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Browse filesadded Removing neural signal artifacts with autoencoder-targeted adversarial transformers (AT-AT) by Benjamin J. Choi
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<td class="auto-style1"><strong>Code</strong></td>
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<td class="auto-style4" style="width: 149px"><strong>🔥up to 95%</strong> <br>
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<td class="auto-style4" style="width: 228px">
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<a href="https://www.mdpi.com/2571-905X/8/1/14" target="_blank">
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mdpi </a> 2025</td>
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<span style="color: rgb(77, 81, 86); font-family: "Google Sans", arial, sans-serif; font-size: 16px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; white-space: normal; background-color: rgb(255, 255, 255); text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">
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👏<a href="https://github.com/avgoustos/EEG-Signal-Analysis-for-Numerical-Digit-Classification-Methodologies-and-Challenges" target="_blank"> Sources </a> (also interesting review of methods congrats) </span></td>
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<td class="auto-style1"><strong>Code</strong></td>
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<td class="auto-style4" style="width: 149px"><strong>🔥97%</strong> <br>
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<td class="auto-style4" style="width: 239px">graph convolutional network (GCN)</td>
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<a href="https://huggingface.co/datasets/DavidVivancos/MindBigData2022_MNIST_EP" target="_blank">
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MindBigData2022_MNIST_EP</a> [3Gb]</td>
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<td class="auto-style4" style="width: 495px"> Benjamin J. Choi
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</td>
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<td style="width: 228px" class="auto-style4">
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<a href="https://arxiv.org/pdf/2502.05332" target="_blank">
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pdf</a> 2025</td>
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<td class="auto-style4">n/a</td>
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<td class="auto-style4" style="width: 149px"><strong>🔥up to 95%</strong> <br>
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<td class="auto-style4" style="width: 228px">
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<a href="https://www.mdpi.com/2571-905X/8/1/14" target="_blank">
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mdpi </a> 2025</td>
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<td class="auto-style4">no
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<span style="color: rgb(77, 81, 86); font-family: "Google Sans", arial, sans-serif; font-size: 16px; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; white-space: normal; background-color: rgb(255, 255, 255); text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial; display: inline !important; float: none;">
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👏<a href="https://github.com/avgoustos/EEG-Signal-Analysis-for-Numerical-Digit-Classification-Methodologies-and-Challenges" target="_blank"> Sources </a> (also interesting review of methods congrats) </span></td>
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</tr>
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