The brain projects require the collection of massive electrophysiological data, aiming to the longitudinal, sectional, or populational neuroscience studies. Quality metrics automatically label the data after centralized preprocessing. However, although the waveforms-based metrics are partially useful, they may be unreliable by neglecting the spectral profiles. Here, we detected the phenomenon of parallel log spectra (PaLOS) that the scalp EEG power in the log scale were parallel to each other from 10% of 2549 HBN EEG. This phenomenon was reproduced in 8% of 412 PMDT EEG from 4 databases. We designed the PaLOS index (PaLOSi) to indicate this phenomenon by decomposing the cross-spectra at different frequencies into the common principal component spaces. We found that the PaLOS biophysically implied a prominently dominant dipole in the source space which was implausible for the resting EEG. And it may be practically resulted from excessive preprocessing. Compared with the 1966 normative EEG cross-spectra, the HBN and the PMDT EEG with PaLOS presented generally much higher electrode pairwise coherences and higher similarity of coherence-based network patterns, which went against the known frequency dependent characteristic of coherence networks. We suggest the PaLOSi should lay in the range of 0.4-0.7 for large resting EEG quality assurance.

翻译：脑计划需要采集大规模电生理数据，以支持纵向、横断面或群体神经科学研究。在集中预处理后，质控指标可自动标记数据。然而，尽管基于波形的指标部分有效，但因忽略频谱特征可能不可靠。本研究在2549份HBN脑电数据的10%中发现了平行对数频谱（PaLOS）现象，即头皮脑电功率在对数尺度上相互平行。该现象在来自4个数据库的412份PMDT脑电数据中以8%的比例被复现。我们通过将不同频率的交叉频谱分解到公共主成分空间，设计了PaLOS指数（PaLOSi）来量化此现象。研究表明，PaLOS在生物物理上暗示源空间中存在显著占主导的偶极子，这在静息态脑电中并不可信，且可能由过度预处理导致。与1966份标准脑电交叉频谱相比，存在PaLOS的HBN和PMDT脑电数据普遍表现出更高的电极对相干性和基于相干性网络模式的更高相似性，这违背了已知的相干网络频率依赖特性。建议将PaLOSi控制在0.4-0.7范围内，以保障大规模静息态脑电质量。