{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T11:43:24Z","timestamp":1778154204047,"version":"3.51.4"},"reference-count":43,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2018,9,18]],"date-time":"2018-09-18T00:00:00Z","timestamp":1537228800000},"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":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2018,9,18]]},"abstract":"<jats:p>Person identification technology recognizes individuals by exploiting their unique, measurable physiological and behavioral characteristics. However, the state-of-the-art person identification systems have been shown to be vulnerable, e.g., anti-surveillance prosthetic masks can thwart face recognition, contact lenses can trick iris recognition, vocoder can compromise voice identification and fingerprint films can deceive fingerprint sensors. EEG (Electroencephalography)-based identification, which utilizes the user's brainwave signals for identification and offers a more resilient solution, has recently drawn a lot of attention. However, the state-of-the-art systems cannot achieve similar accuracy as the aforementioned methods. We propose MindID, an EEG-based biometric identification approach, with the aim of achieving high accuracy and robust performance. At first, the EEG data patterns are analyzed and the results show that the Delta pattern contains the most distinctive information for user identification. Next, the decomposed Delta signals are fed into an attention-based Encoder-Decoder RNNs (Recurrent Neural Networks) structure which assigns varying attention weights to different EEG channels based on their importance. The discriminative representations learned from the attention-based RNN are used to identify the user through a boosting classifier. The proposed approach is evaluated over 3 datasets (two local and one public). One local dataset (EID-M) is used for performance assessment and the results illustrate that our model achieves an accuracy of 0.982 and significantly outperforms the state-of-the-art and relevant baselines. The second local dataset (EID-S) and a public dataset (EEG-S) are utilized to demonstrate the robustness and adaptability, respectively. The results indicate that the proposed approach has the potential to be widely deployed in practical settings.<\/jats:p>","DOI":"10.1145\/3264959","type":"journal-article","created":{"date-parts":[[2018,9,19]],"date-time":"2018-09-19T11:58:41Z","timestamp":1537358321000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":64,"title":["MindID"],"prefix":"10.1145","volume":"2","author":[{"given":"Xiang","family":"Zhang","sequence":"first","affiliation":[{"name":"University of New South Wales, CSE, UNSW, Sydney, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lina","family":"Yao","sequence":"additional","affiliation":[{"name":"University of New South Wales, CSE, UNSW, Sydney, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Salil S.","family":"Kanhere","sequence":"additional","affiliation":[{"name":"University of New South Wales, CSE, UNSW, Sydney, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunhao","family":"Liu","sequence":"additional","affiliation":[{"name":"Tsinghua University, School of Software, Tsinghua University, Beijing, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Gu","sequence":"additional","affiliation":[{"name":"RMIT University, Melbourne, VIC, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaixuan","family":"Chen","sequence":"additional","affiliation":[{"name":"University of New South Wales, CSE, UNSW, Sydney, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2018,9,18]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Multiple object recognition with visual attention. arXiv preprint arXiv:1412.7755","author":"Ba Jimmy","year":"2014","unstructured":"Jimmy Ba , Volodymyr Mnih , and Koray Kavukcuoglu . 2014. 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The International Joint Conference on Neural Networks (IJCNN) (2018)."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0801819105"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0002827"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijpsycho.2015.02.006"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/wics.1262"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/SPMB.2014.7002950"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1300\/J184v07n02_07"},{"key":"e_1_2_1_14_1","volume-title":"The functional significance of delta oscillations in cognitive processing. Frontiers in integrative neuroscience 7","author":"Harmony Thal\u00eda","year":"2013","unstructured":"Thal\u00eda Harmony . 2013. The functional significance of delta oscillations in cognitive processing. 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IEEE, 1--6."},{"key":"e_1_2_1_16_1","volume-title":"Fuzzy and neuro-fuzzy systems in medicine","author":"Kerem Dan H","unstructured":"Dan H Kerem and Amir B Geva . 2017. Brain state identification and forecasting of acute pathology using unsupervised fuzzy clustering of EEG temporal patterns . In Fuzzy and neuro-fuzzy systems in medicine . CRC Press , 19--68. Dan H Kerem and Amir B Geva. 2017. Brain state identification and forecasting of acute pathology using unsupervised fuzzy clustering of EEG temporal patterns. In Fuzzy and neuro-fuzzy systems in medicine. CRC Press, 19--68."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/IranianCEE.2016.7585697"},{"key":"e_1_2_1_18_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik","year":"2014","unstructured":"Diederik Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). 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A field study of the accuracy and reliability of a biometric iris recognition system. Science 8 Justice 53, 2 (2013), 98--102."},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDAR.2017.139"},{"key":"e_1_2_1_24_1","volume-title":"Signal Processing in Neuroscience","author":"Li.","unstructured":"xiaoli Li. 2016. Signal Processing in Neuroscience . Springer , 8--12. xiaoli Li. 2016. Signal Processing in Neuroscience. Springer, 8--12."},{"key":"e_1_2_1_25_1","volume-title":"Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025","author":"Luong Minh-Thang","year":"2015","unstructured":"Minh-Thang Luong , Hieu Pham , and Christopher D Manning . 2015. Effective approaches to attention-based neural machine translation. arXiv preprint arXiv:1508.04025 ( 2015 ). Minh-Thang Luong, Hieu Pham, and Christopher D Manning. 2015. 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Expert Evidence and Scientific Proof in Criminal Trials (2017)."},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2013.2266831"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1038\/sj.npp.1300888"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1152\/jn.00249.2015"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2016.09.365"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.appet.2011.04.004"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2004.827072"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/2815317.2815341"},{"key":"e_1_2_1_36_1","volume-title":"Normal and Altered States of Function","author":"Steriade Mircea","unstructured":"Mircea Steriade . 1991. Alertness , quiet sleep, dreaming . In Normal and Altered States of Function . Springer , 279--357. Mircea Steriade. 1991. Alertness, quiet sleep, dreaming. 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JA Unar, Woo Chaw Seng, and Almas Abbasi. 2014. A review of biometric technology along with trends and prospects. Pattern recognition 47, 8 (2014), 2673--2688."},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1122"},{"key":"e_1_2_1_40_1","volume-title":"Survey on the attention based RNN model and its applications in computer vision. arXiv preprint arXiv:1601.06823","author":"Wang Feng","year":"2016","unstructured":"Feng Wang and David MJ Tax . 2016. Survey on the attention based RNN model and its applications in computer vision. arXiv preprint arXiv:1601.06823 ( 2016 ). Feng Wang and David MJ Tax. 2016. Survey on the attention based RNN model and its applications in computer vision. arXiv preprint arXiv:1601.06823 (2016)."},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1523\/JNEUROSCI.2643-09.2009"},{"key":"e_1_2_1_42_1","volume-title":"Multimodality Sensor Data Classification with Selective Attention. 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