In this tutorial we will make use of the BCI2000 Offline Analysis "Load Settings" feature that allows us to load previously saved settings. In User Tutorial:Performing an Offline Analysis of EEG Data we manually entered all the analysis parameters.
#BCI 2000 WIKI OFFLINE ANALYSIS HOW TO#
For instruction on how to inspect data using the BCI2000 Viewer, please see User Reference:BCI2000Viewer. Using this tool, you will be able to see how state variables change with respect to the data over time. If you are relatively new to BCI2000, you may find it helpful to inspect the data files we'll be using with the BCI2000 Viewer. When the symbol that flashes is not the symbol of focus, StimulusType will not be equal to 1. In the case of the ecog2 data, we used the state variable StimulusType such that StimulusType is equal to 1 when the letter that flashes is the letter that the subject is focused on (i.e., the letter that is currently highlighted). Such labels can be attached to BCI2000 data using state variables. This way, it was possible to label evoked responses according to whether they occurred for the highlighted symbol or for some other symbol in the matrix. In each run of the experiment, the subject was asked to focus on the symbol in the speller matrix that was currently highlighted in the test sequence. A test sequence, consisting of a string of letters with a single letter highlighted, was also displayed. In the ecog2 session the subject was placed in front of a monitor displaying a P300 speller matrix.
#BCI 2000 WIKI OFFLINE ANALYSIS DOWNLOAD#
Please download this file and extract the contents to data/samplefiles/.
This tutorial will make use of the ecog2_1.dat session that is included as part of the supplementary sample files downloadable here. As in User Tutorial:Performing a Time-Domain Offline Analysis of EEG Data, we will use BCI2000 Offline Analysis to help determine the P300 parameters for a given subject. As in the case of EEG data, we expect the basic properties of the P300 evoked potential to be the same for all individuals while the response's latency, width, and spatial pattern may vary. In this section of the tutorial, we perform a time-domain offline analysis of a dataset recorded using an ECoG.
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