EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 341 (2025) EPJ Web Conf., 341 (2025) 01016 References
Open Access
Issue
EPJ Web Conf.
Volume 341, 2025
2nd International Conference on Advent Trends in Computational Intelligence and Communication Technologies (ICATCICT 2025)
Article Number 01016
Number of page(s) 10
DOI https://doi.org/10.1051/epjconf/202534101016
Published online 20 November 2025
  1. D. V. Christensen et al., "2022 roadmap on neuromorphic computing and engineering," Neuromorphic Comput. Eng., vol. 2, no. 2, p. 022501, 2022. [Google Scholar]
  2. G. Edlinger, C. Rizzo, and C. Guger, "Brain Computer Interface," in Springer Handbook of Medical Technology, Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 1003-1017 [Google Scholar]
  3. Wolpaw JR, Birbaumer N, Heetderks WJ, McFarland DJ, Peckham PH, Schalk G, Donchin E, Quatrano LA, Robinson CJ, Vaughan TM et al (2000) Brain-computer interface technology: a review of the first international meeting. IEEE Trans RehabilEng 8:164-173. [Google Scholar]
  4. S. K. Mudgal, S. K. Sharma, J. Chaturvedi, and A. Sharma, "Brain computer interface advancement in neurosciences: Applications and issues," Interdiscip. Neurosurg., vol. 20, no. 100694, p. 100694, 2020. [Google Scholar]
  5. Baraka Maiseli, A. T. Abdalla, and L.V. Massawe, "Mercy Mbise, Khadija Mkocha1, Nassor Ally Nassor, Moses Ismail, James Michael and SamwelKimambo" Brain-computer interface: trend, challenges, and threats 2023," vol. 10. [Google Scholar]
  6. Vaid S, Singh P, Kaur C. EEG signal analysis for BCI interface: A review. In: International Conference on Advanced Computing and Communication Technologies, ACCT. Institute of Electrical and Electronics Engineers Inc., 2015, pp. 143-147. [Google Scholar]
  7. Mak JN, Wolpaw JR. Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects. IEEE Rev Biomed Eng. 2009;2:187-199. doi: 10.1109/RBME.2009.2035356. PMID: 20442804; PMCID: PMC2862632. [Google Scholar]
  8. Sarah N. Abdulkader, Ayman Atia, Mostafa-Sami M. Mostafa, Brain computer interfacing: Applications and challenges, Egyptian Informatics Journal, Volume 16, Issue 2, 2015,Pages 213-230, ISSN 1110-8665, https://doi.org/10.1016/j.eij.2015.06.002. [Google Scholar]
  9. Zander TO, Kothe C (2011) Towards passive brain-computer interfaces: applying brain-computer interface technology to human-machine systems in general. J Neural Eng 8:025005. [CrossRef] [PubMed] [Google Scholar]
  10. T. Al-aniand D. Tr, 'Signal Processing and Classification Approaches for Brain-Computer Interface', Intelligent and Biosensors. InTech, Jan. 01, 2010. doi: 10.5772/7032 [Google Scholar]
  11. Guo, T., Pan, K., Jiao, Y., et. al. (2022). Versatile memristor for memory and neuromorphic computing. [Google Scholar]
  12. Bihui Yu, Sibo Zhang, Lili Zhou, Jingxuan Wei, Linzhuang Sun, Liping Bu, "Brain-inspired computing based on deep learning for human-computer interaction: A review, Neurocomputing, Volume 650, 2025, 130928,ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2025.130928. [Google Scholar]
  13. Maiseli, B., Abdalla, A.T., Massawe, L.V. et al. Brain-computer interface: trend, challenges, and threats. Brain Inf. 10, 20 (2023). https://doi.org/10.1186/s40708-023-00199-3 [Google Scholar]
  14. Yike Sun, Xiaogang Chen, Bingchuan Liu, Liyan Liang, Yijun Wang, Shangkai Gao, Xiaorong Gao, Signal acquisition of brain-computer interfaces: A medical-engineering crossover perspective review, Fundamental Research, Volume 5, Issue 1, 2025, Pages 3-16, ISSN 2667-3258, https://doi.org/10.1016/j.fmre.2024.04.011. [Google Scholar]
  15. Bihui Yu, Sibo Zhang, Lili Zhou, Jingxuan Wei, Linzhuang Sun, Liping Bu, "Brain-inspired computing based on deep learning for human-computer interaction: A review, Neurocomputing, Volume 650, 2025, 130928,ISSN 0925-2312, https://doi.org/10.1016/j.neucom.2025.130928. [Google Scholar]
  16. Yang, Yikai & Eshraghian, Jason & Truong, Nhan & Nikpour, Armin & Kavehei, Omid. (2023). Neuromorphic deep spiking neural networks for seizure detection. Neuromorphic Computing and Engineering. 3. 10.1088/2634-4386/acbab8. [Google Scholar]
  17. Li, Y., Zeng, S., & Hao, J. (2019). Non-invasive optical guided tumor metastasis/vessel imaging by using lanthanide nanoprobe with enhanced downshifting emission beyond 1500 nm. ACS nano, 13(1), 248-259 [Google Scholar]
  18. Salahuddin U, Gao PX. Signal Generation, Acquisition, and Processing in Brain Machine Interfaces: A Unified Review. Front Neurosci. 2021 Sep 13;15:728178. doi: 10.3389/fnins.2021.728178. PMID: 34588951; PMCID: PMC8475516 [Google Scholar]
  19. Chih-Hung Wang, Chu-Lin Tsai, Hua Li, Chin-Hua Su, Tou-Yuan Tsai, Joyce Tay, Cheng-Yi Wu, Meng-Che Wu, Maximilian H.T. Schmieschek, Oezguer A. Onur, Chien-Chang Lee, Chien-Hua Huang, Comparison of neuroprognostic performance between manually and automatically computed gray-white matter ratios on brain computed tomography following cardiac arrest: A systematic review and meta-analysis, NeuroImage, Volume 316, 2025, 121298, ISSN 10538119, https://doi.org/10.1016/j.neuroimage.2025.121298. [Google Scholar]
  20. Xiao Sun, Zhipeng Xia, Tianyu Wang, Boyan Jin, Jialin Meng, Resistive random access memory based artificial neural network for brain-inspired neuromorphic computing, Materials Today, Volume 88, 2025, Pages 567-584,ISSN 1369-7021, https://doi.org/10.1016/j.mattod.2025.06.002. [Google Scholar]
  21. Sewell L, Abbas A, Kane N. Introduction to interpretation of the EEG in intensive care. BJA Educ. 2019 Mar;19(3):74-82. doi: 10.1016/j.bjae.2018.11.002. Epub 2018 Dec 17. PMID: 33456874; PMCID: PMC7808102. [Google Scholar]
  22. Wireko Andrew Awuah, Arjun Ahluwalia, Kwadwo Darko, Vivek Sanker, Joecelyn Kirani Tan, Pearl Ohenewaa Tenkorang, Adam Ben-Jaafar, Sruthi Ranganathan, Nicholas Aderinto, Aashna Mehta, Muhammad Hamza Shah, Kevin Lee Boon Chun, Toufik Abdul-Rahman, Oday Atallah, Bridging Minds and Machines: The Recent Advances of Brain-Computer Interfaces in Neurological and Neurosurgical Applications, World Neurosurgery, Volume 189, 2024, Pages 138-153, ISSN 1878-8750, https://doi.org/10.1016Zj.wneu.2024.05.104. [Google Scholar]
  23. Lazarou I, Nikolopoulos S, Petrantonakis PC, Kompatsiaris I, Tsolaki M. EEG-based braincomputer interfaces for communication and rehabilitation of people with motor impairment: a novel Approach of the 21 st century. Front HumNeurosci. 2018;12:14. [Google Scholar]
  24. T. Al-ani and D. Tr, 'Signal Processing and Classification Approaches for Brain-Computer Interface', Intelligent and Biosensors. InTech, Jan. 01, 2010. doi: 10.5772/7032. [Google Scholar]
  25. Park SA, Hwang HJ, Lim JH, Choi JH, Jung HK, Im CH. Evaluation of feature extraction methods for EEG-based brain-computer interfaces in terms of robustness to slight changes in electrode locations. Med Biol Eng Comput. 2013 May;51(5):571-9. doi: 10.1007/s11517-012-1026-1. Epub 2013 Jan 17. PMID: 23325145. [Google Scholar]
  26. Mak JN, Wolpaw JR. Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects. IEEE Rev Biomed Eng. 2009;2:187-199. doi: 10.1109/RBME.2009.2035356. PMID: 20442804; PMCID: PMC2862632. [Google Scholar]
  27. Chaudhary U, Birbaumer N, RamosMurguialday A. Brainecomputer interfaces for communication and rehabilitation. Nat Rev Neurol. 2016;12:513-525. [Google Scholar]
  28. Z. Yu, A. M. Abdulghani, A. Zahid, H. Heidari, M. A. Imran and Q. H. Abbasi, "An Overview of Neuromorphic Computing for Artificial Intelligence Enabled Hardware-Based Hopfield Neural Network," in IEEE Access, vol. 8, pp. 6708567099, 2020, doi: [Google Scholar]
  29. Jiadong Wu, Yinan Wang, Zhiwei Li, Lun Lu, Qingjiang Li, A Review of Computing with Spiking Neural Networks, Computers, Materials and Continua, Volume 78, Issue 3, 2024, Pages 2909-2939, ISSN 1546-2218, https://doi.org/10.32604/cmc.2024.047240. [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.