Welcome to the ButIf project homepage. The current release is the version 1.0, which can be downloaded in the [Download] section.
About the latest updates, see the log file. BUTIFtoolbox is used to extract transient oscillatory dynamics from signals, in order to analyze local or large scale synchrony (using SES). See below for some litterature about these applications. Applications to other fields could be researched, as for instance in speech processing.
 

BUTIF toolbox is licensed (creative commons license), and distributed for scholar use only. 
ButIf toolbox 1.0 by Francois-B. Vialatte et al. is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 2.1 Japan License.
Publication of results obtained using this toolbox must cite relevant litterature:
Vialatte F.,  Sole-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, 10:46, 2009. [open access link]
Vialatte F.,  Sole-Casals J., Dauwels J., Maurice M., Cichocki A.,
Bump Time Frequency toolbox software, version 1.0, (2008).
available online at: http://www.bsp.brain.riken.jp/~fvialatte/bumptoolbox/download.html [Download]
Vialatte F., 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. [science direct link]

If you have questions or comments, if the problem is not already discussed in the [FAQ] section, you can use the [Discussion group].
You can also directly  [Contact us].

Applications

Until now, sparse time-frequency bump modelling  was  succesfully applied to model invasive EEG (local field potentials) event related potentials, to classify scalp EEG from patients with early stage of Alzheimer's disease (AD), to represent simultaneously time-frequency and space information using a sonification approach, to exploit  time-frequency space information using a synchrony model (Stochastic Event Synchrony, or SES), and to extract oscillations of steady state visual event potential epochs in single trials.

You can refer to the following litterature concerning these applications:
 

• Local Field Potentials

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.
link

Vialatte F.B., Martin C., Ravel N., Quenet B., Dreyfus G., Gervais R.
Oscillatory activity, behaviour and memory, new approaches for LFP signal analysis
35th annual general meeting of the European Brain and Behaviour Neuroscience Society, EBBS, Barcellona, Spain, september 17-20 2003
Acta Neurobiologiae Experimentalis 2003, Vol. 63.
pdf

• Scalp EEG (Alzheimer, SSVEP)

Vialatte F.B., Cichocki A., Dreyfus G., Musha T., Rutkowski T., Gervais R.
Blind source separation and sparse bump modelling of time frequency representation of EEG signals: New tools for early detection of Alzheimer's disease
IEEE Workshop on Machine Learning for Signal Processing 2005, Mystic CT, USA, September 28-30
Proc. IEEE MLSP 2005.
link
 

• Sonification

Rutkowski T.M., Vialatte F.B., Cichocki A., Mandic D.P., Barros A.K.
Auditory Feedback for Brain Computer Interface Management - An EEG Data Sonification Approach -.
10th International Conference on Knowledge-Based & Intelligent Information & Engineering Systems, KES 2006, Bournemouth, UK, October 9-11 2006
LNAI 4253:1232-1239.
link

• SES

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, 2009, 21(8):2152-2202
pdf

Dauwels J., Vialatte F., Weber T., Cichocki A.
Quantifying statistical interdependance by message passing on graphs-part II: Multi-Dimensional Point Processes
Neural Computation, 2009, 21(8):2203-2268.
pdf

• About bump modelling itself

Vialatte F.B., Dauwels J., Solé-Casals J., Maurice M., Cichocki A.
Improved Sparse Bump Modeling for Electrophysiological Data.
15th International Conference on Neural Information Processing, ICONIP, Auckland, New Zealand, November 25-28 2008
ICONIP 2008, LNCS, Part I, 5506:224-231, published in 2009.
pdf

Vialatte F.B., Maurice M., Cichocki A.
Why sparse bump models?
OHBM meeting, Melbourne, Australia, June 15-19 2008.
Neuroimage, 41(S1):S159.
pdf

Vialatte F., 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.
link