Page 37 - BH-2-2
P. 37
Brain & Heart Predictive modeling using electroencephalogram
doi: 10.1088/1361-6579/aad57e doi: 10.1155/2022/6750757
15. Hasan MJ, Shon D, Im K, Choi HK, Yoo DS, Kim JM. Sleep 20. Kanthavel D, Sangeetha SKB, Keerthana KP. An empirical
state classification using power spectral density and residual study of vehicle to infrastructure communications-an
neural network with multichannel EEG signals. Appl Sci. intense learning of smart infrastructure for safety and
2020;10:7639. mobility. Int J Intell Netw. 2021;2:77-82.
doi: 10.3390/app10217639 doi: 10.1016/j.ijin.2021.06.003
16. Brouwer AM, Snelting A, Jaswa M, Flascher O, Krol L, 21. Aggarwal S, Chugh N. Review of machine learning
Zander T. Physiological Effects of Adaptive Cruise Control techniques for EEG based brain computer interface. Arch
Behaviour in Real Driving. In: Proceedings of the 2017 ACM Comput Methods Eng. 2022;29:3001-3020.
Workshop on an Application-oriented Approach to BCI out of doi: 10.1007/s11831-021-09684-6
the Laboratory. 2017. p. 15-19.
22. Ahn M, Jun SC, Yeom HG, Cho H. Editorial: Deep
doi: 10.1145/3038439.3038441 learning in brain-computer interface. Front Hum Neurosci.
17. Karuppusamy NS, Kang BY. Multimodal system to detect 2022;16:927567.
driver fatigue using EEG, gyroscope, and image processing. doi: 10.3389/fnhum.2022.927567
IEEE Access. 2020;8:129645-129667.
23. Zhu H, Forenzo D, He B. On the deep learning models for
doi: 10.1109/Access.2020.3009226 EEG-based brain-computer interface using motor imagery.
18. Sangeetha SKB, Kumar MS, Deeba K, Rajadurai H, IEEE Trans Neural Syst Rehabil Eng. 2022;30:2283-2291.
Maheshwari V, Dalu GT. An empirical analysis of an doi: 10.1109/TNSRE.2022.3198041
optimized pretrained deep learning model for COVID-19
diagnosis. Comput Math Methods Med. 2022;2022:9771212. 24. Immanuel RR, Sangeetha SKB. Analysis of EEG Signal
with Feature and Feature Extraction Techniques for
doi: 10.1155/2022/9771212 Emotion Recognition Using Deep Learning Techniques. In:
Proceedings of International Conference on Computational
19. Khalaf OI, Ogudo KA, Sangeetha SKB. Design of Graph-
based layered learning-driven model for anomaly Intelligence and Data Engineering. Singapore: Springer
detection in distributed cloud IoT network. Mob Inf Syst. Nature Singapore; 2022. p. 141-154.
2022;2022:6750757. doi: 10.1007/978-981-99-0609-3_10
Volume 2 Issue 2 (2024) 13 doi: 10.36922/bh.2819

