{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:11:25Z","timestamp":1760148685122,"version":"build-2065373602"},"reference-count":24,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T00:00:00Z","timestamp":1685404800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["52130403"],"award-info":[{"award-number":["52130403"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>There is an important application value in assessing an operator\u2019s mental pressure (MP) level in human\u2013computer cooperative tasks through continuous asymmetric electroencephalogram (EEG) signals, which can help predict hidden risks. Due to the different distributions of EEG features in different periods, it is particularly challenging to accurately identify brain states by training and testing asymmetric EEG signals with static pattern classifiers. Due to the limitations of non-stationary neurophysiological data capture technology, cross-session MP recognition schemes can only be used as an auxiliary means in practical applications. Deep learning methods can achieve stable feature extraction at a high level. Based on this advantage, this paper proposes a triplet loss (TL)-based CNN model that can automatically update the weights of shallow hidden neurons in cross-session MP classification tasks. Firstly, the generalization ability of the CNN model under both intra-session and cross-session conditions is evaluated. Moreover, the proposed model is compared with the existing MP classifier under different feature selection and noise destruction modes. According to the results, our TL-based CNN model has high performance in processing cross-session EEG features.<\/jats:p>","DOI":"10.3390\/sym15061173","type":"journal-article","created":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T02:01:15Z","timestamp":1685498475000},"page":"1173","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Mental Pressure Recognition Method Based on CNN Model and EEG Signal under Cross Session"],"prefix":"10.3390","volume":"15","author":[{"given":"Song","family":"Zhou","sequence":"first","affiliation":[{"name":"Software College, Northeastern University, Shenyang 110169, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianhan","family":"Gao","sequence":"additional","affiliation":[{"name":"Software College, Northeastern University, Shenyang 110169, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Xu","sequence":"additional","affiliation":[{"name":"Science and Technology on Special System Simulation Laboratory, Beijing Simulation Center, Beijing 100854, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.neubiorev.2012.10.003","article-title":"Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness","volume":"44","author":"Borghini","year":"2014","journal-title":"Neurosci. 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