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Project 3 Biologically-driven Musical Instrument
Quentin Noirhomme - Jean-Julien
Filatriau Communication and Remote Sensing
Lab. Université catholique de Louvain
July 18th 2005
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Objectives
Analysis
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EEG Origin
Brain Facts
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EEG Recording
DTI cap
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EEG
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EEG

• Noise
• Background activities
• Activities with partial control
• used for Brain Computer Interface
• Training possible

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EEG Rhythms

• Alpha
• 8-13 Hz
• first discovered
• first used for music

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EEG Rhythms

• Alpha
• Delta
• 0.1-3.5 Hz
• Theta
• 4-7.5 Hz

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EEG Rhythms

• Alpha
• Delta
• Theta
• Mu
• 8-12 Hz
• Beta
• 14-30 Hz

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Mu and Beta rhythms
Wolpaw et al., 2002
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Event Related Potentials
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Topography
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ElectroCardioGram
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ElectroMyoGram
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Objectives
Mapping
Analysis
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EEG Analysis

• Frequency Analysis
• Fourier, auto-regressive modelling
• Wavelets
• Event detection
• Localization
• Hjort Analysis

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Objectives
Mapping
Analysis
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Objectives
Mapping
Analysis
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Sound synthesis

• Sound synthesis
• creation of a sound signal from specific
  algorithms implemented on a computer.
• synthesis parameters that influence sound
  characteristics (pitch, spectral content,
  timbre...)
• ex additive synthesis, soustractive
  synthesis, FM synthesis, waveshaping synthesis...

• Ex Frequency modulation synthesis
• y(t) A sin ?C t I sin(?M t )

Synthesis parameters
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Digital music instruments

• Real-time sound synthesis for  live
  interpretation - Sound synthesis parameters
  controlled by players gestures gt
  interaction allows musician to act on the sound
  like in a traditional musical instrument gt
  Mapping correspondance functions between
  gestures and synthesis parameters.

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Examples of digital music instruments (from LMA,
Marseille)
The photosonic emulator (training)
The Voicer (in live situation)
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Biologically-driven musical instrument

• Objective of our project using infos extracted
  from EEG/EMG analysis as sound synthesis
  parameters to drive a digital music
  instrument.- Two main issues gt synthesis
  algorithm(s) gt adequate  mapping  between
  EEG/EMG data and sound synthesis parameters.-
  Previous works in interaction biology/music
  gt Brouse (2001) Interharmonium (EEG) gt
  Tanaka (2002) BioMuse (EMG) gt Miranda
  Brouse (2005) BCI-Piano (EEG)

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References

• Papers
•  Braincomputer interfaces for communication and
  control , Wolpaw al., 2002
•  Toward direct Brain-Computer musical
  interface , Miranda Brouse, 2005
•  Musical Performance Practice on Sensor-based
  Instruments , Tanaka, 2002
• Web sites
• http//www.tele.ucl.ac.be/noirhom/eNTERFACE3/inde
  x.html
• http//cmr.soc.plymouth.ac.uk/interaction.htm
• http//www.music.mcgill.ca/brouse/conversation/

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Team

• Benoît MACQ - professor (UCL)
• Jean-Julien FILATRIAU - PhD student (UCL)
• Quentin NOIRHOMME - PhD student (UCL)
• Burak ARSLAN - invited professor (TCTS, Mons)
• Rolando BONAL CACERES - professor (La Havane,
  Cuba)
• Andrew BROUSE - PhD student (University of
  Plymouth)
• Julien CASTET - future PhD student (ACROE,
  Grenoble)
• Rémy LEHEMBRE - future PhD student (UCL)
• Cédric SIMON - future PhD student (UCL)

July 18th 2005
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Thanks for your attention !