Francois-B. Vialatte
  • Wako-Shi, Saitama-Ken (near Tokyo)
  • Japan
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Francois-B. Vialatte's Colleagues

  • Juan F Gomez-Molina
  • Manuel Gamez
  • John P. Boyce III
  • Alexandra Elbakyan
  • Philippe Valdois
 

Francois-B. Vialatte's Page

Profile Information

Main areas of research:
Brain Signal Processing, Neuroscience, Alzheimer's disease and brain disorders, Evoked Potentials, Brain Computer Interface
Interests/keywords:
EEG, fMRI, wavelets, time-frequency, bump modeling, MCI, Alzheimer, BCI, meditation, rehabilitation, cognition, brain, neural sychrony, neurophenomenology
Current title/position:
Ph.D. / Research Scientist
Current affiliation/employer:
Riken Brain Science Institute
Collaborators:
Andrzej Cichocki (Riken BSI, Japan)
Justin Dauwels (MIT, USA)
Monique Maurice (Riken BSI, Japan)
Jordi Sole-Casals (University of Vic, Spain)
Charles Latchoumane (KAIST, South Korea)
Corinna Haenschel (University of Bangor, UK)
Johannes Pantel (JW Goethe University, Germany)
Maria Kniazeva (CHUV, Switzerland)
PhD Advisor and University:
G. Dreyfus (ESPCI, Paris), R. Gervais (CNRS, Lyon) - France
Postdoc Advisor and University:
A. Cichocki, Riken BSI (Japan)
Personal or laboratory homepage:
http://www.bsp.brain.riken.jp/~fvialatte/
Publications:
Dauwels J., Vialatte F.B., Cichocki A.
A comparative study of synchrony measures for the early diagnosis of Alzheimer's disease based on EEG. Neuroimage, in press.

Vialatte FB, Dauwels J, Maurice M, Yamaguchi Y, Cichocki A. On the synchrony of steady state visual evoked potentials and oscillatory burst events.
Cognitive Neurodynamics, in press.

Dauwels J., Vialatte F., Weber T., Cichocki A.
Quantifying statistical interdependance by message passing on graphs, PART I: algorithms and applications to neural signals.
Neural Computation, in press.

Dauwels J., Vialatte F., Weber T., Cichocki A.
Quantifying statistical interdependance by message passing on graphs, PART II: Multi-Dimensional Point Processes. Neural Computation, in press.

Vialatte F.B., Solé-Casals J., Dauwels J., Maurice M., Cichocki A. Bump Time-Frequency Toolbox: a Toolbox for Time-Frequency Oscillatory Bursts Extraction in Electrophysiological Signals
BMC Neuroscience, 2009, 10:46.

Vialatte F.B., Bakardjian H., Prasad R., Cichocki, A.
EEG paroxysmal gamma waves during Bhramari Pranayama: a yoga breathing technique
Consciousness and Cognition, in press.
doi:10.1016/j.concog.2008.01.004
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Vialatte F.B., Solé-Casals J., Cichocki A.
EEG windowed statistical wavelet scoring for evaluation and discrimination of muscular artifacts
Physiological Measurement, 2008, 29(12):1435-52.

Vialatte F.B., Cichocki, A.
Split-Test Bonferroni correction for QEEG Statistical Maps. Biological Cybernetics, 2008, 98(4):295-303.

Chen Z., Ohara S., Cao J., Vialatte F.B., Lenz F.A., Cichocki A. Statistical modeling and analysis of laser-evoked potentials of electrocorticogram recordings from awake humans
Computational Intelligence and Neuroscience, vol. 2007, Article ID 10479, 2007.
doi:10.1155/2007/10479.

Woon W.L., Cichocki A., Vialatte F.B., Musha T.
Techniques for early detection of Alzheimer's disease using spontaneous EEG recordings.
Physiological Measurement 2007, 28(4):335-347.

Vialatte F.B., Martin C., Dubois R., Quenet B., Gervais R., Dreyfus G. A machine learning approach to the analysis of time-frequency maps, and its application to neural dynamics
Neural Networks 2007, 20:194-209.

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At 11:28pm on September 29, 2009, zulqarnain zahid said…
hi
I m a student of electrical engineering in air university pakistan
i m working on bci as my final year project
i m working on wheel chair control using bci
i will be needing ur help
At 1:58am on September 28, 2009, zulqarnain zahid said…
Assalamo Alaikum
i m a student of electrical engineering in air university pakistan
i m working on bci as my final year project
i m working on wheel chair control using bci
i will be needing ur help
At 11:38am on August 26, 2009, Manuel Gamez said…
What´s up Francois!
More than a piece of code, it´s a simulation of an EEG adquisition device performed by simulink blocks (with some parameters of course), without noise, and have de advantage that you can add your pattern recognition code, and run a full simulation of it. The only problem is that you will need a very powerfull computer, because simulink is a very heavy tool for computer resources. You can run a BCI with patient connected almost at real time, only if your code is not too long. For example, I can work with 2 channels with 200 Hz sampling speed, and run patter recogniton arlgorithm (with 4th. level operations) and run my simulation of pointer movement in a intel pentium dual core at 1.6 Ghz, 1Mb caché, 1GB of ram, and leaving windows only with the essential services. Tou will need a little circuit, an audio plug (that you use to connect your headset to the computer audio input), and a Matlab 2007a release or superior. Please write me to see how we can share this info and data. Thans a lot and good day!!!
 
 
 

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