'''BEst - Brain Entropy in space and time'''

''This  toolbox implements the "maximum-entropy on the mean" (MEM) technique  for solving MEG, EEG and MEG/EEG source localizations. Along with the  classical MEM (Amblard et al. 2004), we exploit wavelet decompositions  of the data in order to target specifically oscillatory patterns (wMEM)  or synchronous cortical sources (rMEM). '''Explanation of cMEM, rMEM, wMEM in 2-3 sentences here.'''''

''BEst,  for "Brain Entropy in space and time", is the result of a collaborative  work between École de Technologie Supérieure and the department of  biomedical enginnering at'' [[http://www.bic.mni.mcgill.ca/ResearchLabsMFIL/HomePage|McGill]] ''university (Montreal, Canada). The present tutorial shows how to use MEM on MEG data from''

1) Simulations: 2 examples of realistic simulations of spikes

2) Inter-ictal epileptic discharges (IEDs): Average of 171 spikes

3) Median Nerve Stimulation (MNS) data: Average of 98 trials from Right hand responses

4) Sleep spindles: 5 spontaneous events

These datasets can be downloaded from [[http://dl.dropbox.com/u/6206322/TutorialBEst.zip|here]]. For each dataset the anatomy and gain matrices are provided. '''Below we will illustrate the application of the three MEM methods on MNS dataset (BEst_tutorial_median_nerve)'''.

{{http://neuroimage.usc.edu/brainstorm/Tutorials/TutBEst?action=AttachFile&do=get&target=ETS.png|ETS.png|height="107",width="150"}} {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutBEst?action=AttachFile&do=get&target=Mcgill_logo.png|Mcgill_logo.png|height="47",width="200"}}  {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutBEst?action=AttachFile&do=get&target=MFIL.png|MFIL.png|height="86",width="322"}}

See below for more info on the BEst license and the associated publications.

'''MEM: preliminary steps'''

We  need one gain matrix for each recording modality considered for source  localization. These matrices can be computed within Brainstorm using  either, spherical or realistic (recommended) head models. See the  tutorial on [[http://neuroimage.usc.edu/brainstorm/Tutorials/TutHeadModel|Head models]]  for details. Note that unlike the other methods proposed in Brainstorm,  the MEM does not require prior calculation of a noise covariance  matrix. This matrix can be estimated using a '''physiological baseline'''signal  that can either be part of the data to be localized or a different  piece of data. We can also use empty room noise recordings if available  to estimate this matrix.

 1. Uncompress downloaded dataset in a folder
 1. Import  the data in brainstorm by clicking:      File > Load protocol >  Load from folder > browse the protocol       (BEst_tutorial_median_nerve)
 1. Go to the data view mode (second button on      top of the database explorer)
 1. Right-click on the data average >      Compute sources > BEst. Hit 'Ok' '''Note:'''  The MEM option      only appears in expert mode. Make sure it is  toggled from the buttom at      the bottom left. It is recommended that  you customize the comment field so      as to avoid confusion when  performing multiple localizations for a given      dataset.
 {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutBEst?action=AttachFile&do=get&target=Compute_source.png|Compute_source.png|height="518",width="333"}} {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutBEst?action=AttachFile&do=get&target=Choose_source_localization_algorithm.png|Choose_source_localization_algorithm.png|height="514",width="302"}}
 1. Select the MEM method ('''cMEM''': time-series      representation, '''wMEM''': time-scale representation, or '''rMEM''':       wavelet representation). Define the data time window and the  baseline data      while keeping other parameters at default values.  Then hit 'Ok'.

''''' '''''

'''cMEM'''

 1. Selection  of a specific time window may be      useful for long data epochs  (default: localize whole data window).      Suggested time window for  this dataset: 0.010s-0.030s.
 1. Three choices are available for baseline      definition.
  * '''Default''':  the panel looks for a dataset called       'baseline' within the same  study as the data to be localized. When       not found, this option is  disabled.
  * '''Within data''': the baseline is a time window within the       recordings.
  * '''Import: '''the  user can import baseline data from a file       using brainstorm import  tools In all cases, the user can define a time       window for the  baseline
 1. Hitting 'Ok' will launch the cMEM  method.      At the end, a new results file appears in the database  under the      appropriate dataset. Simply double-click it to visualize.

'''Parameters of cMEM:'''

 * Clustering:  Dynamic (Link), Stable in time      (Link), MSP scores threshold (Link -  Arbitrary/FDR), neighborhood order      (Link), spatial smoothing  (Link).
 * Data definition: Time window (Link),      baseline (Link).
 * Expert: (Link to a page explaining the      expert parameters).

'''wMEM'''

''' '''

 1. Selection  of a specific time window may be useful to localize specific  time-frequency components of the recordings (default: localize whole  data window). Suggested time window for this dataset: 0.010s to 0.030s.
 1. Three choices are available for baseline definition.
 '''             Default''':  the panel looks for a dataset called 'baseline' within the same study  as the data to be
  . localized. When not found, this option is disabled.
  . '''Within data''': the baseline is a time window within the recordings.Suggested window: -1 to -0.15s
  '''Import: '''the  user can import baseline data from a file using brainstorm import tools  In all cases, the
   . user can define a time window for the baseline

 1. Selection of scales to be localized. Suggested scale = 4.
 1. ''''' '''''Hitting  'Ok' will launch the wMEM method. At the end, a new results file  appears in the database under the appropriate dataset. Simply  double-click it to visualize.

'''Parameters of wMEM:'''

 * Clustering:  Dynamic (Link), wavelet-adaptive      (Link), MSP scores threshold  (Link - FDR), neighborhood order (Link),      spatial smoothing (Link).
 * Data definition: Time window (Link),      baseline (Link).
 * Expert: (Link to a page explaining the      expert parameters).

The most innovative aspect of the BEst toolbox is the application of the entropic source localization framework (MEM) on wavelet coefficients. The aim of this approach is to localize neural sources responsible for a specific activity such as oscillations or synchrony. We consider two kinds of wavelet transforms, continuous (rMEM, Zerouali et al. 2011) and discrete (wMEM, Lina et al. 2012), which make explicit the synchronous and oscillatory components, respectively.

 * '''wMEM: '''The      data is decomposed following a discrete multiresolution scheme using real Daubeachies      wavelets. The time-scale plane can then be thresholded to keep only      significant coefficients, up to a fraction of total signal power. The MEM      then localizes the sources of each coefficient, thus yielding wavelet      coefficients for each source. As we keep only significant oscillatory      modes from the time-scale planes, this method efficiently targets the      oscillating neural sources. Interestingly, real Daubechies wavelets have a      stable inverse transform, which allows us to retrieve real time course for      each source.
 * '''rMEM:''' The      signal is decomposed into continuous time-frequency plane using analytic      Morse wavelets. We then extract multivariate ridges from these planes, and      extract the complex coefficients along the ridge lines. The sources of      these complex signals are then localized using the MEM. As the ridges      reflect synchronous signal components, the rMEM targets synchronous      sources. The following figure illustrates the results obtained by the MEM      solver and the wavelet-based MEM on Brainstorm tutorial data.

'''rMEM'''

 1. Selection of a specific time window may be      useful for long data epochs (default: localize whole data window).      Suggested time window for this dataset: 0s to 0.s.
 1. Three choices are available for baseline      definition.
  * '''Default''': the panel looks for a dataset called       'baseline' within the same study as the data to be localized. When       not found, this option is disabled.
  * '''Within data''': the baseline is a time window within the       recordings.
  * '''Import: '''the user can import baseline data from a file       using brainstorm import tools In all cases, the user can define a time       window for the baseline
 1. Specify the frequency band for ridge      computation. Suggested values for this dataset: 12-30Hz (Beta)
 1. Definition of the minimum duration of the      synchrony. Suggested value for this dataset: 20ms
 1. Hitting 'Ok' will launch the rMEM method. At the end, a new results file appears in the database under the appropriate dataset. Simply double-click it to visualize.

'''Parameters of rMEM:'''

 * Clustering: Dynamic (Link),      wavelet-adaptive (Link), MSP scores threshold (Link - Arbitrary/FDR),      neighborhood order (Link), spatial smoothing (Link).
 * Data definition: Time window (Link),      baseline (Link).
 * Expert: (Link to a page explaining the expert parameters).

'''Results from the other datasets'''

 . ·       '''cMEM'''o   Simulationso   Epileptic spikes      ·       '''wMEM'''o   Simulationso   Sleep spindles''' '''

·      '''rMEM''' '''                  o  Simulations'''

'''License'''

BEst is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. BEst is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should receive a copy of the GNU General Public License along with BEst. If not, get it [[http://gnu.org/licenses|here]].

The references to be acknowledged are:

 * '''MEM with stable clustering'''. Chowdhury R, Lina JM, Kobayashi E, Grova C. MEG      source localization of spatially extended generators of epileptic      activity: cpmaring entropic and hierarchical bayesian approaches. PLoS      One, 2012 (in revision)
 * '''wMEM. '''Lina      JM, Chowdhury R, Lemay E, Kobayashi E, Grova C. Wavelet-based localization      of oscillatory sources from magnetoencephalography data. IEEE      Transactions on biomedical engineering, 2012 (Ahead of print)
 * '''rMEM.''' Zerouali      Y, Herry C, Jemel B, Grova C. Localization of synchronous cortical neural      sources. IEEE      Transactions on biomedical engineering, 2011 (Ahead of print)

Additionnal references:

 * '''MSP.''' Mattout      J, Pelegrini-Issac M, Granero L, Benali H. Multivariate source      prelocalization (MSP): use of functionnaly informed basis functions for      better conditioning the MEG inverse problem. [[http://neuroimage.usc.edu/brainstorm/NeuroImage|NeuroImage]] 26:      356:373.
 * '''MEM.''' Amblard      C, Lapalme E, Lina JM. Biomagnetic Sources Detection by Maximum Entropy on      the Mean with Graphical Models, IEEE Transactions on Biomedical      Engineering, 55(3): 427-442
 * '''minFunc. '''Schmidt M. see [[http://www.di.ens.fr/~mschmidt/Software/copyright.html|license]]