Data from P300-based Neurofeedback Training for Attention Enhancement with 4 EEG Electrodes

<p>The dataset contains EEG and behavioral data of 10 participants who completed 11 runs (i.e. copy-spelled 11 words) in a P300 speller task, as well as a random dot motion (RDM) task and questionnaires in a single experimental session. The data comes from a study that uses a modified version of the protocol described here:</p> <p>Noble SC,&nbsp;Woods E,&nbsp;Ward T,&nbsp;Ringwood JV. &ldquo;Adaptive P300-Based Brain-Computer Interface for Attention Training: Protocol for a Randomized Controlled Trial.&rdquo;&nbsp;<em>JMIR Res Protoc</em>&nbsp;2023, 12:e46135, doi:&nbsp;<a href="https://doi.org/10.2196/46135">10.2196/46135</a></p> <p>These are the differences to the protocol above:</p> <ul> <li>More but shorter words in the P300 speller (see below)</li> <li>Only 4 electrodes were used (Pz, POz, P7 and P8)</li> <li>Task difficulty adaptation is by iterative learning control (ILC) only</li> </ul> <p>Each participant folder contains:</p> <ul> <li>[xxx]-raw.[xxx] &ndash; unprocessed EEG signals from 4 electrodes for all 11 P300 speller runs in Openvibe (.ov) and Matlab (.mat) file formats, see details of the runs below</li> <li>classifier.cfg - LDA classifier weights</li> <li>log.txt - contains start and end time of the experiment, and performance in the P300 speller and RDM tasks</li> </ul> <p>The file &ldquo;Questionnaire scores.csv&rdquo; contains the responses to the questionnaire described in the experimental protocol and the NASA Task Load Index (TLX) for all participants.</p> <p>The .ov and .mat files contain data from the following runs:</p> <table> <tbody> <tr> <th>Filename</th> <th>Word to be copy-spelled</th> <th>Number of flashes per row and column</th> <th>Feedback given to participant</th> </tr> <tr> <td>calibration-signal1</td> <td>THE</td> <td>12</td> <td>no</td> </tr> <tr> <td>calibration-signal2</td> <td>QUICK</td> <td>12</td> <td>no</td> </tr> <tr> <td>calibration-signals</td> <td>Concatenation of calibration-signal1 and calibration-signal2</td> <td>&nbsp;</td> <td>&nbsp;</td> </tr> <tr> <td>eval</td> <td>DOG</td> <td>12</td> <td>yes</td> </tr> <tr> <td>training-run-1</td> <td>WIZARD</td> <td>10</td> <td>yes</td> </tr> <tr> <td>training-run-2</td> <td>HUMBLE</td> <td>varying</td> <td>yes</td> </tr> <tr> <td>training-run-3</td> <td>JOKERS</td> <td>varying</td> <td>yes</td> </tr> <tr> <td>training-run-4</td> <td>UNLOCK</td> <td>varying</td> <td>yes</td> </tr> <tr> <td>training-run-5</td> <td>THRIVE</td> <td>varying</td> <td>yes</td> </tr> <tr> <td>training-run-6</td> <td>JUNGLE</td> <td>varying</td> <td>yes</td> </tr> <tr> <td>training-run-7</td> <td>SHADOW</td> <td>varying</td> <td>yes</td> </tr> <tr> <td>training-run-8</td> <td>FROZEN</td> <td>varying</td> <td>yes</td> </tr> </tbody> </table> <p>&nbsp;</p> <p>This research is supported by the Irish Research Council under project ID GOIPG/2020/692 and Science Foundation Ireland under grant number 12/RC/2289_P2.</p>