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EEG signal background and real-time processing

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These are the slides that I presented at the first Brain Control Club hackathon in Paris, see http://cri-paris.org/scientific-clubs/brain-control-club/

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EEG signal background and real-time processing

  1. 1. EEG signal background and real-time processing Robert Oostenveld r.oostenveld@donders.ru.nl Donders Institute, Radboud University, Nijmegen, NL Karolinska Institute, Stockholm, SE
  2. 2. Outline Brain activity and how to measure it The source of EEG signals Controlling alpha oscillations Realtime processing and closed-loop systems
  3. 3. Brain activity spiking activity (local) field potentials magnetic fields blood flow blood oxigenation biochemical concentrations
  4. 4. Methods to record brain activity (sharp-tipped electrodes) multi electrode array (c.f. Utah array) sEEG multitrodes ECoG surface electrodes (voltage sensitive dyes) scalp EEG MEG NIRS arterial spin labeling BOLD MRS
  5. 5. EEG Instrumentation
  6. 6. Recording EEG
  7. 7. Outline Brain activity and how to measure it The source of EEG signals Controlling alpha oscillations Realtime processing and closed-loop systems
  8. 8. pre-synaptic action potential post-synaptic potential
  9. 9. electric current
  10. 10. Superposition of source activity
  11. 11. Standard electrode placement
  12. 12. High-density electrode placement
  13. 13. Outline Brain activity and how to measure it The source of EEG signals Controlling alpha oscillations – and others Realtime processing and closed-loop systems
  14. 14. Adapted from Jensen & Mazaheri (2010) Frontiers Neurosci. a b c
  15. 15. Adapted from Jensen & Mazaheri (2010) Frontiers Neurosci. a b c
  16. 16. Adapted from Jensen & Mazaheri (2010) Frontiers Neurosci. a b c High alpha = inattention Low alpha = attending
  17. 17. Other brain signals used in EEG-BCI pay attention to one feature, ignore others SSVEP – steady state visual evoked potential P300 – positivity around 300 ms after stimulus imagine movements Mu rhythm – mix of 10 and 20 Hz over sensory-motor regions
  18. 18. Outline Brain activity and how to measure it The source of EEG signals Controlling alpha oscillations Realtime processing and closed-loop systems
  19. 19. Conventional experiment M/EEG, fMRI,... data source analysis stimulus presentation hard disk hard disk collect many data from many subjects, analyse later
  20. 20. Realtime experiment / BCI loop • Challenge: timely handling of incoming data, preprocessing, analysis, sending outputs M/EEG, fMRI,... data source preprocessing feature extract. analysis stimulus presentation
  21. 21. Buffering relaxes timing constraints... • analysis side can pick data when convenient • can look back in time if needed M/EEG, fMRI,... data source analysis software stimulus presentation datastream or ringbuffer
  22. 22. ... and facilitates talking to different devices more easily MEG: CTF, Neuromag fMRI: Siemens EEG: TMSI, Biosemi, OpenBCI NIRS: Artinis analysis software FieldTrip buffer ECoG: Neuralynx, Micromed
  23. 23. Multiple applications for analysis etc. applications can communicate through buffer („events“) FieldTrip buffer analysis software analysis software other platforms online display MEG: CTF, Neuromag fMRI: Siemens EEG: TMSI, Biosemi, OpenBCI NIRS: Artinis ECoG: Neuralynx, Micromed
  24. 24. Extends naturally to pipelines EEG system „raw“ EEG data translate into control signals filter & re-reference „clean“ EEG data monitor data quality e.g. drift loose electrodes
  25. 25. Concepts of pipeline sequence artifact detection spectral estimation classification or regression raw data “control” signal many numbers few numbers
  26. 26. amplifier analysis computer storage Controlled device Experimental control Therapeutic operator Subject data feedback signal EEG controlsignal data Optimization Behavior, analysis and control signals In green the basic minimal BCI setup. In pink the additional feedback signal for a neurofeedback system. In orange the registration and storage of all data to reconstruct and analyze the neurofeedback system. In yellow the optimization of the analysis and/or control. In red the control of the experimentor or therapist. controlfeedback
  27. 27. Python modules Redis FieldTrip buffer USB CV/gate web server USB-MIDI interface OpenBCI interface audience/subject artist/performer scientist/engineer

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