{"type":"ScholarlyArticle","title":"DBPNet: Dual-Branch Parallel Network with Temporal-Frequency Fusion for Auditory Attention Detection","authors":[{"name":"Qinke Ni","slug":"qinke-ni","given":"Qinke","family":"Ni","affiliation":"Anhui Province Key Laboratory of Multimodal Cognitive Computation , School of Computer Science and Technology , Anhui University , Hefei , 230601 , China"},{"name":"Hongyu Zhang","slug":"hongyu-zhang","given":"Hongyu","family":"Zhang","affiliation":"Anhui Province Key Laboratory of Multimodal Cognitive Computation , School of Computer Science and Technology , Anhui University , Hefei , 230601 , China"},{"name":"Cunhang Fan","slug":"cunhang-fan","given":"Cunhang","family":"Fan","affiliation":"Anhui Province Key Laboratory of Multimodal Cognitive Computation , School of Computer Science and Technology , Anhui University , Hefei , 230601 , China"},{"name":"Shengbing Pei","slug":"shengbing-pei","given":"Shengbing","family":"Pei","affiliation":"Anhui Province Key Laboratory of Multimodal Cognitive Computation , School of Computer Science and Technology , Anhui University , Hefei , 230601 , China"},{"name":"Chang Zhou","slug":"chang-zhou","given":"Chang","family":"Zhou","affiliation":"Anhui Province Key Laboratory of Multimodal Cognitive Computation , School of Computer Science and Technology , Anhui University , Hefei , 230601 , China"},{"name":"Zhao Lv","slug":"zhao-lv","given":"Zhao","family":"Lv","affiliation":"Anhui Province Key Laboratory of Multimodal Cognitive Computation , School of Computer Science and Technology , Anhui University , Hefei , 230601 , China"}],"abstract":"Auditory attention decoding (AAD) aims to recognize the attended speaker based on electroencephalography (EEG) signals in multi-talker environments. Most AAD methods only focus on the temporal or frequency domain, but neglect the relationships between these two domains, which results in the inability to simultaneously consider both time-varying and spectral-spatial information. To address this issue, this paper proposes a dual-branch parallel network with temporal-frequency fusion for AAD, named DBPNet, which consists of the temporal attentive branch and the frequency residual branch. Specifically, the temporal attentive branch aims to capture the time-varying features in the EEG time-series signal. The frequency residual branch aims to extract spectral-spatial features of multi-band EEG signals by the residual convolution. Finally, these dual branches are fused to consider both EEG signals time-varying and spectral-spatial features and get classification results. Experimental results show that compared with the best baseline, DBPNet achieves a relative improvement of 20.4% with a 0.1-second decision window for the MM-AAD dataset, but the number of trainable parameters is reduced by about 91 times.","doi":"10.24963/ijcai.2024/345","url":"https://ijcai.org/proceedings/2024/345","pdf":"https://ijcai.org/proceedings/2024/0345.pdf","markdown":"https://ijcai.org/proceedings/2024/345.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/345","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Humans and AI","track":"Main Track","number":345,"pages":{"first":3115,"last":3123},"keywords":["Humans and AI: HAI: Brain sciences","Humans and AI: HAI: Cognitive modeling"],"videos":[],"references":[{"position":1,"raw":"[Aftanas et al., 2004] Lyubomir I Aftanas, Natalya V Reva, Anton A Varlamov, Sergey V Pavlov, and Victor P Makhnev. Analysis of evoked eeg synchronization and desynchronization in conditions of emotional activation in humans: temporal and topographic characteristics. Neuro- science and behavioral physiology, 34:859-867, 2004.","title":"Analysis of evoked eeg synchronization and desynchronization in conditions of emotional activation in humans: temporal and topographic characteristics","authors":"Natalya V Lyubomir I Aftanas, Anton A Reva, Sergey V Varlamov, Victor P Pavlov, Makhnev","year":"2004","venue":"Neuroscience and behavioral physiology","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bassett and Sporns, 2017] Danielle S Bassett and Olaf Sporns. Network neuroscience. Nature neuroscience, 20(3):353-364, 2017.","title":"Network neuroscience","authors":"Danielle S Bassett, Olaf Sporns","year":"2017","venue":"Nature neuroscience","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bednar et al., 2017] Adam Bednar, Francis M Boland, and Edmund C Lalor. Different spatio-temporal electroen- cephalography features drive the successful decoding of binaural and monaural cues for sound localization. Euro- pean Journal of Neuroscience, 45(5):679-689, 2017.","title":"Different spatio-temporal electroencephalography features drive the successful decoding of binaural and monaural cues for sound localization","authors":"Adam Bednar, Francis M Boland, Edmund C Lalor","year":"2017","venue":"European Journal of Neuroscience","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cai et al., 2021a] Siqi Cai, Peiwen Li, Enze Su, and Long- han Xie. Auditory attention detection via cross-modal at- tention. Frontiers in neuroscience, 15:652058, 2021.","title":"Auditory attention detection via cross-modal attention","authors":"Cai","year":"2021","venue":"Frontiers in neuroscience","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cai et al., 2021b] Siqi Cai, Pengcheng Sun, Tanja Schultz, and Haizhou Li. Low-latency auditory spatial attention de- tection based on spectro-spatial features from eeg. In 2021 43rd Annual International Conference of the IEEE Engi- neering in Medicine & Biology Society (EMBC), pages 5812-5815. IEEE, 2021.","title":"Low-latency auditory spatial attention detection based on spectro-spatial features from eeg","authors":"Cai","year":"2021","venue":"43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Cai et al., 2023a] Siqi Cai, Peiwen Li, and Haizhou Li. A bio-inspired spiking attentional neural network for atten- tional selection in the listening brain. IEEE Transactions on Neural Networks and Learning Systems, 2023.","title":"A bio-inspired spiking attentional neural network for attentional selection in the listening brain","authors":"Cai","year":"2023","venue":"IEEE Transactions on Neural Networks and Learning Systems","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Cai et al., 2023b] Siqi Cai, Tanja Schultz, and Haizhou Li. Brain topology modeling with eeg-graphs for auditory spa- tial attention detection. IEEE Transactions on Biomedical Engineering, 2023.","title":"Brain topology modeling with eeg-graphs for auditory spatial attention detection","authors":"Cai","year":"2023","venue":"IEEE Transactions on Biomedical Engineering","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Cheng et al., 2020] Nicholas Cheng, Kok Soon Phua, Hwa Sen Lai, Pui Kit Tam, Ka Yin Tang, Kai Kei Cheng, Raye Chen-Hua Yeow, Kai Keng Ang, Cuntai Guan, and Jeong Hoon Lim. Brain-computer interface-based soft robotic glove rehabilitation for stroke. IEEE Transactions on Biomedical Engineering, 67(12):3339-3351, 2020.","title":"Brain-computer interface-based soft robotic glove rehabilitation for stroke","authors":"Nicholas Cheng, Soon Kok, Hwa Sen Phua, Pui Lai, Kit Tam, Ka Yin Tang, Kai Kei Cheng, Raye, Chen-Hua Yeow, Kai Keng Ang, Cuntai Guan, Jeong Hoon Lim","year":"2020","venue":"IEEE Transactions on Biomedical Engineering","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Choi et al., 2013] Inyong Choi, Siddharth Rajaram, Lenny A Varghese, and Barbara G Shinn-Cunningham. Quantifying attentional modulation of auditory-evoked cortical responses from single-trial electroencephalogra- phy. Frontiers in human neuroscience, 7:115, 2013.","title":"Quantifying attentional modulation of auditory-evoked cortical responses from single-trial electroencephalography","authors":"Choi","year":"2013","venue":"Frontiers in human neuroscience","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Choi et al., 2014] Inyong Choi, Le Wang, Hari Bharadwaj, and Barbara Shinn-Cunningham. Individual differences in attentional modulation of cortical responses correlate with selective attention performance. Hearing research, 314:10-19, 2014.","title":"Individual differences in attentional modulation of cortical responses correlate with selective attention performance","authors":"Choi","year":"2014","venue":"Hearing research","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Ciccarelli et al., 2019] Gregory Ciccarelli, Michael Nolan, Joseph Perricone, Paul T Calamia, Stephanie Haro, James O'sullivan, Nima Mesgarani, Thomas F Quatieri, and Christopher J Smalt. Comparison of two-talker attention decoding from eeg with nonlinear neural networks and lin- ear methods. Scientific reports, 9(1):11538, 2019.","title":"Comparison of two-talker attention decoding from eeg with nonlinear neural networks and linear methods","authors":"Gregory Ciccarelli, Michael Nolan, Joseph Perricone, Stephanie Paul T Calamia, James O Haro, Nima 'sullivan, Thomas F Mesgarani, Christopher J Quatieri, Smalt","year":"2019","venue":"Scientific reports","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Das et al., 2016] Neetha Das, Wouter Biesmans, Alexander Bertrand, and Tom Francart. The effect of head-related fil- tering and ear-specific decoding bias on auditory attention detection. Journal of neural engineering, 13(5):056014, 2016.","title":"Neetha Das, Wouter Biesmans, Alexander Bertrand, and Tom Francart. The effect of head-related filtering and ear-specific decoding bias on auditory attention detection","authors":"Das","year":"2016","venue":"Journal of neural engineering","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Das et al., 2019] Neetha Das, Tom Francart, and Alexander Bertrand. Auditory attention detection dataset kuleuven. Zenodo, 2019.","title":"Neetha Das, Tom Francart, and Alexander Bertrand. Auditory attention detection dataset kuleuven","authors":"Das","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Deng et al., 2020] Yuqi Deng, Inyong Choi, and Barbara Shinn-Cunningham. Topographic specificity of alpha power during auditory spatial attention. Neuroimage, 207:116360, 2020.","title":"Topographic specificity of alpha power during auditory spatial attention","authors":"Yuqi Deng, Inyong Choi, Barbara Shinn-Cunningham","year":"2020","venue":"Neuroimage","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Ding and Simon, 2012] Nai Ding and Jonathan Z Simon. Emergence of neural encoding of auditory objects while listening to competing speakers. Proceedings of the Na- tional Academy of Sciences, 109(29):11854-11859, 2012.","title":"Emergence of neural encoding of auditory objects while listening to competing speakers","authors":"Nai Ding, Jonathan Z Simon","year":"2012","venue":"Proceedings of the National Academy of Sciences","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Fiebelkorn and Kastner, 2019] Ian C Fiebelkorn and Sabine Kastner. A rhythmic theory of attention. Trends in cogni- tive sciences, 23(2):87-101, 2019.","title":"A rhythmic theory of attention","authors":"C Ian, Sabine Fiebelkorn, Kastner","year":"2019","venue":"Trends in cognitive sciences","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Frantzidis et al., 2010] Christos A Frantzidis, Charalampos Bratsas, Christos L Papadelis, Evdokimos Konstantinidis, Costas Pappas, and Panagiotis D Bamidis. Toward emo- tion aware computing: an integrated approach using multi- channel neurophysiological recordings and affective visual stimuli. IEEE transactions on Information Technology in Biomedicine, 14(3):589-597, 2010.","title":"Toward emotion aware computing: an integrated approach using multichannel neurophysiological recordings and affective visual stimuli","authors":"Christos A Frantzidis, Charalampos Bratsas, Christos L Papadelis, Evdokimos Konstantinidis, Costas Pappas, Panagiotis D Bamidis","year":"2010","venue":"IEEE transactions on Information Technology in Biomedicine","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Fuglsang et al., 2017] Søren Asp Fuglsang, Torsten Dau, and Jens Hjortkjær. Noise-robust cortical tracking of at- tended speech in real-world acoustic scenes. Neuroimage, 156:435-444, 2017.","title":"Noise-robust cortical tracking of attended speech in real-world acoustic scenes","authors":"Fuglsang","year":"2017","venue":"Neuroimage","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Fuglsang et al., 2018] Søren A Fuglsang, Daniel DE Wong, and Jens Hjortkjær. Eeg and audio dataset for auditory attention decoding. Zenodo, 2018.","title":"Eeg and audio dataset for auditory attention decoding","authors":"Fuglsang","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Geirnaert et al., 2020] Simon Geirnaert, Tom Francart, and Alexander Bertrand. Fast eeg-based decoding of the di- rectional focus of auditory attention using common spatial patterns. IEEE Transactions on Biomedical Engineering, 68(5):1557-1568, 2020.","title":"Fast eeg-based decoding of the directional focus of auditory attention using common spatial patterns","authors":"Simon Geirnaert, Tom Francart, Alexander Bertrand","year":"2020","venue":"IEEE Transactions on Biomedical Engineering","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Geirnaert et al., 2021] Simon Geirnaert, Servaas Vandecap- pelle, Emina Alickovic, Alain de Cheveigne, Edmund Lalor, Bernd T Meyer, Sina Miran, Tom Francart, and Alexander Bertrand. Electroencephalography-based audi- tory attention decoding: Toward neurosteered hearing de- vices. IEEE Signal Processing Magazine, 38(4):89-102, 2021.","title":"Electroencephalography-based auditory attention decoding: Toward neurosteered hearing devices","authors":"Simon Geirnaert, Servaas Vandecappelle, Emina Alickovic, Alain De Cheveigne, Edmund Lalor, Bernd T Meyer, Sina Miran, Tom Francart, Alexander Bertrand","year":"2021","venue":"IEEE Signal Processing Magazine","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Golumbic et al., 2013] Elana M Zion Golumbic, Nai Ding, Stephan Bickel, Peter Lakatos, Catherine A Schevon, Guy M McKhann, Robert R Goodman, Ronald Emerson, Ashesh D Mehta, Jonathan Z Simon, et al. Mechanisms underlying selective neuronal tracking of attended speech at a \"cocktail party\". Neuron, 77(5):980-991, 2013.","title":"Mechanisms underlying selective neuronal tracking of attended speech at a \"cocktail party","authors":"Elana M Zion Golumbic, Nai Ding, Stephan Bickel, Peter Lakatos, Catherine A Schevon, Guy M Mckhann, Ronald Robert R Goodman, Emerson, D Ashesh, Jonathan Z Mehta, Simon","year":"2013","venue":"Neuron","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Haykin and Chen, 2005] Simon Haykin and Zhe Chen. The cocktail party problem. Neural computation, 17(9):1875- 1902, 2005.","title":"The cocktail party problem","authors":"Simon Haykin, Zhe Chen","year":"2005","venue":"Neural computation","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016.","title":"Deep residual learning for image recognition","authors":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun","year":"2016","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Jiang et al., 2022] Yifan Jiang, Ning Chen, and Jing Jin. De- tecting the locus of auditory attention based on the spectro- spatial-temporal analysis of eeg. Journal of Neural Engi- neering, 19(5):056035, 2022.","title":"Detecting the locus of auditory attention based on the spectrospatial-temporal analysis of eeg","authors":"Yifan Jiang, Ning Chen, Jing Jin","year":"2022","venue":"Journal of Neural Engineering","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Jones and Boltz, 1989] Mari Riess Jones and Marilyn Boltz. Dynamic attending and responses to time. Psychological review, 96(3):459, 1989.","title":"Dynamic attending and responses to time","authors":"Boltz Jones, Mari Riess Jones, Marilyn Boltz","year":"1989","venue":"Psychological review","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Jones et al., 2002] Mari Riess Jones, Heather Moynihan, Noah MacKenzie, and Jennifer Puente. Temporal aspects of stimulus-driven attending in dynamic arrays. Psycho- logical science, 13(4):313-319, 2002.","title":"Temporal aspects of stimulus-driven attending in dynamic arrays","authors":"Mari Riess Jones, Heather Moynihan, Noah Mackenzie, Jennifer Puente","year":"2002","venue":"Psychological science","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Liu et al., 2017] Yong-Jin Liu, Minjing Yu, Guozhen Zhao, Jinjing Song, Yan Ge, and Yuanchun Shi. Real-time movie-induced discrete emotion recognition from eeg signals. IEEE Transactions on Affective Computing, 9(4):550-562, 2017.","title":"Real-time movie-induced discrete emotion recognition from eeg signals","authors":"Yong-Jin Liu, Minjing Yu, Guozhen Zhao, Jinjing Song, Yan Ge, Yuanchun Shi","year":"2017","venue":"IEEE Transactions on Affective Computing","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Lotte and Guan, 2010] Fabien Lotte and Cuntai Guan. Reg- ularizing common spatial patterns to improve bci designs: unified theory and new algorithms. IEEE Transactions on biomedical Engineering, 58(2):355-362, 2010.","title":"Regularizing common spatial patterns to improve bci designs: unified theory and new algorithms","authors":"Lotte, Guan ;, Fabien Lotte, Cuntai Guan","year":"2010","venue":"IEEE Transactions on biomedical Engineering","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Mesgarani and Chang, 2012] Nima Mesgarani and Ed- ward F Chang. Selective cortical representation of at- tended speaker in multi-talker speech perception. Nature, 485(7397):233-236, 2012.","title":"Nima Mesgarani and Edward F Chang. Selective cortical representation of attended speaker in multi-talker speech perception","authors":"Chang Mesgarani","year":"2012","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Miran et al., 2018] Sina Miran, Sahar Akram, Alireza Sheikhattar, Jonathan Z Simon, Tao Zhang, and Behtash Babadi. Real-time tracking of selective auditory attention from m/eeg: A bayesian filtering approach. Frontiers in neuroscience, 12:262, 2018.","title":"Real-time tracking of selective auditory attention from m/eeg: A bayesian filtering approach","authors":"Sina Miran, Sahar Akram, Alireza Sheikhattar, Jonathan Z Simon, Tao Zhang, Behtash Babadi","year":"2018","venue":"Frontiers in neuroscience","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[O'sullivan et al., 2015] James A O'sullivan, Alan J Power, Nima Mesgarani, Siddharth Rajaram, John J Foxe, Bar- bara G Shinn-Cunningham, Malcolm Slaney, Shihab A Shamma, and Edmund C Lalor. Attentional selection in a cocktail party environment can be decoded from single- trial eeg. Cerebral cortex, 25(7):1697-1706, 2015.","title":"Attentional selection in a cocktail party environment can be decoded from singletrial eeg","authors":"A James, Alan J O'sullivan, Nima Power, Siddharth Mesgarani, John J Rajaram, Barbara G Foxe, Malcolm Shinn-Cunningham, Shihab A Slaney, Edmund C Shamma, Lalor","year":"2015","venue":"Cerebral cortex","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Power et al., 2012] Alan J Power, John J Foxe, Emma-Jane Forde, Richard B Reilly, and Edmund C Lalor. At what time is the cocktail party? a late locus of selective atten- tion to natural speech. European Journal of Neuroscience, 35(9):1497-1503, 2012.","title":"At what time is the cocktail party? a late locus of selective attention to natural speech","authors":"Alan J Power, John J Foxe, Emma-Jane Forde, Richard B Reilly, Edmund C Lalor","year":"2012","venue":"European Journal of Neuroscience","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Puffay et al., 2023] Corentin Puffay, Bernd Accou, Lies Bollens, Mohammad Jalilpour Monesi, Jonas Vanthorn- hout, Tom Francart, et al. Relating eeg to continuous speech using deep neural networks: a review. arXiv preprint arXiv:2302.01736, 2023.","title":"Relating eeg to continuous speech using deep neural networks: a review","authors":"Corentin Puffay, Bernd Accou, Lies Bollens, Mohammad Jalilpour Monesi, Jonas Vanthornhout, Tom Francart","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Ramoser et al., 2000] Herbert Ramoser, Johannes Muller- Gerking, and Gert Pfurtscheller. Optimal spatial filtering of single trial eeg during imagined hand movement. IEEE transactions on rehabilitation engineering, 8(4):441-446, 2000.","title":"Optimal spatial filtering of single trial eeg during imagined hand movement","authors":"Ramoser","year":"2000","venue":"IEEE transactions on rehabilitation engineering","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Shi et al., 2013] Li-Chen Shi, Ying-Ying Jiao, and Bao- Liang Lu. Differential entropy feature for eeg-based vigi- lance estimation. In 2013 35th Annual International Con- ference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 6627-6630. IEEE, 2013.","title":"Differential entropy feature for eeg-based vigilance estimation","authors":"Li-Chen Shi, Ying-Ying Jiao, Bao-Liang Lu","year":"2013","venue":"35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Snyder, 1987] John Parr Snyder. Map projections-A work- ing manual, volume 1395. US Government Printing Of- fice, 1987.","title":"Map projections-A working manual","authors":"John Parr, Snyder","year":"1987","venue":null,"doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Stone, 1966] Harold S Stone. R66-50 an algorithm for the machine calculation of complex fourier series. IEEE Transactions on Electronic Computers, (4):680-681, 1966.","title":"R66-50 an algorithm for the machine calculation of complex fourier series","authors":"S Harold, Stone","year":"1966","venue":"IEEE Transactions on Electronic Computers","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Su et al., 2022] Enze Su, Siqi Cai, Longhan Xie, Haizhou Li, and Tanja Schultz. Stanet: A spatiotemporal at- tention network for decoding auditory spatial attention from eeg. IEEE Transactions on Biomedical Engineering, 69(7):2233-2242, 2022.","title":"Stanet: A spatiotemporal attention network for decoding auditory spatial attention from eeg","authors":"Su","year":"2022","venue":"IEEE Transactions on Biomedical Engineering","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Vandecappelle et al., 2021] Servaas Vandecappelle, Lucas Deckers, Neetha Das, Amir Hossein Ansari, Alexander Bertrand, and Tom Francart. Eeg-based detection of the locus of auditory attention with convolutional neural net- works. Elife, 10:e56481, 2021.","title":"Eeg-based detection of the locus of auditory attention with convolutional neural networks","authors":"Servaas Vandecappelle, Lucas Deckers, Neetha Das, Amir Hossein Ansari, Alexander Bertrand, Tom Francart","year":"2021","venue":"Elife","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Vaswani et al., 2017] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.","title":"Attention is all you need","authors":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, Illia Polosukhin","year":"2017","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Viswanathan et al., 2019] Vibha Viswanathan, Hari M Bharadwaj, and Barbara G Shinn-Cunningham. Elec- troencephalographic signatures of the neural representa- tion of speech during selective attention. Eneuro, 6(5), 2019.","title":"Electroencephalographic signatures of the neural representation of speech during selective attention","authors":"Hari M Vibha Viswanathan, Barbara G Bharadwaj, Shinn-Cunningham","year":"2019","venue":"Eneuro","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Wöstmann et al., 2016] Malte Wöstmann, Björn Herrmann, Burkhard Maess, and Jonas Obleser. Spatiotempo- ral dynamics of auditory attention synchronize with speech. Proceedings of the National Academy of Sciences, 113(14):3873-3878, 2016.","title":"Spatiotemporal dynamics of auditory attention synchronize with speech","authors":"Malte Wöstmann, Björn Herrmann, Burkhard Maess, Jonas Obleser","year":"2016","venue":"Proceedings of the National Academy of Sciences","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Zheng and Lu, 2015] Wei-Long Zheng and Bao-Liang Lu. Investigating critical frequency bands and channels for eeg-based emotion recognition with deep neural networks. IEEE Transactions on autonomous mental development, 7(3):162-175, 2015.","title":"Investigating critical frequency bands and channels for eeg-based emotion recognition with deep neural networks","authors":"Wei-Long Zheng, Bao-Liang Lu","year":"2015","venue":"IEEE Transactions on autonomous mental development","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}