<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Surgical Skill Assessment | MacsLAB</title><link>https://jbnu.macs.or.kr/en/tag/surgical-skill-assessment/</link><atom:link href="https://jbnu.macs.or.kr/en/tag/surgical-skill-assessment/index.xml" rel="self" type="application/rss+xml"/><description>Surgical Skill Assessment</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Tue, 13 Oct 2026 00:00:00 +0900</lastBuildDate><image><url>https://jbnu.macs.or.kr/media/icon_hu006eacee63deb1d8999057a3cbfdb748_78083_512x512_fill_lanczos_center_3.png</url><title>Surgical Skill Assessment</title><link>https://jbnu.macs.or.kr/en/tag/surgical-skill-assessment/</link></image><item><title>Modality-Aware Spectral Alignment for Personalized Federated EEG–Gaze Learning</title><link>https://jbnu.macs.or.kr/en/publication/0049-modality-aware-spectral-alignment-for-personalized-federated-eeg-gaze-learning/</link><pubDate>Tue, 13 Oct 2026 00:00:00 +0900</pubDate><guid>https://jbnu.macs.or.kr/en/publication/0049-modality-aware-spectral-alignment-for-personalized-federated-eeg-gaze-learning/</guid><description>&lt;h3 id="publication-details">Publication Details&lt;/h3>
&lt;p>Published online on October 13, 2026, in &lt;em>IEEE Signal Processing Letters&lt;/em> as an Early Access article, pages 1–5.&lt;/p>
&lt;p>The research overview above summarizes the publisher&amp;rsquo;s abstract.&lt;/p>
&lt;p>&lt;a href="https://ieeexplore.ieee.org/document/11735077" target="_blank" rel="noopener">View the article on IEEE Xplore&lt;/a>&lt;/p>
&lt;h3 id="journal-metrics-2025-jcr">Journal Metrics (2025 JCR)&lt;/h3>
&lt;table>
&lt;thead>
&lt;tr>
&lt;th>Metric&lt;/th>
&lt;th>Value&lt;/th>
&lt;/tr>
&lt;/thead>
&lt;tbody>
&lt;tr>
&lt;td>Category&lt;/td>
&lt;td>Engineering, Electrical &amp;amp; Electronic (SCIE)&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>Journal Impact Factor&lt;/td>
&lt;td>3.6&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>Journal Impact Factor without self-citations&lt;/td>
&lt;td>3.0&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>JIF percentile&lt;/td>
&lt;td>60.9&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>Top percentage&lt;/td>
&lt;td>39.1%&lt;/td>
&lt;/tr>
&lt;tr>
&lt;td>Quartile&lt;/td>
&lt;td>Q2&lt;/td>
&lt;/tr>
&lt;/tbody>
&lt;/table>
&lt;p>&lt;em>Based on the 2025 JCR record. The top percentage is calculated as 100 − 60.9 = 39.1%.&lt;/em>&lt;/p>
&lt;h3 id="authors-and-affiliations">Authors and Affiliations&lt;/h3>
&lt;ul>
&lt;li>&lt;strong>Seo-Yeon Choi&lt;/strong>: Division of Electronics and Information Engineering, Jeonbuk National University, Jeonju, South Korea.&lt;/li>
&lt;li>&lt;strong>Kyungsu Lee&lt;/strong>: Division of Electronics and Information Engineering and Department of Computer Science &amp;amp; Artificial Intelligence, Jeonbuk National University, Jeonju, South Korea.&lt;/li>
&lt;/ul>
&lt;h3 id="author-keywords">Author Keywords&lt;/h3>
&lt;p>Electroencephalography, federated learning, multimodal learning, personalization.&lt;/p></description></item></channel></rss>