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First progress meeting
Brain Control Club
4-5-2017
CRI
http://cri-paris.org/scientific-clubs/brain-control-club/
Organization & Members
• Group leader: Ignacio Rebollo (CRI)
• Communication: Hernan Anllo (CRI)
• Finance: Claire Baudelet (CRI)
• Mentors: Dr. Stephen Whitmarsh (ENS)
Dr. Guillaume Dumas (Pasteur)
Prof. Robert Oostenveld (DCCN, KI)
• Approx. 12 CRI students working on 4/5 projects
Brain Control Club
Goal
• Understanding theory and applications of BCI
• Through hands-on creation of BCI’s
Approach
• Group projects on a specific BCI application
• Sharing knowledge within and between projects
Support
• Autodidactic
• Educational tools and material
• Necessary hardware and software
• Hackathons
• Mentorship
• Invited experts
Brain Control Club
Goal
• Understanding theory and applications of BCI
• Through hands-on creation of BCI’s
Approach
• Group projects on a specific BCI application
• Sharing knowledge within and between projects
Support
• Autodidactic
• Educational tools and material
• Necessary hardware and software
• Hackathons
• Mentorship
• Invited experts
Goal
• Status of each project
• Accomplishments so far
• Share knowledge between projects
• Identify obstacles
Projects
• Nirvana
• Morpheus
• EEG-game
• EEGsynth.org
• EEG-composition (NEW)
Progress meeting
Project Nirvana
Ignacio
Claire
Elizabeth
Hernan
(Raly)
Background
• Bodily signals (heart, stomach, respiration, brain) interact with
each other during different mental/bodily states
• Brain alpha (10Hz) power fluctuations are associated with
vigilance fluctuations, together with changes in respiration and
heart rate
• Understanding their coupling in real time could allow us to
detect certain mental states
• Although these processes are normally unconscious, explicit
knowledge of them (e.g. sounds), allow us to exert some
control over them, a technique known as biofeedback
Objectives
1. Develop tools for online tracking of mental states such as
vigilance, concentration or stress
2. Use biofeedback to help users attain or maintain a desired
mental state
Progress so far
1. Software
• MATLAB for processing raw incoming data
• EEGsynth.org modules
• Openbci2ft (monopolar, bipolar),
• EEG playback
2. EEG instrumentation and hardware
• EEG recording
• OpenBCI architecture
• EEG recording by Raspberry Pi
1. Testcase
• Play the sound of the power of alpha based on pre-
recorded dataset
Next steps
1. Acquire multimodal 20 minutes dataset (EGG, ECG, EEG) for playback
2. Update Openbci2ft.c to allow recording from 8 to 16 channels
3. Online detection of eye blinks and saccades
4. EGG module (online detection of 0.05Hz waveform)
5. Heartbeat module
6. Physiological connectivity modules (e.g. Heart – Brain coupling)
Obstacles so far
Signal quality and preprocessing
• Amplifier saturates sometimes
• OpenBCI GUI shows good data, but data received in
MATLAB seems very noisy
• Optimal acquisition parameters for the EEGsynth?
• Optimal filter settings (using MATLAB)?
Project Morpheus
Background
Targeted memory reactivation (TMR)
Background
What happens in stage 2?
Hypothesis
Hypothesis
Hypothesis
TMR
effective
Hypothesis
TMR
effective
TMR
non-effective
• do task with many associated sounds
• separate sounds into 4 categories and play them during sleep:
• sounds after deltas
• sounds during spindles
• no sounds
• non-specific (random) sounds
• test performance on task
Hypothesis
Plan
Task design
• obtain object-sound pairs
• code task with GUI
EEG analysis
• get signal in real time
• make sounds respond to detected events
• record sleep data
• make online detection work using pre-recorded
data (replayed)
• record exact timing of sounds to future analysis
• test sleep session: 4 sounds only (no task) –
analyse and debug
Experiment
• recruit ~5-10 subjects
• test
• analyse data :))
Plan
Task design
• obtain object-sound pairs
• code task with GUI
EEG analysis
• get signal in real time
• make sounds respond to detected events
• record sleep data
• make online detection work using pre-recorded
data (replayed)
• record exact timing of sounds to future analysis
• test sleep session: 4 sounds only (no task) –
analyse and debug
Experiment
• recruit ~5-10 subjects
• test
• analyse data :))
Brain-based music
composition
Liburn Jupolli & Stephen Whitmarsh
Accent marks in music composition
staccato
staccatissimo
marcato
accent
tenuto
McFadden KL, Steinmetz SE, Carroll AM, Simon ST, Wallace A, et al. (2014)
Test-Retest Reliability of the 40 Hz EEG Auditory Steady-State Response. PLOS ONE
B. Ross et al. J (2005) Stimulus Induced Desynchronization of Human
Auditory 40-Hz Steady-State Responses. Neurophysiol.
Kaongoen, Jo (2017) The effect of selective attention on multiple ASSRs for future BCI
application. 5th International Winter Conference on Brain-Computer Interface (BCI)
Auditory Steady-State response
Treble (guitar)
Bass (guitar)
EEGsynth
45Hz AM
35Hz AM
Amplitude
Amplitude
35Hz EEG
45Hz EEG
Schematic design
Treble (guitar)
Bass (guitar)
EEGsynth
45Hz AM
35Hz AM
Amplitude
Amplitude
35Hz EEG
45Hz EEG
attention
Schematic design
Treble (guitar)
Bass (guitar)
EEGsynth
45Hz AM
35Hz AM
Amplitude
Amplitude
35Hz EEG
45Hz EEG
attention
Schematic design
Plan:
• Use outputCVgate device to create 0-10v modulation at 35/45 Hz.
 Use VCA to create amplitude modulation
• Analyze relative difference in EEG amplitude at steady-state freq.
 Modulate VCA amplitude with outputCVgate device
So far:
• Creating outputCVgate device
EEG-synth project
Per Huttner
Jean-Louis Huhta
Robert Oostenveld
Stephen Whitmarsh
and many more…
Brain Control Club progress meeting Project: Introduction and Projects
Some highlights in recent progress
• Thoroughly tested EEGsynth installation instructions for Windows,
OS-X, Linux (Raspian, Mint, Redhat)
• Pipeline for creating EEG music composition scores
• Plug-and-play(back) of EEGsynth on Raspberry Pi
• Live light control by EEGsynth on (Neopixel and DMX)
Brain Control Club progress meeting Project: Introduction and Projects
Offline playback of control signals based on EEG
Composition &
MIDI score
based on EEG
using LilyPad
Plug-and-play
EEG-synth
WiFi network
and remote
access
EEG-controlled
light in visual
art installation
March 16th – April 16th
EEG-controlled
light in visual
art installation
March 16th – April 16th
Portable EEGsynth costume
by Emilia Rota, Stockholm
http://cri-paris.org/scientific-clubs/brain-control-club/

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Brain Control Club progress meeting Project: Introduction and Projects

  • 1. First progress meeting Brain Control Club 4-5-2017 CRI http://cri-paris.org/scientific-clubs/brain-control-club/
  • 2. Organization & Members • Group leader: Ignacio Rebollo (CRI) • Communication: Hernan Anllo (CRI) • Finance: Claire Baudelet (CRI) • Mentors: Dr. Stephen Whitmarsh (ENS) Dr. Guillaume Dumas (Pasteur) Prof. Robert Oostenveld (DCCN, KI) • Approx. 12 CRI students working on 4/5 projects
  • 3. Brain Control Club Goal • Understanding theory and applications of BCI • Through hands-on creation of BCI’s Approach • Group projects on a specific BCI application • Sharing knowledge within and between projects Support • Autodidactic • Educational tools and material • Necessary hardware and software • Hackathons • Mentorship • Invited experts
  • 4. Brain Control Club Goal • Understanding theory and applications of BCI • Through hands-on creation of BCI’s Approach • Group projects on a specific BCI application • Sharing knowledge within and between projects Support • Autodidactic • Educational tools and material • Necessary hardware and software • Hackathons • Mentorship • Invited experts Goal • Status of each project • Accomplishments so far • Share knowledge between projects • Identify obstacles Projects • Nirvana • Morpheus • EEG-game • EEGsynth.org • EEG-composition (NEW) Progress meeting
  • 6. Background • Bodily signals (heart, stomach, respiration, brain) interact with each other during different mental/bodily states • Brain alpha (10Hz) power fluctuations are associated with vigilance fluctuations, together with changes in respiration and heart rate • Understanding their coupling in real time could allow us to detect certain mental states • Although these processes are normally unconscious, explicit knowledge of them (e.g. sounds), allow us to exert some control over them, a technique known as biofeedback
  • 7. Objectives 1. Develop tools for online tracking of mental states such as vigilance, concentration or stress 2. Use biofeedback to help users attain or maintain a desired mental state
  • 8. Progress so far 1. Software • MATLAB for processing raw incoming data • EEGsynth.org modules • Openbci2ft (monopolar, bipolar), • EEG playback 2. EEG instrumentation and hardware • EEG recording • OpenBCI architecture • EEG recording by Raspberry Pi 1. Testcase • Play the sound of the power of alpha based on pre- recorded dataset
  • 9. Next steps 1. Acquire multimodal 20 minutes dataset (EGG, ECG, EEG) for playback 2. Update Openbci2ft.c to allow recording from 8 to 16 channels 3. Online detection of eye blinks and saccades 4. EGG module (online detection of 0.05Hz waveform) 5. Heartbeat module 6. Physiological connectivity modules (e.g. Heart – Brain coupling)
  • 10. Obstacles so far Signal quality and preprocessing • Amplifier saturates sometimes • OpenBCI GUI shows good data, but data received in MATLAB seems very noisy • Optimal acquisition parameters for the EEGsynth? • Optimal filter settings (using MATLAB)?
  • 13. Targeted memory reactivation (TMR) Background
  • 14. What happens in stage 2?
  • 19. • do task with many associated sounds • separate sounds into 4 categories and play them during sleep: • sounds after deltas • sounds during spindles • no sounds • non-specific (random) sounds • test performance on task Hypothesis
  • 20. Plan Task design • obtain object-sound pairs • code task with GUI EEG analysis • get signal in real time • make sounds respond to detected events • record sleep data • make online detection work using pre-recorded data (replayed) • record exact timing of sounds to future analysis • test sleep session: 4 sounds only (no task) – analyse and debug Experiment • recruit ~5-10 subjects • test • analyse data :))
  • 21. Plan Task design • obtain object-sound pairs • code task with GUI EEG analysis • get signal in real time • make sounds respond to detected events • record sleep data • make online detection work using pre-recorded data (replayed) • record exact timing of sounds to future analysis • test sleep session: 4 sounds only (no task) – analyse and debug Experiment • recruit ~5-10 subjects • test • analyse data :))
  • 23. Accent marks in music composition staccato staccatissimo marcato accent tenuto
  • 24. McFadden KL, Steinmetz SE, Carroll AM, Simon ST, Wallace A, et al. (2014) Test-Retest Reliability of the 40 Hz EEG Auditory Steady-State Response. PLOS ONE B. Ross et al. J (2005) Stimulus Induced Desynchronization of Human Auditory 40-Hz Steady-State Responses. Neurophysiol. Kaongoen, Jo (2017) The effect of selective attention on multiple ASSRs for future BCI application. 5th International Winter Conference on Brain-Computer Interface (BCI) Auditory Steady-State response
  • 25. Treble (guitar) Bass (guitar) EEGsynth 45Hz AM 35Hz AM Amplitude Amplitude 35Hz EEG 45Hz EEG Schematic design
  • 26. Treble (guitar) Bass (guitar) EEGsynth 45Hz AM 35Hz AM Amplitude Amplitude 35Hz EEG 45Hz EEG attention Schematic design
  • 27. Treble (guitar) Bass (guitar) EEGsynth 45Hz AM 35Hz AM Amplitude Amplitude 35Hz EEG 45Hz EEG attention Schematic design
  • 28. Plan: • Use outputCVgate device to create 0-10v modulation at 35/45 Hz.  Use VCA to create amplitude modulation • Analyze relative difference in EEG amplitude at steady-state freq.  Modulate VCA amplitude with outputCVgate device So far: • Creating outputCVgate device
  • 29. EEG-synth project Per Huttner Jean-Louis Huhta Robert Oostenveld Stephen Whitmarsh and many more…
  • 31. Some highlights in recent progress • Thoroughly tested EEGsynth installation instructions for Windows, OS-X, Linux (Raspian, Mint, Redhat) • Pipeline for creating EEG music composition scores • Plug-and-play(back) of EEGsynth on Raspberry Pi • Live light control by EEGsynth on (Neopixel and DMX)
  • 33. Offline playback of control signals based on EEG
  • 34. Composition & MIDI score based on EEG using LilyPad
  • 36. EEG-controlled light in visual art installation March 16th – April 16th
  • 37. EEG-controlled light in visual art installation March 16th – April 16th
  • 38. Portable EEGsynth costume by Emilia Rota, Stockholm