Adversarial attacks and defenses in physiological computing: a systematic review

Open Access

Review

Issue		Natl Sci Open Volume 2, Number 1, 2023


Article Number		20220023
Number of page(s)		29
Section		Information Sciences
DOI		https://doi.org/10.1360/nso/20220023
Published online		26 August 2022

Fairclough SH. Fundamentals of physiological computing. Interacting Comput 2009; 21: 133–145. [Article] [CrossRef] [Google Scholar]
Minsky M.The Society of Mind. New York: Simon and Schuster, 1988. [Google Scholar]
Jacucci G, Fairclough S, Solovey ET. Physiological computing. Computer 2015; 48: 12–16. [Article] [CrossRef] [Google Scholar]
Han X, Hu Y, Foschini L, et al. Deep learning models for electrocardiograms are susceptible to adversarial attack. Nat Med 2020; 26: 360–363. [Article] [CrossRef] [PubMed] [Google Scholar]
Lance BJ, Kerick SE, Ries AJ, et al. Brain-computer interface technologies in the coming decades. Proc IEEE 2012; 100: 1585–1599. [Article] [CrossRef] [Google Scholar]
Daly JJ, Wolpaw JR. Brain-computer interfaces in neurological rehabilitation. Lancet Neurol 2008; 7: 1032–1043. [Article] [Google Scholar]
Huang H, Xie Q, Pan J, et al. An EEG-based brain computer interface for emotion recognition and its application in patients with disorder of consciousness. IEEE Trans Affective Comput 2021; 12: 832–842. [Article] [CrossRef] [Google Scholar]
Shanechi MM. Brain-machine interfaces from motor to mood. Nat Neurosci 2019; 22: 1554–1564. [Article] [CrossRef] [PubMed] [Google Scholar]
Chen X, Wang Y, Nakanishi M, et al. High-speed spelling with a noninvasive brain-computer interface. Proc Natl Acad Sci USA 2015; 112: E6058–E6067. [Article] [NASA ADS] [Google Scholar]
Wolpaw JR, Birbaumer N, McFarland DJ, et al. Brain-computer interfaces for communication and control. Clin Neurophysiol 2002; 113: 767–791. [Article] [CrossRef] [PubMed] [Google Scholar]
Peng R, Jiang J, Kuang G, et al. EEG-based automatic Epilepsy detection: Review and outlook (in Chinese). Acta Automatica Sinica, 2022; 48: 335–350. [Google Scholar]
Wu D, Xu Y, Lu BL. Transfer learning for EEG-based brain-computer interfaces: A review of progress made since 2016. IEEE Trans Cogn Dev Syst 2022; 14: 4–19. [Article] [CrossRef] [Google Scholar]
Rim B, Sung NJ, Min S, et al. Deep learning in physiological signal data: A survey. Sensors 2020; 20: 969. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
Lawhern VJ, Solon AJ, Waytowich NR, et al. EEGNet: A compact convolutional neural network for EEG-based brain-computer interfaces. J Neural Eng 2018; 15: 056013. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
Schirrmeister RT, Springenberg JT, Fiederer LDJ, et al. Deep learning with convolutional neural networks for EEG decoding and visualization. Hum Brain Mapp 2017; 38: 5391–5420. [Article] [CrossRef] [PubMed] [Google Scholar]
Kostas D, Rudzicz F. Thinker invariance: Enabling deep neural networks for BCI across more people. J Neural Eng 2020; 17: 056008. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
Asif U, Roy S, Tang J, et al. SeizureNet: Multi-spectral deep feature learning for seizure type classification. In: Proceedings of Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology, 2020. 77-87. [Google Scholar]
Goodfellow SD, Goodwin A, Greer R, et al. Towards understanding ECG rhythm classification using convolutional neural networks and attention mappings. In: Proceedings of the 3rd Machine Learning for Healthcare Conference, Stanford, 2018. 83-101. [Google Scholar]
Hwang B, You J, Vaessen T, et al. Deep ECGNet: An optimal deep learning framework for monitoring mental stress using ultra short-term ECG signals. TeleMed e-Health 2018; 24: 753–772. [Article] [CrossRef] [PubMed] [Google Scholar]
Szegedy C, Zaremba W, Sutskever I, et al. Intriguing properties of neural networks. In: Proceedings of International Conference on Learning Representations, Banff, 2014. [Google Scholar]
Goodfellow IJ, Shlens J, Szegedy C. Explaining and harnessing adversarial examples. In: Proceedings of International Conference on Learning Representations, San Diego, 2015. [Google Scholar]
Qiu S, Liu Q, Zhou S, et al. Review of artificial intelligence adversarial attack and defense technologies. Appl Sci 2019; 9: 909. [Article] [Google Scholar]
Miller DJ, Xiang Z, Kesidis G. Adversarial learning targeting deep neural network classification: A comprehensive review of defenses against attacks. Proc IEEE 2020; 108: 402–433. [Article] [CrossRef] [Google Scholar]
Sharif M, Bhagavatula S, Bauer L, et al. Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition. In: Proceedings of ACM SIGSAC Conference on Computer and Communications Security, Vienna, 2016. 1528-1540. [Google Scholar]
Brown TB, Mané D, Roy A, et al. Adversarial patch. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, 2017. [Google Scholar]
Chen Q, Ma X, Zhu Z, et al. Evolutionary multi-tasking single-objective optimization based on cooperative co-evolutionary memetic algorithm. In: Proceedings of the 13th International Conference on Computational Intelligence and Security, 2017. 197-201. [Google Scholar]
Athalye A, Engstrom L, Ilyas A, et al. Synthesizing robust adversarial examples. In: Proceedings of the 35th International Conference on Machine Learning, Stockholm, 2018. 284-293. [Google Scholar]
Evtimov I, Eykholt K, Fernandes E, et al. Robust physical-world attacks on deep learning visual classiffication. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, 2018. 1625-1634. [Google Scholar]
Finlayson SG, Chung HW, Kohane IS, et al. Adversarial attacks against medical deep learning systems. ArXiv: 1804.05296. [Google Scholar]
Finlayson SG, Bowers JD, Ito J, et al. Adversarial attacks on medical machine learning. Science 2019; 363: 1287–1289. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
Rahman A, Hossain MS, Alrajeh NA, et al. Adversarial examples-security threats to COVID-19 deep learning systems in medical IoT devices. IEEE Internet Things J 2021; 8: 9603–9610. [Article] [CrossRef] [Google Scholar]
Ma X, Niu Y, Gu L, et al. Understanding adversarial attacks on deep learning based medical image analysis systems. Pattern Recognition 2021; 110: 107332. [Article] [CrossRef] [Google Scholar]
Kaissis GA, Makowski MR, Rückert D, et al. Secure, privacy-preserving and federated machine learning in medical imaging. Nat Mach Intell 2020; 2: 305–311. [Article] [CrossRef] [Google Scholar]
Zhang X, Wu D, Ding L, et al. Tiny noise, big mistakes: Adversarial perturbations induce errors in brain-computer interface spellers. Natl Sci Rev 2021; 8: nwaa233. [Article] [CrossRef] [Google Scholar]
Karimian N.How to attack PPG biometric using adversarial machine learning. In: Proceedings of Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure, 2019. 11009: 1100909 [Google Scholar]
Karimian N, Woodard D, Forte D. ECG biometric: Spoofing and countermeasures. IEEE Trans Biom Behav Identity Sci 2020; 2: 257–270. [Article] [CrossRef] [Google Scholar]
Bernal SL, Celdran AH, Maimó LF, et al. Cyberattacks on miniature brain implants to disrupt spontaneous neural signaling. IEEE Access 2020; 8: 152204. [Article] [CrossRef] [Google Scholar]
Pfurtscheller G, Neuper C. Motor imagery and direct brain-computer communication. Proc IEEE 2001; 89: 1123–1134. [Article] [CrossRef] [Google Scholar]
Handy TC.Event-Related Potentials: A Methods Handbook. Boston: The MIT Press, 2005. [Google Scholar]
Lees S, Dayan N, Cecotti H, et al. A review of rapid serial visual presentation-based brain-computer interfaces. J Neural Eng 2018; 15: 021001. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
Sutton S, Braren M, Zubin J, et al. Evoked-potential correlates of stimulus uncertainty. Science 1965; 150: 1187–1188. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
Friman O, Volosyak I, Graser A. Multiple channel detection of steady-state visual evoked potentials for brain-computer interfaces. IEEE Trans Biomed Eng 2007; 54: 742–750. [Article] [CrossRef] [PubMed] [Google Scholar]
Beverina F, Palmas G, Silvoni S, et al. User adaptive BCIs: SSVEP and P300 based interfaces. Psychnology J, 2003; 1: 331–354 [Google Scholar]
Sellers EW, Donchin E. A P300-based brain-computer interface: Initial tests by ALS patients. Clin Neurophysiol 2006; 117: 538–548. [Article] [CrossRef] [PubMed] [Google Scholar]
Geller EB. Responsive neurostimulation: Review of clinical trials and insights into focal epilepsy. Epilepsy Behav 2018; 88: 11–20. [Article] [CrossRef] [Google Scholar]
Gummadavelli A, Zaveri HP, Spencer DD, et al. Expanding brain-computer interfaces for controlling epilepsy networks: Novel thalamic responsive neurostimulation in refractory epilepsy. Front Neurosci 2018; 12: 474. [Article] [CrossRef] [PubMed] [Google Scholar]
Picard R.Affective Computing. Cambridge: The MIT Press, 1997. [Google Scholar]
Ekman P, Friesen WV. Constants across cultures in the face and emotion. J Personality Soc Psychol 1971; 17: 124–129. [Article] [CrossRef] [PubMed] [Google Scholar]
Russell JA. A circumplex model of affect. J Personality Soc Psychol 1980; 39: 1161–1178. [Article] [CrossRef] [Google Scholar]
Mehrabian A.Basic Dimensions for a General Psychological Theory: Implications for Personality, Social, Environmental, and Developmental Studies. Cambridge: Oelgeschlager, Gunn & Hain, 1980. [Google Scholar]
Quan X, Zeng Z, Jiang J, et al. Physiological signals based affective computing: A systematic review (in Chinese). Acta Automatica Sinica, 2021; 47: 1769–1784. [Google Scholar]
Chittaro L, Sioni R. Affective computing vs. affective placebo: Study of a biofeedback-controlled game for relaxation training. Int J Hum-Comput Studies 2014; 72: 663–673. [Article] [CrossRef] [Google Scholar]
Aranha RV, Correa CG, Nunes FLS. Adapting software with affective computing: A systematic review. IEEE Trans Affective Comput 2021; 12: 883–899. [Article] [CrossRef] [Google Scholar]
Boeke DK, Miller ME, Rusnock CF, et al. Exploring individualized objective workload prediction with feedback for adaptive automation. In: Proceedings of Industrial and Systems Engineering Research Conference, Nashville, 2015. 1437-1446. [Google Scholar]
Aricó P, Borghini G, Di Flumeri G, et al. Adaptive automation triggered by EEG-based mental workload index: A passive brain-computer interface application in realistic air traffic control environment. Front Hum Neurosci 2016; 10: 539. [Article] [PubMed] [Google Scholar]
de Greef T, Lafeber H, van Oostendorp H, et al. Eye movement as indicators of mental workload to trigger adaptive automation. In: Proceedings of International Conference on Foundations of Augmented Cognition, San Diego, 2009. 219-228. [Google Scholar]
Park J, Zahabi M. Cognitive workload assessment of prosthetic devices: A review of literature and meta-analysis. IEEE Trans Hum-Mach Syst 2022; 52: 181–195. [Article] [CrossRef] [Google Scholar]
Coiera E.Guide to Health Informatics. Boca Raton: CRC Press, 2015. [CrossRef] [Google Scholar]
Mishra T, Wang M, Metwally AA, et al. Pre-symptomatic detection of COVID-19 from smartwatch data. Nat Biomed Eng 2020; 4: 1208–1220. [Article] [CrossRef] [PubMed] [Google Scholar]
Charlton PH, Kyriacou PA, Mant J, et al. Wearable photoplethysmography for cardiovascular monitoring. Proc IEEE 2022; 110: 355–381. [Article] [CrossRef] [PubMed] [Google Scholar]
Guo YT, Cui Y, Zhao C, et al. Machine-learning fusion approach for the prediction of atrial fibrillation onset using photoplethysmographic-based smart device. Eur Heart J 2021; 42: ehab724.3058. [Article] [CrossRef] [Google Scholar]
Singh YN, Singh SK, Ray AK. Bioelectrical signals as emerging biometrics: Issues and challenges. ISRN Signal Processing 2012; 2012: 1–13. [Article] [CrossRef] [Google Scholar]
Thomas KP, Vinod AP. Toward EEG-Based biometric systems: The great potential of brain-wave-based biometrics. IEEE Syst Man Cybern Mag 2017; 3: 6–15. [Article] [CrossRef] [Google Scholar]
Agrafioti F, Gao J, Hatzinakos D, et al. Heart Biometrics: Theory, Methods and Applications. In: Biometrics. London: InTechOpe, 2011. 199–216. [Google Scholar]
Yadav U, Abbas SN, Hatzinakos D. Evaluation of PPG biometrics for authentication in different states. In: Proceedings of International Conference on Biometrics, Queensland, 2018. 277-282. [Google Scholar]
Bianco S, Napoletano P. Biometric recognition using multimodal physiological signals. IEEE Access 2019; 7: 83581–83588. [Article] [CrossRef] [Google Scholar]
Zhang X, Wu D. On the vulnerability of CNN classifiers in EEG-based BCIs. IEEE Trans Neural Syst Rehabil Eng 2019; 27: 814–825. [Article] [CrossRef] [PubMed] [Google Scholar]
Moosavi-Dezfooli SM, Fawzi A, Frossard P. DeepFool: A simple and accurate method to fool deep neural networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, 2016. 2574-2582. [Google Scholar]
Carlini N, Wagner D. Towards evaluating the robustness of neural networks. In: Proceedings of IEEE Symposium on Security and Privacy, San Jose, 2017. 39-57. [Google Scholar]
Kurakin A, Goodfellow IJ, Bengio S. Adversarial examples in the physical world. In: Proceedings of International Conference on Learning Representations, Toulon, 2017. [Google Scholar]
Papernot N, McDaniel P, Goodfellow I, et al. Practical black-box attacks against machine learning. In: Proceedings of Asia Conference on Computer and Communications Security, Abu Dhabi, 2017. 506-519. [Google Scholar]
Xiao H, Biggio B, Brown G, et al. Is feature selection secure against training data poisoning? In: Proceedings of the 32nd International Conference on Machine Learning, Lille, 2015. 1689-1698. [Google Scholar]
Mei S, Zhu X. Using machine teaching to identify optimal training-set attacks on machine learners. In: Proceedings of AAAI Conference on Artificial Intelligence, 2015. 29: 2871-2877. [Google Scholar]
Biggio B, Nelson B, Laskov P. Support vector machines under adversarial label noise. In: Proceedings of Asian Conference on Machine Learning, Taipei, 2011. 97-112. [Google Scholar]
Fawaz HI, Forestier G, Weber J, et al. Adversarial attacks on deep neural networks for time series classification. In: Proceedings of International Joint Conference on Neural Networks, Budapest, 2019. 1-8. [Google Scholar]
Karim F, Majumdar S, Darabi H. Adversarial attacks on time series. IEEE Trans Pattern Anal Mach Intell 2021; 43: 3309–3320. [Article] [CrossRef] [PubMed] [Google Scholar]
Harford S, Karim F, Darabi H. Generating adversarial samples on multivariate time series using variational autoencoders. IEEE CAA J Autom Sin 2021; 8: 1523–1538. [Article] [CrossRef] [Google Scholar]
Cheng P, Roedig U. Personal voice assistant security and privacy—A survey. Proc IEEE 2022; 110: 476–507. [Article] [CrossRef] [Google Scholar]
Jiang X, Zhang X, Wu D. Active learning for black-box adversarial attacks in EEG-based brain-computer interfaces. In: Proceedings of IEEE Symposium Series on Computational Intelligence, Xiamen, 2019. [Google Scholar]
Liu Z, Meng L, Zhang X, et al. Universal adversarial perturbations for CNN classifiers in EEG-based BCIs. J Neural Eng 2021; 18: 0460a4. [Article] [NASA ADS] [CrossRef] [Google Scholar]
Meng L, Huang J, Zeng Z, et al. EEG-based brain-computer interfaces are vulnerable to backdoor attacks. Engineering, 2022, [Article]. [Google Scholar]
Bian R, Meng LB, Wu DR. SSVEP-based brain-computer interfaces are vulnerable to square wave attacks. Sci China Inf Sci 2022; 65: 140406. [Article] [CrossRef] [Google Scholar]
Meng L, Lin C-T, Jung T-P, et al. White-box target attack for EEG-based BCI regression problems. In: Proceedings of International Conference on Neural Information Processing, Sydney, 2019. [Google Scholar]
Aminifar A.Universal adversarial perturbations in epileptic seizure detection. In: Proceedings of International Joint Conference on Neural Networks, 2020. 1-6. [Google Scholar]
Newaz A, Haque NI, Sikder AK, et al. Adversarial attacks to machine learning-based smart healthcare systems. ArXiv: 2010.03671. [Google Scholar]
Wang S, Nepal S, Rudolph C, et al. Backdoor attacks against transfer learning with pre-trained deep learning models. IEEE Trans Serv Comput 2022; 15: 1526–1539. [Article] [CrossRef] [MathSciNet] [Google Scholar]
Maiorana E, Hine GE, Rocca DL, et al. On the vulnerability of an EEG-based biometric system to hill-climbing attacks algorithms’ comparison and possible countermeasures. In: Proceedings of IEEE 6th International Conference on Biometrics: Theory, Applications and Systems, 2013. 1-6. [Google Scholar]
Eberz S, Paoletti N, Roeschlin M, et al. Broken hearted: How to attack ECG biometrics. In: Proceedings of Network and Distributed System Security Symposium. San Diego: Internet Society, 2017. [Google Scholar]
Wu D, Lawhern VJ, Gordon S, et al. Driver drowsiness estimation from EEG signals using online weighted adaptation regularization for regression (OwARR). IEEE Trans Fuzzy Syst 2017; 25: 1522–1535. [Article] [CrossRef] [Google Scholar]
Ienca M, Haselager P, Emanuel EJ. Brain leaks and consumer neurotechnology. Nat Biotechnol 2018; 36: 805–810. [Article] [CrossRef] [PubMed] [Google Scholar]
Jarchum I. The ethics of neurotechnology. Nat Biotechnol 2019; 37: 993–996. [Article] [CrossRef] [PubMed] [Google Scholar]
Binnendijk A, Marler T, Bartels EM. Brain-Computer Interfaces: U.S. Military Applications and Implications, An Initial Assessment. Santa Monica: RAND Corporation, 2020. [CrossRef] [Google Scholar]
Sundararajan K.Privacy and security issues in brain computer interfaces. Dissertation for Master's Degree. Auckland: Auckland University of Technology, 2017. [Google Scholar]
Paoletti N, Jiang Z, Islam MA, et al. Synthesizing stealthy reprogramming attacks on cardiac devices. In: Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems, 2019. 13-22. [Google Scholar]
Karimian N, Woodard DL, Forte D. On the vulnerability of ECG verification to online presentation attacks. In: Proceedings of IEEE International Joint Conference on Biometrics, Denver, 2017. 143-151. [Google Scholar]
Bernal SL, Celdrán AH, Pérez GM, et al. Security in brain-computer interfaces. ACM Comput Surv 2022; 54: 1–35. [Article] [CrossRef] [Google Scholar]
Tramèr F, Kurakin A, Papernot N, et al. Ensemble adversarial training: Attacks and defenses. ArXiv: 1705.07204. [Google Scholar]
Hosseini H, Chen Y, Kannan S, et al. Blocking transferability of adversarial examples in black-box learning systems. ArXiv: 1703.04318. [Google Scholar]
Das N, Shanbhogue M, Chen S-T, et al. Keeping the bad guys out: Protecting and vaccinating deep learning with JPEG compression. ArXiv: 1705.02900. [Google Scholar]
Xie C, Wang J, Zhang Z, et al. Adversarial examples for semantic segmentation and object detection. In: Proceedings of IEEE International Conference on Computer Vision, Venice, 2017. 1369-1378. [Google Scholar]
Papernot N, McDaniel P, Wu X, et al. Distillation as a defense to adversarial perturbations against deep neural networks. In: Proceedings of IEEE Symposium on Security and Privacy, San Jose, 2016. 582-597. [Google Scholar]
Xu W, Evans D, Qi Y. Feature squeezing: Detecting adversarial examples in deep neural networks. ArXiv: 1704.01155. [Google Scholar]
Gu S, Rigazio L. Towards deep neural network architectures robust to adversarial examples. ArXiv: 1412.5068. [Google Scholar]
Gao J, Wang B, Lin Z, et al. DeepCloak: Masking deep neural network models for robustness against adversarial samples. ArXiv: 1702.06763. [Google Scholar]
Qayyum A, Qadir J, Bilal M, et al. Secure and robust machine learning for healthcare: A survey. IEEE Rev Biomed Eng 2021; 14: 156–180. [Article] [CrossRef] [PubMed] [Google Scholar]
Samangouei P, Kabkab M, Chellappa R. Defense-GAN: Protecting classifiers against adversarial attacks using generative models. ArXiv: 1805.06605. [Google Scholar]
Liao F, Liang M, Dong Y, et al. Defense against adversarial attacks using high-level representation guided denoiser. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, 2018. 1778-1787. [Google Scholar]
Hussein A, Djandji M, Mahmoud RA, et al. Augmenting DL with adversarial training for robust prediction of epilepsy seizures. ACM Trans Comput Healthcare 2020; 1: 1–18. [Article] [CrossRef] [MathSciNet] [Google Scholar]
Sadeghi K, Banerjee A, Gupta SK. An analytical framework for security-tuning of artificial intelligence applications under attack. In: Proceedings of IEEE International Conference On Artificial Intelligence Testing, San Francisco, 2019. 111-118. [Google Scholar]
Cai H, Venkatasubramanian KK. Detecting malicious temporal alterations of ECG signals in body sensor networks. In: Proceedings of International Conference on Network and System Security, New York, 2015. 531-539. [Google Scholar]
Cai H, Venkatasubramanian KK. Detecting signal injection attack-based morphological alterations of ECG measurements. In: Proceedings of International Conference on Distributed Computing in Sensor Systems, Washington, 2016. 127-135. [Google Scholar]
Rade R, Moosavi-Dezfooli S-M. Reducing excessive margin to achieve a better accuracy vs. robustness trade-off. In: Proceedings of International Conference on Learning Representations, 2022. [Google Scholar]
Shafahi A, Najibi M, Ghiasi A, et al. Adversarial training for free! In: Proceedings of Advances in Neural Information Processing Systems, Vancouver, 2019. [Google Scholar]
Carlini N, Wagner DA. Adversarial examples are not easily detected: Bypassing ten detection methods. In: Proceedings of Workshop on Artificial Intelligence and Security, Dallas, 2017. [Google Scholar]
Zheng WL, Liu W, Lu Y, et al. EmotionMeter: A multimodal framework for recognizing human emotions. IEEE Trans Cybern 2019; 49: 1110–1122. [Article] [CrossRef] [PubMed] [Google Scholar]
He H, Wu D. Transfer learning for brain-computer interfaces: A Euclidean space data alignment approach. IEEE Trans Biomed Eng 2020; 67: 399–410. [Article] [CrossRef] [PubMed] [Google Scholar]
Wu D, Lawhern VJ, Hairston WD, et al. Switching EEG headsets made easy: Reducing offline calibration effort using active weighted adaptation regularization. IEEE Trans Neural Syst Rehabil Eng 2016; 24: 1125–1137. [Article] [CrossRef] [PubMed] [Google Scholar]
Wu D, Jiang X, Peng R. Transfer learning for motor imagery based brain-computer interfaces: A tutorial. Neural Networks 2022; 153: 235–253. [Article] [Google Scholar]
Zhang F, Chan PPK, Biggio B, et al. Adversarial feature selection against evasion attacks. IEEE Trans Cybern 2016; 46: 766–777. [Article] [CrossRef] [PubMed] [Google Scholar]
Denning T, Matsuoka Y, Kohno T. Neurosecurity: Security and privacy for neural devices. FOC 2009; 27: E7. [Article] [CrossRef] [Google Scholar]
Rushanan M, Rubin AD, Kune DF, et al. SoK: Security and privacy in implantable medical devices and body area networks. In: Proceedings of IEEE Symposium on Security and Privacy, 2014. 524-539. [Google Scholar]
Camara C, Peris-Lopez P, Tapiador JE. Security and privacy issues in implantable medical devices: A comprehensive survey. J BioMed Inf 2015; 55: 272–289. [Article] [CrossRef] [Google Scholar]
Pycroft L, Boccard SG, Owen SLF, et al. Brainjacking: Implant security issues in invasive neuromodulation. World Neurosurg 2016; 92: 454–462. [Article] [CrossRef] [PubMed] [Google Scholar]
Xia K, Duch W, Sun Y, et al. Privacy-preserving brain-computer interfaces: A systematic review. IEEE Trans Comput Soc Syst 2022; [Article]. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.