experimental design.
    Required reading:

    Additional reading:


    Stage 2:

    Topic 6: Data measurement and preprocessing specificity. Base line, reference electrode, Laplacian derivation. Spatial analysis. Source density analysis.
    Required reading:

    1st Practical exercise (training): Participation in a real electrophysiological experiment. Data collection in computer.

    Topic 7: Basic statistical methods and algorithms. Distributions, histograms. Means, variance, ANOVA. Tests for significance.
    Required reading: Lecture.

    Topic 8: Correlation and spectral estimates. Deterended fluctuation analysis.
    Required reading:

    2nd Practical exercise (training): Participation data processing. Demonstrations using computer in dialogue mode.

    Topic 9: Introduction to time series analysis. Recursion algorithms (Burg's algorithm). Model class selection. Adequacy of the model testing.
    Required reading:


    Topic 10: Approximations. "Best fit". Least squares technique. Chebishev polynomial. Maximum Likelihood Method.
    Required reading:


    Topic 11: Overview on stochastic dynamical approach for identification of BES. Practical acquaintance with experimental setup, data recording, preprocessing and analysis.
    3rd Practical exercise (training): Participation data processing. Demonstrations using computer in dialogue mode.

    Topic 12: Stochastic dynamical approach for identification of RP. Practical ac-quaintance with experimental setup, data recording, preprocessing and analysis
    Required reading:

    Additional reading:


    Topic 13: Introduction to chaotic dynamics. Classical measures. Effect of initial conditions. Examples of recent application for EEG data analysis.
    Required reading:


    Topic 14: Recurrent algorithms for detection of chaotic segments embedded in colored (EEG-like) noise. Examples. Computer simulations.
    Required reading:

    Additional reading:


  10. Assessment:
    The knowledge obtained will be evaluated by:
    Test #1 - (after the lecture #5): To read selected paper(s) in the chosen topic (CNV, LPC, RP, etc.) and write a commentary at home on the basic concepts, methods and procedures, results and discussion. Open defence within the group.
    Test #2 - (after the lecture #11): Partial exam on the measurements, processing and statistical analysis of ST.
    Test #3 (at the end): Practical exam on conducting an experiment and computer data processing.
    Test #4: Final Examination on the whole material.
    Additional requirement for those who wish to prepare Master Thesis based on COG 560: Preparation of a short project and defence.

    Grading procedure:
    Test #1: 25%
    Test #2: 35%
    Test #3: 40%
    Test #4: Final estimate on the intermediate tests. 100%
    Final estimate on the Project.

  11. Prerequisites:
    COG417 or COG418, COG376.

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