Graz visualization

Summary

Doc_BoxAlgorithm_GrazVisualization.png
  • Plugin name : Graz visualization
  • Version : 0.2
  • Author : Bruno Renier, Jussi T. Lindgren
  • Company : INRIA/IRISA
  • Short description : Visualization plugin for the Graz experiment
  • Documentation template generation date : Dec 30 2016

Description

Visualization/Feedback plugin for the Graz experiment

The Graz visualization box implements the display of the well-known Graz BCI paradigm, typically used for Motor Imagery BCI experiments. The paradigm is described e.g. in Pfurtscheller & Neuper, "Motor Imagery and Direct Brain-Computer Communication", 2001.

In its most common use, the Graz visualization instructs the user to perform either left or right hand motor imagery by presenting left and right arrows, correspondingly. In order to know when to present what, the box must be driven with a stimulation stream that provides the timeline of the experiment, including trial starts, trial stops, etc.

The same box can be used both for data collection and for real-time displays. When used real time, the box can display a directional blue bar to illustrate the strength and direction of the prediction.

Inputs

1. Stimulations

The timeline of the events.

  • Type identifier : Stimulations (0x6f752dd0, 0x082a321e)

2. Amplitude

For online use and feedback, the strength of the current activation. This can be for example the continuous output from a classifier.

  • Type identifier : Streamed matrix (0x544a003e, 0x6dcba5f6)

Settings

1. Show instruction

If true, the user will be shown the arrows.

  • Type identifier : Boolean (0x2cdb2f0b, 0x12f231ea)
  • Default value : [ true ]

2. Show feedback

If true, the current activation will be presented as feedback in form of a blue bar.

  • Type identifier : Boolean (0x2cdb2f0b, 0x12f231ea)
  • Default value : [ false ]

3. Delay feedback

If true, feedback will be shown only after the trial. Otherwise immediately.

  • Type identifier : Boolean (0x2cdb2f0b, 0x12f231ea)
  • Default value : [ false ]

4. Show accuracy

If true, a little matrix will display how many online trials matched the arrow direction.

  • Type identifier : Boolean (0x2cdb2f0b, 0x12f231ea)
  • Default value : [ false ]

5. Predictions to integrate

How many predictions to integrate for computing the feedback bar?

  • Type identifier : Integer (0x007deef9, 0x2f3e95c6)
  • Default value : [ 5 ]

6. Positive feedback only

If true, the blue bar will only be displayed if its direction agrees with the arrow.

  • Type identifier : Boolean (0x2cdb2f0b, 0x12f231ea)
  • Default value : [ false ]

Online visualisation settings

Examples

Miscellaneous

The timeline required by the box can be generated by a Lua stimulator. OpenViBE is bundled with a few motor imagery examples illustrating this (in folder "bci-examples/").

In order to place the markers (stimulations) to the recorded EEG stream accurately in time, the box connects to the Acquisition Server's TCP Tagging plugin and forwards the received timeline there after rendering. The subsequent scenarios and writers should then use the timeline from the Acquisition Server output and not directly from the timeline generating box. But due to the long duration of time the motor imagery is typically integrated, this paradigm could be less sensitive to marker alignment issues compared e.g. to P300.