Make Me Think. A Brief Introduction to BCI.

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A BRIEF INTRODUCTION TO THE BRAIN-COMPUTER INTERFACES
A brain-computer interface (BCI) is a hardware and software system that allows brain activity alone to control computers or external devices. In this short talk, I will review the state-of-the-art of BCIs, looking at the different steps that form a brain-computer interface: signal acquisition, preprocessing or enhancement, feature extraction and classification and the control interface. We will discuss different BCI types their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported so far (including commercially available devices).
At the first part, I will introduce basics of the Brain functioning and will review the neuroimaging modalities used in the signal acquisition, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Then, we will discuss different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Finally, we will overview various BCI applications that control a range of devices and discuss neurofeedback technology for medical, learning and gaming applications.

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Make Me Think. A Brief Introduction to BCI.

  1. 1. MAKE ME THINK A BRIEF INTRODUCTION TO THE BRAIN-COMPUTER INTERFACES Nikita Lukianets Neuroscience Researcher, SIGNALIFE @nikiluk #BCI #WUD2013
  2. 2. WHAT IS THE BRAIN-COMPUTER INTERFACE?
  3. 3. “ system that permits neuronal activity alone to communicate with an external device
  4. 4. A BIT OF FASHION: CONCEPT IS NOT NEW 1929: Hans Berger discovers the EEG. Studies of brain diseases 1959: David H. Hubel and Torsten Wiesel. Single neuron recordings to map the visual cortex 1967: The first record by multielectrode arrays. 1998: Kennedy and Bakay, control of a cursor.
  5. 5. BCI LOGICAL SCHEME Feature Extraction Signal Features Intention (User Effort) Intent Classification Feedback Strategy Environment User Training Machine Learning Raw signal filtering
  6. 6. FREQUENCIES mu theta beta 8-13 1-4 delta 4-7 7-13 13-30 alpha 25-100 gamma Oscillatory activity is observed throughout the central nervous system at all levels of organization. Three different levels have been widely recognized: the micro-scale (activity of a single neuron), the meso-scale (activity of a local group of neurons) and the macro-scale (activity of different brain regions)
  7. 7. NEUROIMAGING How can we record from the brain?
  8. 8. BCI CLASSIFICATION BY NEUROIMAGING ECoG Invasive Intracortical EEG fMRI Non-Invasive NIRS MEG
  9. 9. DEEP BRAIN STIMULATION/RECORDING Stimulation/recording using microelectrodes/Microarrays • Parkinson • Severe depression • Tourette Syndrome • Chronic pain
  10. 10. EEG Recording of electrical activity along the scalp. EEG measures voltage fluctuations resulting from ionic current flows within the neurons of the brain
  11. 11. ECOG Electrocorticography (ECoG), or intracranial EEG (iEEG), is the practice of using electrodes placed directly on the exposed surface of the brain to record electrical activity from the cerebral cortex. ECoG may be performed either in the operating room during surgery (intraoperative ECoG) or outside of surgery (extraoperative ECoG)
  12. 12. FMRI Detecting associated changes in blood flow • • • • Non-Invasive High precision Robust, expensive Non-portable
  13. 13. MEG The spatial distributions of the magnetic fields are analyzed to localize the sources of the activity within the brain
  14. 14. NIRS fNIR is a non-invasive imaging method involving the quantification of chromophore. NIR spectrum light takes advantage of the optical window in which skin, tissue, and bone are mostly transparent to NIR light in the spectrum of 700-900 nm, while hemoglobin (Hb) and deoxygenated-hemoglobin (deoxy-Hb) are stronger absorbers of light.
  15. 15. BCI NEUROIMAGING COMPARISON //Luis Fernando Nicolas-Alonso, Brain Computer Interfaces, a Review, Sensors 2012
  16. 16. REGISTRATION POSSIBILITIES Non-invasive 0-60 Hz Invasive 0-10 000 Hz
  17. 17. CONTROL SIGNALS What are Control signal types in BCI?
  18. 18. COMPARISON
  19. 19. STEADY STATE VISUALLY EVOKED POTENTIALS Based on “resonance” effect SSVP + Transcranial focused ultrasound (FUS, TMS) http:// www.youtube.com/watch?v=VaJjHgyHnEc
  20. 20. EVENT-RELATED POTENTIALS (P300/N400) When recorded by electroencephalography (EEG), it surfaces as a positive deflection in voltage with a latency ~300ms.
  21. 21. NEUROFEEDBACK What Modalities could be used for Feedback?
  22. 22. NEUROFEEDBACK • Appropriate feedback strategy should depend on the control signal • Incapable users may particularly benefit from positively-biased feedback • Intracortical stimulation/Optogenetics/FUS/TMS/Visual and Auditory fMRI 20 mins > EEG 30 hours
  23. 23. APPLICATIONS What are the applications of BCI?
  24. 24. APPLICATIONS User capability Entertainment Users with no severe disability Neuroprosthesis Locomotion Environment LIS users Communication CLIS users Information Transfer Rate //Luis Fernando Nicolas-Alonso, Brain Computer Interfaces, a Review, Sensors 2012
  25. 25. BEYOND MEDICAL APPLICATIONS Gaming & Entertainment Evaluation User state monitoring Training & Education Cognitive improvement Device Control Security & Safety
  26. 26. CONSUMER APPS What is available on the market today?
  27. 27. NECOMIMI Electrodos: 1 1 mental state July 2012 Neuroware
  28. 28. MINDWAVE MOBILE Electrodos: 1 2 mental states (based on 4 brainwaves), eyeblinks 21 March 2011 Neurosky .NET,Android,iPhone,Arduino
  29. 29. MINDSET Electrodos: 1 2 mental states (based on 4 brainwaves), eyeblinks 1 December 2011 Neurosky
  30. 30. MINDBALL Electrodos: 1 1 mental state 21 December 2009 Mattel (Neurosky partner)
  31. 31. STAR WARS FORCE TRAINER Electrodos: 1 1 mental state 21 June 2009 Uncle Milton (Neurosky partner)
  32. 32. XWAVE SPORT Electrodos:1 8 EEG bands 5 January 2011 XWave
  33. 33. MYNDPLAY Electrodos:1 8 EEG bands 1 December 2011 MyndPlay
  34. 34. NEURAL IMPULSE ACTUATOR Electrodos:3 2 brainwaves (Alpha & Beta), facial muscle and eye movements May 2008 OCZ Technology Python
  35. 35. EMOTIV EPOC Electrodos:14 4 mental states (based on brainwaves), 13 conscious thoughts, facial expressions, head movements (sensed by 2 gyros) 21 December 2009 Emotiv Systems C#, C++, Java, Matlab, Python
  36. 36. INTENDIX Electrodos: 64 It can detect different brain signals with an accuracy of 99% March 2012 G.TEC
  37. 37. OPENEEG OpenEEG project is about making plans and software for do-ityourself EEG devices available for free (as in GPL)
  38. 38. OPENVIBE OpenViBE is a Open Source oftware platform dedicated to designing, testing and using brain-computer interfaces
  39. 39. TO LEARN MORE 1) http://www.coursera.org 2) http://www.gocognitive.net/
  40. 40. THANK YOU! Nikita Lukianets http://linkedin.com/in/nikiluk nikiluk@outlook.com

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