{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T22:52:07Z","timestamp":1768517527423,"version":"3.49.0"},"reference-count":26,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,8,25]],"date-time":"2022-08-25T00:00:00Z","timestamp":1661385600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Department Project of Jilin Province","award":["20200401095GX"],"award-info":[{"award-number":["20200401095GX"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Recently, emotional electroencephalography (EEG) has been of great importance in brain\u2013computer interfaces, and it is more urgent to realize automatic emotion recognition. The EEG signal has the disadvantages of being non-smooth, non-linear, stochastic, and susceptible to background noise. Additionally, EEG signal processing network models have the disadvantages of a large number of parameters and long training time. To address the above issues, a novel model is presented in this paper. Initially, a deep sparse autoencoder network (DSAE) was used to remove redundant information from the EEG signal and reconstruct its underlying features. Further, combining a convolutional neural network (CNN) with long short-term memory (LSTM) can extract relevant features from task-related features, mine the correlation between the 32 channels of the EEG signal, and integrate contextual information from these frames. The proposed DSAE + CNN + LSTM (DCRNN) model was experimented with on the public dataset DEAP. The classification accuracies of valence and arousal reached 76.70% and 81.43%, respectively. Meanwhile, we conducted experiments with other comparative methods to further demonstrate the effectiveness of the DCRNN method.<\/jats:p>","DOI":"10.3390\/e24091187","type":"journal-article","created":{"date-parts":[[2022,8,25]],"date-time":"2022-08-25T21:28:12Z","timestamp":1661462892000},"page":"1187","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Deep Sparse Autoencoder and Recursive Neural Network for EEG Emotion Recognition"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3878-6662","authenticated-orcid":false,"given":"Qi","family":"Li","sequence":"first","affiliation":[{"name":"Department of Electronics and Information Engineering, Changchun University of Science and Technology, Changchun 130012, China"}]},{"given":"Yunqing","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Electronics and Information Engineering, Changchun University of Science and Technology, Changchun 130012, China"}]},{"given":"Yujie","family":"Shang","sequence":"additional","affiliation":[{"name":"Department of Electronics and Information Engineering, Changchun University of Science and Technology, Changchun 130012, China"}]},{"given":"Qiong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Electronics and Information Engineering, Changchun University of Science and Technology, Changchun 130012, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4134-9017","authenticated-orcid":false,"given":"Fei","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Electronics and Information Engineering, Changchun University of Science and Technology, Changchun 130012, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Luo, J., Tian, Y., Yu, H., Chen, Y., and Wu, M. 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