{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T05:24:34Z","timestamp":1751520274643,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":32,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,10,29]],"date-time":"2021-10-29T00:00:00Z","timestamp":1635465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,10,29]]},"DOI":"10.1145\/3500931.3500933","type":"proceedings-article","created":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T05:13:49Z","timestamp":1640236429000},"page":"7-10","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Application of Deep Learning Method in Emotional Brain computer Interface"],"prefix":"10.1145","author":[{"given":"Shangpu","family":"Wu","sequence":"first","affiliation":[{"name":"Electronic engineering, TianGong University, Xiqing, Tianjin, China"}]}],"member":"320","published-online":{"date-parts":[[2021,12,22]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_3_2_1_1_1","DOI":"10.1088\/1741-2552\/aab2f2"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_2_1","DOI":"10.3171\/2009.4.FOCUS0979"},{"key":"e_1_3_2_1_3_1","volume-title":"Assessment and cost comparison of sleep-deprived eeg, mri and pet in the prediction of surgical treatment for epilepsy. Seizure., 11: 303--309","author":"Jr D. B.","year":"2002","unstructured":"Jr , D. B. , Bell , W. L. , Jr , J. , Mathews , V. P. , Glazier , S. S. ( 2002 ) Assessment and cost comparison of sleep-deprived eeg, mri and pet in the prediction of surgical treatment for epilepsy. Seizure., 11: 303--309 . Jr, D. B., Bell, W. L., Jr, J., Mathews, V. P., Glazier, S. S. (2002) Assessment and cost comparison of sleep-deprived eeg, mri and pet in the prediction of surgical treatment for epilepsy. Seizure., 11: 303--309."},{"key":"e_1_3_2_1_4_1","article-title":"A tutorial survey of architectures, algorithms, and applications for deep learning","author":"Deng L.","year":"2014","unstructured":"Deng , L. ( 2014 ) A tutorial survey of architectures, algorithms, and applications for deep learning . Apsipa Transactions on Signal & Information Processing., 3: -. Deng, L. (2014) A tutorial survey of architectures, algorithms, and applications for deep learning. Apsipa Transactions on Signal & Information Processing., 3: -.","journal-title":"Apsipa Transactions on Signal & Information Processing., 3: -."},{"key":"e_1_3_2_1_5_1","volume-title":"Deep convolutional neural network for the automated detection and diagnosis of seizure using eeg signals. Computers in Biology and Medicine., 100: 270--278","author":"Acharya U. R.","year":"2017","unstructured":"Acharya , U. R. , Oh , S. L. , Hagiwara , Y. , Tan , J. H. , Adeli , H. ( 2017 ) Deep convolutional neural network for the automated detection and diagnosis of seizure using eeg signals. Computers in Biology and Medicine., 100: 270--278 . Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., Adeli, H. (2017) Deep convolutional neural network for the automated detection and diagnosis of seizure using eeg signals. Computers in Biology and Medicine., 100: 270--278."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_6_1","DOI":"10.1109\/SPMB.2017.8257015"},{"key":"e_1_3_2_1_7_1","first-page":"1","volume-title":"2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI)","author":"Morabito F. C.","year":"2016","unstructured":"Morabito , F. C. , Campolo , M. , Ieracitano , C. , Ebadi , J. M. , & Bramanti , P. ( 2016 ) Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer's disease patients from scalp EEG recordings. In: 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI) . Bologna, Italy. pp. 1 -- 6 . Morabito, F. C., Campolo, M., Ieracitano, C., Ebadi, J. M., & Bramanti, P. (2016) Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer's disease patients from scalp EEG recordings. In: 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI). Bologna, Italy. pp. 1--6."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_8_1","DOI":"10.1007\/978-94-017-7239-6_14"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_9_1","DOI":"10.1109\/EMBC.2015.7318980"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_10_1","DOI":"10.1016\/j.bspc.2017.12.001"},{"key":"e_1_3_2_1_11_1","first-page":"18","volume-title":"Proceedings of the 7th Graz Brain-Computer Interface Conference","author":"Maddula R. K.","year":"2017","unstructured":"Maddula , R. K. , Stivers , J. , Mousavi , M. , Ravindran , S. , Sa , V. ( 2017 ) Deep recurrent convolutional neural networks for classifying P300 BCI signals . In Proceedings of the 7th Graz Brain-Computer Interface Conference , Graz, Austria. pp. 18 -- 22 . Maddula, R. K., Stivers, J., Mousavi, M., Ravindran, S., Sa, V. (2017) Deep recurrent convolutional neural networks for classifying P300 BCI signals. In Proceedings of the 7th Graz Brain-Computer Interface Conference, Graz, Austria. pp. 18--22."},{"key":"e_1_3_2_1_12_1","first-page":"149","article-title":"Mindid: person identification from brain waves through attention-based recurrent neural network","volume":"2","author":"Zhang","year":"2017","unstructured":"X Zhang , Yao, L., Kanhere , S. S. , Liu , Y. , Chen , K. ( 2017 ) Mindid: person identification from brain waves through attention-based recurrent neural network . Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies. , 2 : 149 . X Zhang, Yao, L., Kanhere, S. S., Liu, Y., Chen, K. (2017) Mindid: person identification from brain waves through attention-based recurrent neural network. Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies., 2: 149.","journal-title":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies."},{"key":"e_1_3_2_1_13_1","first-page":"493","volume-title":"2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)","author":"Hajinoroozi M.","year":"2016","unstructured":"Hajinoroozi , M. , Mao , Z. , Huang , Y. ( 2016 ) Prediction of driver's drowsy and alert states from EEG signals with deep learning . In: 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) . Cancun, Mexico. pp. 493 -- 496 . Hajinoroozi, M., Mao, Z., Huang, Y. (2016) Prediction of driver's drowsy and alert states from EEG signals with deep learning. In: 2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). Cancun, Mexico. pp. 493--496."},{"key":"e_1_3_2_1_14_1","first-page":"493","volume-title":"2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Banff, AB, Canada.","author":"Prasad M.","year":"2017","unstructured":"Prasad , M. , Huang , Y. C. , Wang , Y. K. , Lin , C. T. ( 2017 ). Brain Dynamic States Analysis based 3D Convolutional Neural Network . In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Banff, AB, Canada. pp. 493 -- 496 . Prasad, M., Huang, Y. C., Wang, Y. K., Lin, C. T. (2017). Brain Dynamic States Analysis based 3D Convolutional Neural Network. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Banff, AB, Canada. pp. 493--496."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_15_1","DOI":"10.23919\/ICACT.2018.8323716"},{"key":"e_1_3_2_1_16_1","first-page":"27","volume-title":"Japan.","author":"Yanagimoto M","year":"2016","unstructured":"Yanagimoto M , Sugimoto C. ( 2016 ) Recognition of persisting emotional valence from EEG using convolutional neural networks In: 2016 IEEE 9th International Workshop on Computational Intelligence and Applications (IWCIA). Hiroshima , Japan. pp. 27 -- 32 . Yanagimoto M, Sugimoto C. (2016) Recognition of persisting emotional valence from EEG using convolutional neural networks In: 2016 IEEE 9th International Workshop on Computational Intelligence and Applications (IWCIA). Hiroshima, Japan. pp. 27--32."},{"key":"e_1_3_2_1_17_1","first-page":"18","volume-title":"Analyze EEG Signals with Convolutional Neural Network Based on Power Spectrum Feature Selection","author":"Jiang H.","year":"2017","unstructured":"Jiang , H. , Liu , W. Yao , L. ( 2017 ) Analyze EEG Signals with Convolutional Neural Network Based on Power Spectrum Feature Selection . In : CENet 2017-the 7th International Conference on Computer Engineering and Networks. Shanghai, China . pp. 18 -- 24 . Jiang, H., Liu, W. Yao, L. (2017) Analyze EEG Signals with Convolutional Neural Network Based on Power Spectrum Feature Selection. In: CENet 2017-the 7th International Conference on Computer Engineering and Networks. Shanghai, China. pp. 18--24."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_18_1","DOI":"10.1109\/ICCSS.2017.8091408"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_19_1","DOI":"10.1109\/CIDM.2014.7008672"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_20_1","DOI":"10.14569\/IJACSA.2017.081046"},{"key":"e_1_3_2_1_21_1","volume-title":"Spatial-temporal recurrent neural network for emotion recognition","author":"Zhang T.","year":"2018","unstructured":"Zhang , T. , Zheng , W. , Cui , Z. , Zong , Y. , Li , Y. ( 2018 ) Spatial-temporal recurrent neural network for emotion recognition . IEEE transactions on cybernetics., 49: 1--9. Zhang, T., Zheng, W., Cui, Z., Zong, Y., Li, Y. (2018) Spatial-temporal recurrent neural network for emotion recognition. IEEE transactions on cybernetics., 49: 1--9."},{"key":"e_1_3_2_1_22_1","volume-title":"SIGIR 2015 Workshop on Neuro-Physiological Methods in IR Research","author":"Xiang Li","year":"2015","unstructured":"Xiang Li , Peng Zhang, Dawei Song , Guangliang Yu, Yuexian Hou , and Bin Hu. ( 2015 ) EEG based emotion identification using unsupervised deep feature learning. (2015) . In: SIGIR 2015 Workshop on Neuro-Physiological Methods in IR Research . Santiago, Chile. Xiang Li, Peng Zhang, Dawei Song, Guangliang Yu, Yuexian Hou, and Bin Hu. (2015) EEG based emotion identification using unsupervised deep feature learning. (2015). In: SIGIR 2015 Workshop on Neuro-Physiological Methods in IR Research. Santiago, Chile."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_23_1","DOI":"10.1109\/NER.2015.7146583"},{"key":"e_1_3_2_1_24_1","first-page":"148","volume-title":"EEG-based affect states classification using deep belief networks","author":"Xu H.","year":"2016","unstructured":"Xu , H. , Plataniotis , K. N. ( 2016 ) EEG-based affect states classification using deep belief networks . In : Digital Media Industry & Academic Forum (DMIAF) . Santorini, Greece. pp. 148 -- 153 . Xu, H., Plataniotis, K. N. (2016) EEG-based affect states classification using deep belief networks. In: Digital Media Industry & Academic Forum (DMIAF). Santorini, Greece. pp. 148--153."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_25_1","DOI":"10.1109\/BIBE.2014.26"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_26_1","DOI":"10.1109\/MMSP.2016.7813351"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_27_1","DOI":"10.1109\/UPCON.2017.8251115"},{"key":"e_1_3_2_1_28_1","first-page":"373","volume-title":"Mood and Social Context Using EEG Signals. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG","author":"Miranda-Correa J. A.","year":"2018","unstructured":"Miranda-Correa , J. A. , Patras , I. ( 2018 ) A Multi-Task Cascaded Network for Prediction of Affect, Personality , Mood and Social Context Using EEG Signals. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018). Xi'an, China. pp. 373 -- 380 . Miranda-Correa, J. A., Patras, I. (2018) A Multi-Task Cascaded Network for Prediction of Affect, Personality, Mood and Social Context Using EEG Signals. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018). Xi'an, China. pp. 373--380."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_29_1","DOI":"10.1109\/IJCNN.2018.8489331"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_30_1","DOI":"10.1109\/ICMEW.2015.7169796"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_31_1","DOI":"10.1016\/j.patcog.2017.10.013"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_32_1","DOI":"10.1016\/j.neunet.2014.09.003"}],"event":{"acronym":"ISAIMS 2021","name":"ISAIMS 2021: 2nd International Symposium on Artificial Intelligence for Medicine Sciences","location":"Beijing China"},"container-title":["Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3500931.3500933","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3500931.3500933","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:49:40Z","timestamp":1750182580000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3500931.3500933"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,29]]},"references-count":32,"alternative-id":["10.1145\/3500931.3500933","10.1145\/3500931"],"URL":"https:\/\/doi.org\/10.1145\/3500931.3500933","relation":{},"subject":[],"published":{"date-parts":[[2021,10,29]]},"assertion":[{"value":"2021-12-22","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}