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<h1>Publication information</h1> <p>These data are associated with the publication:</p> <p><strong>Adam, K.C.S., Chang, L., Rangan, N. & Serences, J.T.</strong> (2021) Steady-state visually evoked potentials and feature-based attention: Pre-registered null results and a focused review of methodological considerations. <em>Journal of Cognitive Neuroscience.</em></p> <p>A free pre-print version of the manuscript is available on the main project page or at this link: <a href="https://www.biorxiv.org/content/10.1101/2020.08.31.275602v3" rel="nofollow">https://www.biorxiv.org/content/10.1101/2020.08.31.275602v3</a></p> <p>The research plan for this publication was pre-registered prior to data collection. A time-stamped registration of the pre-registration is available at this link: <a href="https://osf.io/kfg9h/" rel="nofollow">https://osf.io/kfg9h/</a> </p> <p>These data are free for re-use for other researchers. If these data end up being useful to you (e.g., planning power analyses, conference presentation, new re-analysis publication), we would really appreciate hearing back from you! Contact: Kirsten Adam, kadam@ucsd.edu </p> <h1>Overview of components</h1> <p>Each component includes a read-me file (e.g., a data dictionary, helpful tips) that should be useful for navigating the structure of the data. </p> <p>Data were collected using a linux machine running Psychtoolbox, a windows machine running ActiView (the Biosemi recording software) and an eyelink machine. Data were analyzed in Matlab R2018A on an iMac (Mac OS 10.13.6). To run the provided code, you will need access to Matlab (Octave is a free alternative to matlab, but note that the code has note been checked for compatibility with it). </p> <h2>Data</h2> <ul> <li><code>Raw EEG data 1</code> and <code>Raw EEG data 2</code>: These components contain the raw EEG data in the .bdf format (Biosemi)</li> <li><code>Preprocessed EEG data 1</code> and <code>Preprocessed EEG data 2</code>: These components contain preprocessed EEG data in the .mat (Matlab) format. </li> <li><code>Raw eye-tracking data</code>: This component contains the raw eye-tracking data in the .edf format. (Note, you cannot change the file name of these particular files, as they have an 8 character limit!)</li> <li><code>Preprocessed eye-tracking data</code>: This component contains the eye-tracking data in a .mat (matlab) format. The unsegemented files are the .edf -&gt; .mat conversion of the raw data. The segmented files contain gaze data (x- and y- coordinates) that have been epoched into trials.</li> <li><code>Behavior data</code>: This component contains .mat files related to behavior (the HFP task and the main SSVEP task)</li> <li><code>Data documentation</code>: This contains read me 'data dictionary' files that describe the variablse found in the different data files. </li> </ul> <h2>Analysis</h2> <ul> <li><code>Plot Paper Figures</code>: This code will take some aggregate data files and plot all the main figures from the paper. This would be a good place to start if you are interested in using the data. </li> <li><code>Analysis Code & Statistics</code>: This is the code used for performing the analyses found in the paper. This code generally takes the single subject EEG files and generates multi-subject composite files for further plotting and analysis. If you just want to analyze or re-plot the data, you do no generally need to re-run these analyses and you can instead start off in the Plot Paper Figures component. <ul> <li><code>Main analyses</code>: The main analyses found in Figures 1-6.</li> <li><code>Appendix analyses</code>: The analyses found in Appendices A - O. </li> <li><code>JASP Statistics Files</code>: Table-format aggregate data (.csv) and ANOVA output tables for the ANOVA analyses found in the paper. </li> </ul> </li> <li><code>Preprocessing Pipeline</code>: The code needed to convert the raw data (.bdf files) into preprocessed data (<code>_EEG.mat</code>). Note, for many analyses, it is not necessary to redo the preprocessing, but we include it for completeness. We recommend that you c heck if you can accompish your analysis using the already pre-processed data. If you want to rerun preprocessing, you'll need to pay attention to notes on some quirks of individaul subjects (e.g., missing trials or split trials). </li> </ul> <h2>Other</h2> <ul> <li><code>Task Code</code>: This component contains the psychtooblox code used to present the stimuli to the participant. </li> </ul>
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