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EditorialFree Access

Editorial: Celebration of the 30th Anniversary of IJNS

Justin Dauwels

Faculty of EEMCS, Section Circuits and Systems, Delft University of Technology, Mekelweg 4, 2628 CD Delft, The Netherlands

E-mail Address: j.h.g.dauwels@tudelft.nl

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https://doi.org/10.1142/S0129065721030131Cited by:0 (Source: Crossref)

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References

1. J. Thomas, J. Jin, P. Thangavel, E. Bagheri, R. Yuvaraj, J. Dauwels, R. Rathakrishnan, J. J. Halford, S. S. Cash and B. Westover, Automated detection of interictal epileptiform discharges from scalp electroencephalograms by convolutional neural networks, Int. J. Neural Syst. 30 (2020) 2050030. Link, Web of Science, Google Scholar
2. W. Y. Peh et al., Multi-center validation study of automated classification of pathological slowing in adult scalp electroencephalograms via frequency features, Int. J. Neural Syst. 31 (2021) 2050074. Link, Web of Science, Google Scholar
3. J. Thomas et al., Automated adult epilepsy diagnostic tool based on interictal scalp electroencephalogram characteristics: A six-center study, Int. J. Neural Syst. 31 (2021) 2150016. Link, Web of Science, Google Scholar
4. P. Thangavel et al., Time–frequency decomposition of scalp electroencephalograms improves deep learning-based epilepsy diagnosis, Int. J. Neural Syst. 31 (2021) 2150032. Link, Web of Science, Google Scholar