EEG signal background and real-time processing
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This document discusses EEG signal background and real-time processing. It begins by describing different methods of measuring brain activity, including EEG. It then discusses the source of EEG signals and how they are generated by synaptic activity and summed across electrodes. The document outlines controlling alpha oscillations and using them in brain-computer interfaces. Finally, it discusses real-time processing and closed-loop systems, including buffering data, connecting different recording devices, and creating online analysis pipelines to generate control signals.
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EEG signal background and real-time processing
- 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. Outline Brain activity and how to measure it The source of EEG signals Controlling alpha oscillations Realtime processing and closed-loop systems
- 3. Brain activity spiking activity (local) field potentials magnetic fields blood flow blood oxigenation biochemical concentrations
- 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
- 7. Outline Brain activity and how to measure it The source of EEG signals Controlling alpha oscillations Realtime processing and closed-loop systems
- 12. electric current
- 13. Superposition of source activity
- 16. Outline Brain activity and how to measure it The source of EEG signals Controlling alpha oscillations – and others Realtime processing and closed-loop systems
- 17. Adapted from Jensen & Mazaheri (2010) Frontiers Neurosci. a b c
- 18. Adapted from Jensen & Mazaheri (2010) Frontiers Neurosci. a b c
- 19. Adapted from Jensen & Mazaheri (2010) Frontiers Neurosci. a b c High alpha = inattention Low alpha = attending
- 20. 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
- 21. Outline Brain activity and how to measure it The source of EEG signals Controlling alpha oscillations Realtime processing and closed-loop systems
- 22. Conventional experiment M/EEG, fMRI,... data source analysis stimulus presentation hard disk hard disk collect many data from many subjects, analyse later
- 23. 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
- 24. 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
- 25. ... 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
- 26. 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
- 27. 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
- 28. Concepts of pipeline sequence artifact detection spectral estimation classification or regression raw data “control” signal many numbers few numbers
- 29. 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
Editor's Notes
- What do you need for an EEG experiment? - Electrodes (cap), amplifier, acquire and monitor data with computers, subject, task, programmed experiment Accurate timing and responses (for experimental but not clinical) Electrode position measurement Impedance, gel Noise free environment (no flourescent light, 50 Hz noise) Comfortable (muscle/eyes…) EEG system does 2 things in order for recorded data to match the signal capability of the Analog to digital converter: Amplify signal (2) convert from Analog to Digital Back in the day, needed two separate electronics to do this. Digital electronics worked at mV, therefore need to amplify recorded signal so that it could be read between -5 to 5 volts (recorded signal at mV is too small to span range) - If it did span the range, then it didn’t span the whole range. Now a days more options are available such that less amplification is required Ground: prevent noise from interfering with potentials of interest Buffer electrodes of interest relative to ground, to get stable signal (less sensitive to imperfections). Use reference electrodes because amplifier are imperfect
- Long duration Mobile Same temporar resolution Cheaper 10,000 – 100,000 vs. Few million