{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T23:19:23Z","timestamp":1773098363128,"version":"3.50.1"},"reference-count":24,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T00:00:00Z","timestamp":1675296000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81901827"],"award-info":[{"award-number":["81901827"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022JM-146"],"award-info":[{"award-number":["2022JM-146"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017596","name":"Natural Science Basic Research Program of Shaanxi province","doi-asserted-by":"publisher","award":["81901827"],"award-info":[{"award-number":["81901827"]}],"id":[{"id":"10.13039\/501100017596","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017596","name":"Natural Science Basic Research Program of Shaanxi province","doi-asserted-by":"publisher","award":["2022JM-146"],"award-info":[{"award-number":["2022JM-146"]}],"id":[{"id":"10.13039\/501100017596","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The original EEG data collected are the 1D sequence, which ignores spatial topology information; Feature Pyramid Networks (FPN) is better at small dimension target detection and insufficient feature extraction in the scale transformation than CNN. We propose a method of FPN and Long Short-Term Memory (FPN-LSTM) for EEG feature map-based emotion recognition. According to the spatial arrangement of brain electrodes, the Azimuth Equidistant Projection (AEP) is employed to generate the 2D EEG map, which preserves the spatial topology information; then, the average power, variance power, and standard deviation power of three frequency bands (\u03b1, \u03b2, and \u03b3) are extracted as the feature data for the EEG feature map. BiCubic interpolation is employed to interpolate the blank pixel among the electrodes; the three frequency bands EEG feature maps are used as the G, R, and B channels to generate EEG feature maps. Then, we put forward the idea of distributing the weight proportion for channels, assign large weight to strong emotion correlation channels (AF3, F3, F7, FC5, and T7), and assign small weight to the others; the proposed FPN-LSTM is used on EEG feature maps for emotion recognition. The experiment results show that the proposed method can achieve Value and Arousal recognition rates of 90.05% and 90.84%, respectively.<\/jats:p>","DOI":"10.3390\/s23031622","type":"journal-article","created":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T01:53:54Z","timestamp":1675302834000},"page":"1622","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Feature Pyramid Networks and Long Short-Term Memory for EEG Feature Map-Based Emotion Recognition"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7581-7945","authenticated-orcid":false,"given":"Xiaodan","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Electronics and Information, Xi\u2019an Polytechnic University, Xi\u2019an 710060, China"}]},{"given":"Yige","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Xi\u2019an Polytechnic University, Xi\u2019an 710060, China"}]},{"given":"Jinxiang","family":"Du","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Xi\u2019an Polytechnic University, Xi\u2019an 710060, China"}]},{"given":"Rui","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Xi\u2019an Polytechnic University, Xi\u2019an 710060, China"}]},{"given":"Kemeng","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Xi\u2019an Polytechnic University, Xi\u2019an 710060, China"}]},{"given":"Lu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information, Xi\u2019an Polytechnic University, Xi\u2019an 710060, China"}]},{"given":"Yichong","family":"She","sequence":"additional","affiliation":[{"name":"School of Life Sciences, Xidian University, Xi\u2019an 710126, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.neucom.2021.02.048","article-title":"A novel transferability attention neural network model for EEG emotion recognition","volume":"447","author":"Li","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"255","DOI":"10.26599\/BSA.2020.9050026","article-title":"Video-triggered EEG-emotion public databases and current methods: A survey","volume":"6","author":"Wanrou","year":"2020","journal-title":"Brain Sci. 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