{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T16:24:45Z","timestamp":1783441485918,"version":"3.54.6"},"reference-count":53,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T00:00:00Z","timestamp":1736208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurorobot."],"abstract":"<jats:p>Significant strides have been made in emotion recognition from Electroencephalography (EEG) signals. However, effectively modeling the diverse spatial, spectral, and temporal features of multi-channel brain signals remains a challenge. This paper proposes a novel framework, the Directional Spatial and Spectral Attention Network (DSSA Net), which enhances emotion recognition accuracy by capturing critical spatial-spectral-temporal features from EEG signals. The framework consists of three modules: Positional Attention (PA), Spectral Attention (SA), and Temporal Attention (TA). The PA module includes Vertical Attention (VA) and Horizontal Attention (HA) branches, designed to detect active brain regions from different orientations. Experimental results on three benchmark EEG datasets demonstrate that DSSA Net outperforms most competitive methods. On the SEED and SEED-IV datasets, it achieves accuracies of 96.61% and 85.07% for subject-dependent emotion recognition, respectively, and 87.03% and 75.86% for subject-independent recognition. On the DEAP dataset, it attains accuracies of 94.97% for valence and 94.73% for arousal. These results showcase the framework's ability to leverage both spatial and spectral differences across brain hemispheres and regions, enhancing classification accuracy for emotion recognition.<\/jats:p>","DOI":"10.3389\/fnbot.2024.1481746","type":"journal-article","created":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T08:03:33Z","timestamp":1736237013000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Directional Spatial and Spectral Attention Network (DSSA Net) for EEG-based emotion recognition"],"prefix":"10.3389","volume":"18","author":[{"given":"Jiyao","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lang","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haifeng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongmei","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,1,7]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"5531","DOI":"10.1038\/s41467-024-49073-8","article-title":"Occipital-temporal cortical tuning to semantic and affective features of natural images predicts associated behavioral responses","volume":"15","author":"Abdel-Ghaffar","year":"2024","journal-title":"Nat. Commun"},{"key":"B2","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1109\/TAFFC.2017.2714671","article-title":"Emotions recognition using EEG signals: a survey","volume":"10","author":"Alarcao","year":"2017","journal-title":"IEEE Trans. Affect. Comput"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1046","DOI":"10.14569\/IJACSA.2017.081046","article-title":"Emotion recognition based on EEG using LSTM recurrent neural network","volume":"8","author":"Alhagry","year":"2017","journal-title":"Int. J. Adv. Comput. Sci. Appl"},{"key":"B4","doi-asserted-by":"publisher","first-page":"955","DOI":"10.14569\/IJACSA.2017.080955","article-title":"Classification of human emotions from electroencephalogram (EEG) signal using deep neural network","volume":"8","author":"Al-Nafjan","year":"2017","journal-title":"Int. J. Adv. Comput. Sci. Appl"},{"key":"B5","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1109\/ICBME.2013.6782224","article-title":"\u201cEEG-based emotion recognition using recurrence plot analysis and k nearest neighbor classifier,\u201d","volume-title":"2013 20th Iranian Conference on Biomedical Engineering (ICBME)","author":"Bahari","year":"2013"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1511.06448","article-title":"Learning representations from EEG with deep recurrent-convolutional neural networks","author":"Bashivan","year":"2015","journal-title":"arXiv preprint arXiv:1511.06448"},{"key":"B7","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Machine Learn"},{"key":"B8","doi-asserted-by":"publisher","first-page":"260718","DOI":"10.3389\/fpsyg.2017.01432","article-title":"Basic emotions in human neuroscience: neuroimaging and beyond","volume":"8","author":"Celeghin","year":"2017","journal-title":"Front. Psychol"},{"key":"B9","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Machine Learn"},{"key":"B10","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/0278-2626(92)90065-T","article-title":"Anterior cerebral asymmetry and the nature of emotion","volume":"20","author":"Davidson","year":"1992","journal-title":"Brain Cogn"},{"key":"B11","doi-asserted-by":"publisher","first-page":"890","DOI":"10.1037\/0033-2909.126.6.890","article-title":"Emotion, plasticity, context, and regulation: perspectives from affective neuroscience","volume":"126","author":"Davidson","year":"2000","journal-title":"Psychol. Bullet"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2401.05819","article-title":"TAnet: a new temporal attention network for EEG-based auditory spatial attention decoding with a short decision window","author":"Ding","year":"2024","journal-title":"arXiv preprint arXiv:2401.05819"},{"key":"B13","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/NER.2013.6695876","article-title":"\u201cDifferential entropy feature for EEG-based emotion classification,\u201d","volume-title":"2013 6th International IEEE\/EMBS Conference on Neural Engineering (NER)","author":"Duan","year":"2013"},{"key":"B14","doi-asserted-by":"publisher","first-page":"1574","DOI":"10.1109\/LSP.2022.3179946","article-title":"EEG-GCN: spatio-temporal and self-adaptive graph convolutional networks for single and multi-view EEG-based emotion recognition","volume":"29","author":"Gao","year":"2022","journal-title":"IEEE Sign. Process. Lett"},{"key":"B15","doi-asserted-by":"publisher","first-page":"e12879","DOI":"10.1111\/psyp.12879","article-title":"On the role of asymmetric frontal cortical activity in approach and withdrawal motivation: an updated review of the evidence","volume":"55","author":"Harmon-Jones","year":"2018","journal-title":"Psychophysiology"},{"key":"B16","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.inffus.2021.10.012","article-title":"Deep learning for depression recognition with audiovisual cues: a review","volume":"80","author":"He","year":"2022","journal-title":"Inform. Fus"},{"key":"B17","doi-asserted-by":"publisher","first-page":"105048","DOI":"10.1016\/j.compbiomed.2021.105048","article-title":"An adversarial discriminative temporal convolutional network for EEG-based cross-domain emotion recognition","volume":"141","author":"He","year":"2022","journal-title":"Comput. Biol. Med"},{"key":"B18","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.patrec.2017.05.020","article-title":"Deep long short-term memory structures model temporal dependencies improving cognitive workload estimation","volume":"94","author":"Hefron","year":"2017","journal-title":"Pat. Recogn. Lett"},{"key":"B19","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput"},{"key":"B20","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1109\/TAFFC.2014.2339834","article-title":"Feature extraction and selection for emotion recognition from EEG","volume":"5","author":"Jenke","year":"2014","journal-title":"IEEE Trans. Affect. Comput"},{"key":"B21","first-page":"309","article-title":"\u201cCross-subject emotion recognition with a decision tree classifier based on sequential backward selection,\u201d","volume-title":"2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Vol. 1","author":"Jiang","year":"2019"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1609.02907","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016","journal-title":"arXiv preprint arXiv:1609.02907"},{"key":"B23","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/T-AFFC.2011.15","article-title":"Deap: a database for emotion analysis; using physiological signals","volume":"3","author":"Koelstra","year":"2011","journal-title":"IEEE Trans. Affect. Comput"},{"key":"B24","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1353\/sor.2010.0049","article-title":"The neuroscience of happiness and pleasure","volume":"77","author":"Kringelbach","year":"2010","journal-title":"Soc. Res"},{"key":"B25","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"B26","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.24963\/ijcai.2018\/216","article-title":"\u201cA novel neural network model based on cerebral hemispheric asymmetry for EEG emotion recognition,\u201d","volume-title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","author":"Li","year":"2018"},{"key":"B27","doi-asserted-by":"publisher","first-page":"568","DOI":"10.1109\/TAFFC.2019.2922912","article-title":"From regional to global brain: a novel hierarchical spatial-temporal neural network model for EEG emotion recognition","volume":"13","author":"Li","year":"2019","journal-title":"IEEE Trans. Affect. Comput"},{"key":"B28","first-page":"305","article-title":"\u201cPositional-spectral-temporal attention in 3D convolutional neural networks for EEG emotion recognition,\u201d","volume-title":"2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","author":"Liu","year":"2021"},{"key":"B29","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1111\/j.1469-8986.2010.01061.x","article-title":"Adjust: an automatic EEG artifact detector based on the joint use of spatial and temporal features","volume":"48","author":"Mognon","year":"2011","journal-title":"Psychophysiology"},{"key":"B30","doi-asserted-by":"publisher","first-page":"3335854","DOI":"10.1109\/JBHI.2023.3335854","article-title":"ST-SCGNN: a spatio-temporal self-constructing graph neural network for cross-subject EEG-based emotion recognition and consciousness detection","volume":"2023","author":"Pan","year":"2023","journal-title":"IEEE J. Biomed. Health Inform"},{"key":"B31","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1126\/science.3992243","article-title":"EEG alpha activity reflects attentional demands, and beta activity reflects emotional and cognitive processes","volume":"228","author":"Ray","year":"1985","journal-title":"Science"},{"key":"B32","first-page":"2701","article-title":"\u201cInstance-adaptive graph for EEG emotion recognition,\u201d","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34","author":"Song","year":"2020"},{"key":"B33","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1109\/TAFFC.2018.2817622","article-title":"EEG emotion recognition using dynamical graph convolutional neural networks","volume":"11","author":"Song","year":"2018","journal-title":"IEEE Trans. Affect. Comput"},{"key":"B34","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1023\/A:1018628609742","article-title":"Least squares support vector machine classifiers","volume":"9","author":"Suykens","year":"1999","journal-title":"Neural Process. Lett"},{"key":"B35","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1109\/TAFFC.2020.3025777","article-title":"EEG-based emotion recognition via channel-wise attention and self attention","volume":"14","author":"Tao","year":"2020","journal-title":"IEEE Trans. Affect. Comput"},{"key":"B36","doi-asserted-by":"publisher","first-page":"1944","DOI":"10.1109\/TNSRE.2024.3399326","article-title":"TASA: temporal attention with spatial autoencoder network for odor-induced emotion classification using EEG","volume":"32","author":"Tong","year":"2024","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng"},{"key":"B37","doi-asserted-by":"publisher","first-page":"117067","DOI":"10.1016\/j.neuroimage.2020.117067","article-title":"Personality and local brain structure: their shared genetic basis and reproducibility","volume":"220","author":"Valk","year":"2020","journal-title":"NeuroImage"},{"key":"B38","doi-asserted-by":"publisher","first-page":"3762","DOI":"10.48550\/arXiv.1706.03762","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inform. Process. Syst"},{"key":"B39","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1007\/978-3-642-24955-6_87","article-title":"\u201cEEG-based emotion recognition using frequency domain features and support vector machines,\u201d","volume-title":"International Conference on Neural Information Processing","author":"Wang","year":"2011"},{"key":"B40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3666002","article-title":"Research progress of EEG-based emotion recognition: a survey","volume":"56","author":"Wang","year":"2024","journal-title":"ACM Comput. Surv"},{"key":"B41","doi-asserted-by":"publisher","first-page":"3433613","DOI":"10.1109\/TAFFC.2024.3433613","article-title":"GROP: Graph orthogonal purification network for EEG emotion recognition","volume":"2024","author":"Wu","year":"2024","journal-title":"IEEE Trans. Affect. Comput"},{"key":"B42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11571-021-09751-5","article-title":"4D attention-based neural network for EEG emotion recognition","volume":"5","author":"Xiao","year":"2022","journal-title":"Cogn. Neurodyn"},{"key":"B43","doi-asserted-by":"publisher","first-page":"107927","DOI":"10.1016\/j.cmpb.2023.107927","article-title":"EESCN: a novel spiking neural network method for EEG-based emotion recognition","volume":"243","author":"Xu","year":"2024","journal-title":"Comput. Methods Progr. Biomed"},{"key":"B44","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1109\/TAFFC.2021.3068496","article-title":"EEG feature selection via global redundancy minimization for emotion recognition","volume":"14","author":"Xu","year":"2021","journal-title":"IEEE Trans. Affect. Comput"},{"key":"B45","doi-asserted-by":"publisher","first-page":"3355488","DOI":"10.1109\/TNSRE.2024.3355488","article-title":"Embedded EEG feature selection for multi-dimension emotion recognition via local and global label relevance","volume":"2024","author":"Xu","year":"2024","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng"},{"key":"B46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.48550\/arXiv.1708.06578","article-title":"EEG-based intention recognition from spatio-temporal representations via cascade and parallel convolutional recurrent neural networks","volume":"2017","author":"Zhang","year":"2017","journal-title":"arXiv preprint arXiv:1708.06578"},{"key":"B47","doi-asserted-by":"publisher","first-page":"4779","DOI":"10.3934\/mbe.2024210","article-title":"Cross-subject EEG-based emotion recognition through dynamic optimization of random forest with sparrow search algorithm","volume":"21","author":"Zhang","year":"2024","journal-title":"Math. Biosci. Eng"},{"key":"B48","doi-asserted-by":"publisher","first-page":"2305","DOI":"10.1007\/s11760-022-02447-1","article-title":"An attention-based hybrid deep learning model for EEG emotion recognition","volume":"17","author":"Zhang","year":"2023","journal-title":"Sign. Image Video Process"},{"key":"B49","doi-asserted-by":"publisher","first-page":"1110","DOI":"10.1109\/TCYB.2018.2797176","article-title":"EmotionMeter: a multimodal framework for recognizing human emotions","volume":"49","author":"Zheng","year":"2018","journal-title":"IEEE Trans. Cybernet"},{"key":"B50","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1109\/TAMD.2015.2431497","article-title":"Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks","volume":"7","author":"Zheng","year":"2015","journal-title":"IEEE Trans. Auton. Ment. Dev"},{"key":"B51","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1109\/TAFFC.2017.2712143","article-title":"Identifying stable patterns over time for emotion recognition from EEG","volume":"10","author":"Zheng","year":"2017","journal-title":"IEEE Trans. Affect. Comput"},{"key":"B52","doi-asserted-by":"publisher","first-page":"2817622","DOI":"10.1109\/TAFFC.2020.2994159","article-title":"EEG-based emotion recognition using regularized graph neural networks","volume":"2018","author":"Zhong","year":"2020","journal-title":"IEEE Trans. Affect. Comput"},{"key":"B53","doi-asserted-by":"publisher","first-page":"3464","DOI":"10.3390\/s24113464","article-title":"EEG emotion recognition network based on attention and spatiotemporal convolution","volume":"24","author":"Zhu","year":"2024","journal-title":"Sensors"}],"container-title":["Frontiers in Neurorobotics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnbot.2024.1481746\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T08:03:44Z","timestamp":1736237024000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnbot.2024.1481746\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,7]]},"references-count":53,"alternative-id":["10.3389\/fnbot.2024.1481746"],"URL":"https:\/\/doi.org\/10.3389\/fnbot.2024.1481746","relation":{},"ISSN":["1662-5218"],"issn-type":[{"value":"1662-5218","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,7]]},"article-number":"1481746"}}