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* <font color=red> [8] </font> '''Penter:''' Used to specify the maximum [http://en.wikipedia.org/wiki/P-value p-value] for  a variable to be entered. The default is 0.1000. Penter must be less than Premove and 0<Penter<1.
* <font color=red> [8] </font> '''Penter:''' Used to specify the maximum [http://en.wikipedia.org/wiki/P-value p-value] for  a variable to be entered. The default is 0.1000. Penter must be less than Premove and 0<Penter<1.
* <font color=red> [9] </font> '''Premove:''' Used to specify the maximum  [http://en.wikipedia.org/wiki/P-value p-value] for a variable to be removed. The default is 0.1500. Premove must be greater than Penter and 0<Premove<1.





Revision as of 20:05, 17 August 2009

Synopsis

The P300 Classifier GUI (Graphical User Interface) is a tool that allows to train and test a linear classifier for detection of evoked related potentials collected with BCI2000. This GUI is designed for the analysis of BCI2000 data collected using the P3Speller or P3AV paradigms. The program generates feature weights derived via the Stepwise Linear Regression technique. The specifics of the feature space and training routine can be manipulated using the GUI. The feature weights derived from the GUI can be saved and imported into BCI2000 as a parameter file fragment for online testing. The GUI provides the following functionality to investigators:

Classifier Training
Generates feature weights from BCI2000 P3Speller or P3AV data files
Classifier Testing
Applies current feature weights to BCI2000 P3Speller or P3AV data files and compares the results

Location

http://www.bci2000.org/svn/trunk/src/private/Tools/P300_classifier

Versioning

Author

Cristhian Potes

Source Code Revisions

  • Initial development: --
  • Tested under: --
  • Known to compile under: --
  • Broken since: --

Control Panel

The P300 Classifier GUI is composed of three panels: Data, Parameters, and Details.

Data Pane

Data Pane allows the user to:

  • Load training and testing data files and an INI file.
  • Generate and apply feature weights
  • Write a parameter file fragment
Data pane


  • [1] Load Training Data Files: Use this button to load BCI2000 data files for training. The information for the selected files will appear at the top of the button.
  • [2] Load Testing Data Files: Use this button to load BCI2000 data files for testing. The information for the selected files will appear at the top of the button. Training and testing data files must be compatible.
  • [3] Load Ini File: Use this button to load an INI file with all the parameters needed for the feature extraction.
  • [4] Generate Feature Weights: Use this button to generate the feature weights after properly configuring all of the parameters in the Parameters pane. The suggested name for the parameter file fragment (*.prm) will appear at the top of the Write *.prm File button. This button will be enable only if the parameters are properly configured and there exists training data files.
  • [5] Apply Feature Weights: Use this button to test the classification accuracy of the parameter file fragment currently stored in the GUI. The classification results will appear in the Details pane.
  • [6] Write *.prm File: Use this button to save the parameters file fragment with the name suggested at the top of this button. The *.prm file is a BCI2000 parameter file fragment that can be imported into BCI2000 for online testing of the feature weights.


Parameters Pane

Parameters Pane contains all the parameters needed to generate the feature weights. These parameters can be loaded using the Load Ini File button. If the parameters are properly configured the Generate Feature Weights button is enabled.

Parameters pane
  • [7] Max Model Features: Used to specify the maximum number of features to be kept in the SWLDA (Stepwise Linear Discriminant Analysis model). Only a single value can be entered for evaluation. The defaults is 60.
  • [8] Penter: Used to specify the maximum p-value for a variable to be entered. The default is 0.1000. Penter must be less than Premove and 0<Penter<1.
  • [9] Premove: Used to specify the maximum p-value for a variable to be removed. The default is 0.1500. Premove must be greater than Penter and 0<Premove<1.



Methodology

Methodology Concept

Methodology Outline

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See also