<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">JCNR</journal-id><journal-title-group><journal-title>Journal of Clinical and Nursing Research</journal-title></journal-title-group><issn>2208-3685</issn><eissn>2208-3693</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/JCNR.v10i8.15434</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Research on the Design of Concentration Training Based on Electroencephalogram Signals</title><url>https://artdesignp.com/journal/JCNR/10/8/10.26689/JCNR.v10i8.15434</url><author>MaNanxing</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>10</volume><issue>8</issue><history><date date-type="pub"><published-time>2026-08-31</published-time></date></history><abstract>Aiming at the existing problems of electroencephalogram (EEG) concentration training, including difficulty in signal noise reduction, mismatch of individual differences, inaccurate quantification of concentration, and homogenized training modes, this paper systematically studies the design of EEG-based concentration training. In accordance with EEG physiological characteristics and cognitive training rules, this paper sorts out the major difficulties of current training technologies and constructs a complete training optimization system from four dimensions: signal processing optimization, individual difference modeling, dynamic quantitative evaluation, and personalized training paradigms. It effectively overcomes the defects of strong subjectivity and low standardization in traditional concentration training, and provides scientific technical ideas and a practical basis for EEG-driven cognitive intervention of concentration.</abstract><keywords>Electroencephalogram signals, Concentration, Quantitative evaluation, Personalized training</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1] &amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; Lu J, 2025, Research on Concentration Training Software Based on EEG Detection and Graphic Guidance, thesis, China Academy of Art.
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