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First, the SSA-MTSNet preserves electrode topology and simultaneously enhances EEG signal frequencies and interactions among brain regions through attention mechanisms. Then, short- and long-term emotional cues are captured using multi-temporal scale convolution, followed by sequence modeling with LSTM. Evaluated on the SEED and SEED-IV datasets, the SSA-MTSNet achieves average accuracies of 98.34% and 91.79% respectively, and 95.13% and 95.30% on the valence and arousal classification tasks of DEAP, respectively. These results suggest that the proposed model achieves competitive performance across different experimental paradigms and emotion labeling strategies by jointly modeling complementary spectral, spatial, and temporal EEG information for emotion recognition.<\/jats:p>","DOI":"10.1186\/s40708-026-00309-x","type":"journal-article","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T06:37:07Z","timestamp":1780468627000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Emotion recognition using spectral-spatial attention multi-temporal scale network: EEG study"],"prefix":"10.1186","volume":"13","author":[{"given":"Zhe","family":"Tao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanghao","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leilei","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuochao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinhua","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keum Shik","family":"Hong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,3]]},"reference":[{"key":"309_CR1","doi-asserted-by":"publisher","DOI":"10.34133\/icomputing.0076","volume":"3","author":"G Pei","year":"2024","unstructured":"Pei G, Li H, Lu Y, Wang Y, Hua S, Li T (2024) Affective computing: recent advances, challenges, and future trends. 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