{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:26:00Z","timestamp":1760145960520,"version":"build-2065373602"},"reference-count":66,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T00:00:00Z","timestamp":1726876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Research Council","award":["820213","CUP B51E22000150006","CUP B53C22003630006","CUP J33C22002970002","2020529PCP"],"award-info":[{"award-number":["820213","CUP B51E22000150006","CUP B53C22003630006","CUP J33C22002970002","2020529PCP"]}]},{"name":"European Union\u2014NextGenerationEU","award":["820213","CUP B51E22000150006","CUP B53C22003630006","CUP J33C22002970002","2020529PCP"],"award-info":[{"award-number":["820213","CUP B51E22000150006","CUP B53C22003630006","CUP J33C22002970002","2020529PCP"]}]},{"name":"Bayesian inference\u2014Grant","award":["820213","CUP B51E22000150006","CUP B53C22003630006","CUP J33C22002970002","2020529PCP"],"award-info":[{"award-number":["820213","CUP B51E22000150006","CUP B53C22003630006","CUP J33C22002970002","2020529PCP"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents an innovative approach leveraging Neuronal Manifold Analysis of EEG data to identify specific time intervals for feature extraction, effectively capturing both class-specific and subject-specific characteristics. Different pipelines were constructed and employed to extract distinctive features within these intervals, specifically for motor imagery (MI) tasks. The methodology was validated using the Graz Competition IV datasets 2A (four-class) and 2B (two-class) motor imagery classification, demonstrating an improvement in classification accuracy that surpasses state-of-the-art algorithms designed for MI tasks. A multi-dimensional feature space, constructed using NMA, was built to detect intervals that capture these critical characteristics, which led to significantly enhanced classification accuracy, especially for individuals with initially poor classification performance. These findings highlight the robustness of this method and its potential to improve classification performance in EEG-based MI-BCI systems.<\/jats:p>","DOI":"10.3390\/s24186110","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T08:56:06Z","timestamp":1727168166000},"page":"6110","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing EEG-Based MI-BCIs with Class-Specific and Subject-Specific Features Detected by Neural Manifold Analysis"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4565-2769","authenticated-orcid":false,"given":"Mirco","family":"Frosolone","sequence":"first","affiliation":[{"name":"Institute of Cognitive Sciences and Technologies, National Research Council, Via Gian Domenico Romagnosi, 00196 Rome, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3804-1719","authenticated-orcid":false,"given":"Roberto","family":"Prevete","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1220-0919","authenticated-orcid":false,"given":"Lorenzo","family":"Ognibeni","sequence":"additional","affiliation":[{"name":"Institute of Cognitive Sciences and Technologies, National Research Council, Via Gian Domenico Romagnosi, 00196 Rome, Italy"},{"name":"Department of Computer, Control and Management Engineering \u2018Antonio Ruberti\u2019 (DIAG), Sapienza University of Rome, 00185 Rome, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1791-6416","authenticated-orcid":false,"given":"Salvatore","family":"Giugliano","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5391-168X","authenticated-orcid":false,"given":"Andrea","family":"Apicella","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6813-8282","authenticated-orcid":false,"given":"Giovanni","family":"Pezzulo","sequence":"additional","affiliation":[{"name":"Institute of Cognitive Sciences and Technologies, National Research Council, Via Gian Domenico Romagnosi, 00196 Rome, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4248-5360","authenticated-orcid":false,"given":"Francesco","family":"Donnarumma","sequence":"additional","affiliation":[{"name":"Institute of Cognitive Sciences and Technologies, National Research Council, Via Gian Domenico Romagnosi, 00196 Rome, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1109\/RBME.2020.2969915","article-title":"A review on machine learning for EEG signal processing in bioengineering","volume":"14","author":"Hosseini","year":"2020","journal-title":"IEEE Rev. 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