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    A group can consist of 2-3 persons enrolled in the class. Each group will pick a particular "mini-research" topic pertinent to this course and investigate the topic through literature search and/or analysis of available BCI data or collecting new data with the help of our TA and BCI researchers in the CSE department. Each group will present their results to class on finals day Monday June 4 (in room CSE 305 10:30am) and will email to Sam and Raj a short report (5-10 pages including figures and references) by Thursday June 7 midnight.

Presentation details: You can bring your own laptop for your presentation on June 4 but in case one is not available, feel free to email Raj to load your presentation (Powerpoint only) on the class laptop.

Project Topics

This is a partial list of possible topics. Contact Raj and Sam once you have decided what you would like to do and if you would like additional team members to be added to your team.
  • EEG-Based BCI

    Offline analysis of EEG data

    1. Data from the UW BCI Group: We have previously collected data from EEG experiments involving SSVEP as well as motor imagery. Your goal will be to use feature extraction (e.g., FFT) with a classification algorithm of your choice to classify the subject's choice. In the case of SSVEP, you need to classify the frequency of the visual stimulus the subject is looking at. In the case of imagery, you need to classify either imagery from the "rest" period or the type of motor imagery (hand versus foot). Note that classification of SSVEP is typically much easier than classification of imagery data.
    2. Data from BCI Competition: This includes datasets from a variety of tasks. Pick your favorite dataset, and either implement one of the submitted solutions, or compare the winning solution with your favorite algorithm.

    Closed-loop BCI using our EEG recording system

    If you obtain good preliminary results from offline analysis, you can try using your method in an online SSVEP/imagery-based BCI task using our EEG system. Contact Raj and Sam if your team is interested in pursuing this option.

  • ECoG-Based BCIs

    The UW BCI group has a large number of ECoG datasets where the patient performed motor actions or engaged in motor imagery of specific actions. The goal is to extract appropriate features from raw ECoG data and build classifiers that can distinguish between different motor actions. We will provide you with a small number of datasets, and your approach can be tested on the other (unseen) datasets. Possible directions to investigate are given below.

    1. Spatial Filters for ECoG Data: Classification techniques use spatial filters for integrating information from multiple sensors and creating a lower-dimensional feature space that is more tractable for classification. The project would examine one of the following filters (references [3-8]):

      a) CSP—a supervised technique for selecting linear projections.
      b) PCA/ICA—unsupervised techniques that are useful in dimensionality reduction,
      c) CSSSP - a supervised technique that simultaneously optimizes the spatial and frequency filters used on the signals.
      d) an algorithm of your choice.

    2. Spectral Filters for ECoG Data: We know that motor activity or imagery is manifested in the power spectrum as changes in the power of particular frequency bands—e.g., the so-called mu band. Here we will examine algorithmic techniques for “fitting” a frequency band filter on ECoG data in order to maximise the discriminability of the resulting data. We will keep the choice of classifier fixed. References [3-6, 9].

    3. Classification Algorithms: Here the features are kept fixed (e.g., simple FFT-based band power features) and the goal is to compare two classification algorithms.
  • Reading Projects

    You will read a set of closely related papers investigating a topic of interest to BCI and make a presentation to the class explaining the topic, summarising/comparing the papers, and suggesting possible directions for future work and improvements. Possible topics to explore:

    1. Estimating underlying neural activity from EEG and its potential use in BCI. See refs [1,2].
    2. EEG artifact modeling/rejection. A significant problem in the analysis of EEG data is its extreme sensitivity to artifacts arising from muscle activity, eyeblinks and movement. What techniques do people use to deal with these artifacts? Can they be removed from the EEG? This can also be converted into data analysis project with our EEG recording system. See refs [18-21].
    3. Learning, longitudinal studies, rehab issues, etc.: What does it take to build an EEG-based BCI? Who can use one? How do things change over time? See refs [22-25].
    4. Leveraging other sources of infortion such as phase synchrony, presence of an error potential etc. [26-30].
    5. Neural correlates of motor learning: Psychophysical research literature aims to build qualitative models of human sensorimotor learning. Evidence of learning can also be observed at the neural level. Can we also do this at the EEG level? See refs [15-17].
    6. Other paradigms for BCI: This could be either a different interaction scheme, e.g., steady-state somatosensory evoked potentials, or a different recording scheme--e.g., fMRI, MEG, fNIR, etc.


These are some of the older classic references that initiated research on the topics above. Be sure to use PubMed and Google Scholar to include in your report more recent work on the topic you choose.
  1. Non-Invasive Estimation of Local Field Potentials for Neuroprosthesis Control, R. Grave de Peralta Menendez, S. Gonzalez Andino, L. Perez, P.W. Ferrez, and J. del R. Millan, in Cognitive Processing, Special Issue on Motor Planning in Humans and Neuroprosthesis Control.
  2. Electrical neuroimaging based on biophysical constraints. Grave de peralta menendez R, Murray MM, et al. Neuroimage 21 (2004)
  3. A brain-computer interface using electrocorticographic signals in humans. Eric C Leuthardt, Gerwin Schalk, Jonathan R Wolpaw, Jeffrey G Ojemann and Daniel W Moran. J. Neural Eng. 1(2), 2004.
  4. Optimizing spatio-temporal filters for improving Brain-Computer Interfacing. Guido Dornhege, Benjamin Blankertz, Matthias Krauledat, Florian Losch, Gabriel Curio, Klaus-Robert Mueller, NIPS 05.
  5. Pregenzer, M.; Pfurtscheller, G.: Frequency Component Selection for an EEG-Based Brain Computer Interface (BCI). . in: IEEE Trans Rehab Engg. (1999) S.
  6. Ramoser, H.; Muller-Gerking, J.; Pfurtscheller, G.: Optimal spatial filtering of single trial EEG during imagined hand movement. in: IEEE transactions on rehabilitation engineering 8 (2000), 441-446
  7. Introduction to ICA by Arnaud Delorme
  8. Jung T-P, Makeig S, Lee T-W, Mc Keown? MJ, Brown G, Bell AJ, and Sejnowski TJ, Independent Component Analysis of Biomedical Signals. Second International Workshop on Independent Component Analysis and Signal Separation, Helsinki, pp. 633-44, 2000. link
  9. Pfurtscheller, G.; Lopes da Silva, F.: Event-related EEG/MEG synchronization and desynchronization: Basic principles. in: Journal of clinical neurophysiology 110 (1999)
  10. Guido Dornhege, Benjamin Blankertz, Gabriel Curio, and Klaus-Robert Muller. Boosting bit rates in non-invasive EEG single-trial classifications by feature combination and multi-class paradigms. IEEE Trans. Biomed. Eng., 51(6):993-1002, 2004.
  11. Schlogl, A.; Lee, F. Y.; Bischof, H.; Pfurtscheller, G.: Characterization of Four-Class Motor Imagery EEG Data for the BCI-Competition 2005. in: Journal of neural engineering 2 (2005)
  12. Robust Classification of EEG Signal for Brain-Computer Interface. Thulasidas, M.; Guan, C.; Wu, J. TNSRE 14 (1) 2006.
  13. ERPs evoked by different matrix sizes: implications for a brain computer interface (BCI) system
    Allison, B.Z.; Pineda, J.A. TNSRE 2003
  14. P. Sajda, A. Gerson and L.C. Parra (2003) High-throughput Image Search via Single-trial Event Detection in a Rapid Serial Visual Presentation Task, IEEE Conference on Neural Engineering Capri Island, Italy, March 20-22, 2003
  15. M.G. Philiastides and P. Sajda (2005) Temporal Characterization of the Neural Correlates of Perceptual Decision Making in the Human Brain, Cerebral Cortex.
  16. Gandolfo, F., Li, C.-S., Benda, B.J., Padoa-Schioppa, C. & Bizzi, E. (2000) Cortical correlates of learning in monkeys adapting to a new dynamical environment. Proc. Natl Acad. Sci. USA, 97, 2259-2263
  17. Emerging Patterns of Neuronal Responses in Supplementary and Primary Motor Areas during Sensorimotor Adaptation. R. Paz, C. Natan, T. Boraud, H. Bergman, and E. Vaadia (2005). J. Neurosci. 25: 10941-10951
  18. Delorme, A., Sejnowski, T., Makeig, S. (2005) Improved rejection of artifacts from EEG data using high-order statistics and independent component analysis. link
  19. Schlogl, A.; Anderer, P.; Roberts, S. J.; Pregenzer, M.; Pfurtscheller, G.: Artefact detection in sleep EEG by the use of Kalman filtering. in: European Medical and Biological Engineering Conference EMBEC (1999), 1648-1649.
  20. Roberts, S. J.; Everson, J.; Rezek, I.; Anderer, P.; Schlogl, A.: Tracking ICA for EEG Eye Movement Artifact Removal. in: European Medical and Biological Engineering Conference EMBEC (1999)
  21. Jung T-P, Makeig S, Westerfield M, Townsend J, Courchesne E, and Sejnowski TJ. Removal of eye activity artifacts from visual event-related potentials in normal and clinical subjects. Clinical Neurophysiology 111(10), 1745-58, 2000.
  22. How many people are able to operate an EEG-based brain-computer interface (BCI)? Guger, C.; Edlinger, G.; Harkam, W.; Niedermayer, I.; Pfurtscheller, G. TNSRE 2003
  23. Learning to control brain rhythms: making a brain-computer interface possible
    Pineda, J.A.; Silverman, D.S.; Vankov, A.; Hestenes, J. TNSRE 2003.
  24. EEG changes accompanying learned regulation of 12-Hz EEG activity. Delorme, A.; Makeig, S. TNSRE 2003.
  25. Evolution of the Mental States Operating a Brain-Computer Interface, J. Mourino, S. Chiappa, et al. in Proceedings of the International Federation for Medical and Biological Engineering, 2002
  26. Phase Synchrony Rate for the Recognition of Motor Imagery in Brain-Computer Interface. Le Song, Evian Gordon, Elly Gysel. NIPS 2005
  27. Brunner, C.; Graimann, B.; Huggins, J. E.; Levine, S. P.; Pfurtscheller, G.:
    Phase relationships between different subdural electrode recordings in man. in: Neuroscience letters 375 (2005) 2 , S. 69-74
  28. Response error correction-a demonstration of improved human-machine performance using real-time EEG monitoring. Parra, L.C.; Spence, C.D.; Gerson, A.D.; Sajda, P. TNSRE 2003
  29. Schalk, G.; Wolpaw, J.; MCFarland, J.; Pfurtscheller, G.: EEG-based communication: presence of an error potential. in: Clinical neurophysiology (2000) , S. 2138-2144
  30. You are wrong! Automatic detection of errors from Brain Waves. P. W. Ferrez, J. del R. Millan. IJCAI 2005.

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