Page 90 - Read Online

Page 24 of 25 Nagwade et al. Soft Sci 2023;3:24 https://dx.doi.org/10.20517/ss.2023.12

89. Tseghai GB, Malengier B, Fante KA, Langenhove L Van. The status of textile-based dry EEG electrodes. 2021;21:63-70. DOI
90. He G, Dong X, Qi M. From the perspective of material science: a review of flexible electrodes for brain-computer interface. Mater
Res Express 2020;7:102001. DOI
91. Stevenson C, Chang Y, He C, et al. Emerging non-invasive brain-computer interface technologies and their clinical applications. In:
Chaurasia, M.A., Juang, CF , editors. Emerging IT/ICT and AI technologies affecting society. Singaporer: Springer; 2023. p. 269-90.
DOI
92. Dupre A, Vincent S, Iaizzo PA. Basic ECG theory, recordings, and interpretation. In: Iaizzo PA, editor. Handbook of Cardiac
Anatomy, Physiology, and Devices. Totowa, NJ: Humana Press; 2005. p. 191-201. DOI
93. Frederiks J, Swenne CA, Kors JA, et al. Within-subject electrocardiographic differences at equal heart rates: role of the autonomic
nervous system. Pflugers Arch 2001;441:717-24. DOI
94. Park C, Youn I, Han S. Single-lead ECG based autonomic nervous system assessment for meditation monitoring. Sci Rep
2022;12:22513. DOI PubMed PMC
95. Madona P, Basti RI, Zain MM. PQRST wave detection on ECG signals. Gac Sanit 2021;35 Suppl 2:S364-9. DOI PubMed
96. AlGhatrif M, Lindsay J. A brief review: history to understand fundamentals of electrocardiography. J Community Hosp Intern Med
Perspect 2012;2:14383. DOI PubMed PMC
97. Martis RJ, Acharya UR, Adeli H. Current methods in electrocardiogram characterization. Comput Biol Med 2014;48:133-49. DOI
PubMed
98. Ramasamy S, Balan A. Wearable sensors for ECG measurement: a review. Sensor Review 2018;38:412-9. DOI
99. Strik M, Ploux S, Weigel D, et al. The use of smartwatch electrocardiogram beyond arrhythmia detection. Trends Cardiovasc Med
;2023:S1050-1738(22)00153. DOI
100. Isakadze N, Martin SS. How useful is the smartwatch ECG? Trends Cardiovasc Med 2020;30:442-8. DOI PubMed
101. Abt G, Bray J, Benson AC. The validity and inter-device variability of the apple watch™ for measuring maximal heart rate. J Sports
Sci 2018;36:1447-52. DOI PubMed
102. Yu J, Park S, Kwon S, Cho K, Lee H. AI-based stroke disease prediction system using ECG and PPG bio-signals. IEEE Access
2022;10:43623-38. DOI
103. Kim T, Park J, Sohn J, Cho D, Jeon S. Bioinspired, highly stretchable, and conductive dry adhesives based on 1D-2D hybrid carbon
nanocomposites for all-in-one ECG electrodes. ACS Nano 2016;10:4770-8. DOI PubMed
104. Ankhili A, Tao X, Cochrane C, Koncar V, Coulon D, Tarlet JM. Ambulatory evaluation of ECG signals obtained using washable
textile-based electrodes made with chemically modified PEDOT:PSS. Sensors 2019;19:416. DOI PubMed PMC
105. Creel DJ. Chapter 33 - the electrooculogram. In: Levin KH, Chauvel P, editors. Handbook of Clinical Neurology. 2019. p. 495-9.
DOI
106. Soundariya RS, Renuga R. Eye movement based emotion recognition using electrooculography. In: 2017 Innovations in Power and
Advanced Computing Technologies (i-PACT). 2017 Apr 21-22; Vellore, India.IEEE; 2017. p. 1-5. DOI
107. Usakli AB, Gurkan S, Aloise F, Vecchiato G, Babiloni F. On the use of electrooculogram for efficient human computer interfaces.
Comput Intell Neurosci 2010;2010:135629. DOI PubMed PMC
108. Choudhari AM, Porwal P, Jonnalagedda V, Mériaudeau F. An electrooculography based human machine interface for wheelchair
control. Biocybern Biomed Eng 2019;39:673-85. DOI
109. Ramkumar S, Emayavaramban G, Sathesh Kumar K, Macklin Abraham Navamani J, Maheswari K, Packia Amutha Priya P. Task
identification system for elderly paralyzed patients using electrooculography and neural networks. In: Haldorai A, Ramu A,
Mohanram S, Onn CC, editors. EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing; 2019 Oct
19; Springer, Cham; 2020. p. 151-61. DOI
110. Kumar D, Sharma A. Electrooculogram-based virtual reality game control using blink detection and gaze calibration. In: 2016
International Conference on Advances in Computing, Communications and Informatics (ICACCI); 2016 Sep 21-24; Jaipur, India.
IEEE;2016. p. 2358-62. DOI
111. Chang WD. Electrooculograms for human-computer interaction: a review. Sensors 2019;19:2690. DOI PubMed PMC
112. Won Y, Lee JJ, Shin J, Lee M, Kim S, Gandla S. Biocompatible, transparent, and high-areal-coverage kirigami PEDOT:PSS
electrodes for electrooculography-derived human-machine interactions. ACS Sens 2021;6:967-75. DOI PubMed
113. Ameri SK, Kim M, Kuang IA, et al. Imperceptible electrooculography graphene sensor system for human-robot interface. npj 2D
Mater Appl 2018:2. DOI
114. Xiao J, Qu J, Li Y. An electrooculogram-based interaction method and its music-on-demand application in a virtual reality
environment. IEEE Access 2019;7:22059-70. DOI
115. Beach C, Karim N, Casson AJ. A graphene-based sleep mask for comfortable wearable eye tracking. In: 2019 41st Annual
International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2019 Jul 23-27; Berlin, Germany.
IEEE; 2019. p. 6693 6. DOI
116. Liang S, Kuo C, Lee Y, et al. Development of an EOG-based automatic sleep-monitoring eye mask. IEEE Trans Instrum Meas
2015;64:2977-85. DOI
117. Li W, Lin K, Chen L, Yang D, Ge Q, Wang Z. Self-powered wireless flexible ionogel wearable devices. ACS Appl Mater Interfaces
2023. DOI
118. Wang W, Wang S, Rastak R, et al. Strain-insensitive intrinsically stretchable transistors and circuits. Nat Electron 2021;4:143-50.