Motor Imagery BCI

This set of scenarios implements the Graz BCI, based on motor imagery of the hands. You will find it in share/openvibe-scenarios/bci/motor-imagery.

• motor-imagery-bci-0-signal-monitoring.xml: This scenario should be always used prior to anything and in background to check the signal quality of the acquisition device. Once you are sure that the EEG acquisition runs correctly, you can go on to the next step !

• motor-imagery-bci-1-acquisition.xml: First step is to acquire some data in order to train the classifier that will discriminate Right and Left hand movements. The training session can be configured in the LUA stimulator (number of trials, timings, etc.).

• motor-imagery-bci-2-classifier-trainer.xml: This scenario trains a LDA classifier based on the previous acquisition session. Note that the signal processing pipeline may be tuned accroding to the type of data acquired. For example, the Reference Channel may not be needed.

• motor-imagery-bci-3-online.xml: This scenario adds real-time feedback to the visualization, using the trained LDA classifier. Again, you may have to tune the signal processing pipeline.

• motor-imagery-bci-4-replay.xml: This scenario is based on the online one, but the input signal is coming from a file rather than acquisition server.

Fig.1 The motor imagery BCI.