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Research/EEG-GAN v1/index.html

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-    Williams, Weinhardt, Wirzberger, &amp; Musslick (in press), Cognitive Science
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-    Williams, Weinhardt, Wirzberger, &amp; Musslick (in press), Cognitive Science
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 <h1 id="research">Research</h1>
 <h2 id="augmenting-eeg-with-generative-adversarial-networks-enhances-brain-decoding-across-classifiers-and-sample-sizes"><b>Augmenting EEG with Generative Adversarial Networks Enhances Brain Decoding Across Classifiers and Sample Sizes</b></h2>
-<h3 id="williams-weinhardt-wirzberger-musslick-in-press-cognitive-science"><i>Williams, Weinhardt, Wirzberger, &amp; Musslick (in press), Cognitive Science</i></h3>
+<h3 id="williams-weinhardt-wirzberger-musslick-in-press-proceedings-of-the-annual-meeting-of-the-cognitive-science-society"><i>Williams, Weinhardt, Wirzberger, &amp; Musslick (in press), Proceedings of the Annual Meeting of the Cognitive Science Society</i></h3>
 <p><center> <a class="md-button" href="https://osf.io">Preprint</a> <a class="md-button" href="https://cognitivesciencesociety.org/cognitive-science-journal/">Journal Print</a> <a class="md-button" href="https://github.com/chadcwilliams/EEG-GAN_CognitiveScience2023">Data and Scripts</a> </center></p>
 <h4 id="abstract"><i>Abstract:</i></h4>
 <p>There is major potential for using electroencephalography (EEG) in brain decoding that has been untapped due to the need for large amounts of data. Advances in machine learning have mitigated this need through data augmentation techniques, such as Generative Adversarial Networks (GANs). Here, we gauged the extent to which GANs can augment EEG data to enhance classification performance. Our objectives were to determine which classifiers benefit from GAN-augmented