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. 2018 Nov 13;9:889. doi: 10.3389/fneur.2018.00889

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Copyright © 2018 Jiang, Li, Yan, Yu, Wang, Ren, Li, Liu, Du, Zhou, Xing, Ren, Zhang and Yang.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Visual and automated identification of false HFOs. In (A–D), the first rows show one second of iEEG raw data, the second rows show the iEEG signal after the band-pass filtering [80–200 Hz for (A,C) and 200–500 Hz for (B,D)], the third rows show the Morlet wavelet spectrum, while the last rows represent a power spectral density map (PSD). The real HFOs appear as isolated islands (A,C) while false HFOs appear as a mountain-like shape (B,D) in the time-spectrum. On the power spectral density map, true HFOs have obvious peaks in the corresponding frequency range (arrows, A,C), but these peaks do not appear in the false HFOs (B,D).