Frontiers in Network Physiology · 2021 · 39 citations · 92 references
NursingNeuroscienceCircadian RhythmsCognitive NeurosciencePsychophysiologyContinuous ElectroencephalographyElectroencephalogram AnalysisElectroencephalogram Signal ProcessingElectroencephalographyNeuronal DynamicsContinuous RecordingElectrophysiological RecordingsFunctional Brain ConnectivityFunctional Brain NetworksNeural Time Series AnalysisUltradian Activity RhythmsUltradian Rhythms
Electroencephalography (EEG) is a widely employed tool for exploring brain dynamics and is used extensively in various domains, ranging from clinical diagnosis via neuroscience, cognitive science, cognitive psychology, psychophysiology, neuromarketing, neurolinguistics, and pharmacology to research on brain computer interfaces. EEG is the only technique that enables the continuous recording of brain dynamics over periods of time that range from a few seconds to hours and days and beyond. When taking long-term recordings, various endogenous and exogenous biological rhythms may impinge on characteristics of EEG signals. While the impact of the circadian rhythm and of ultradian rhythms on spectral characteristics of EEG signals has been investigated for more than half a century, only little is known on how biological rhythms influence characteristics of brain dynamics assessed with modern EEG analysis techniques. At the example of multiday, multichannel non-invasive and invasive EEG recordings, we here discuss the impact of biological rhythms on temporal changes of various characteristics of human brain dynamics: higher-order statistical moments and interaction properties of multichannel EEG signals as well as local and global characteristics of EEG-derived evolving functional brain networks. Our findings emphasize the need to take into account the impact of biological rhythms in order to avoid erroneous statements about brain dynamics and about evolving functional brain networks.
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Norden E. Huang, Steven Long, Man‐Li C. Wu et al. · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · 1998 · 23.3K citations · Full text
Computing, Computational Engineering, Biomedical Signal Processing +16
Estimating the reproducibility of psychological science
Numerical Recipes: The Art of Scientific Computing
Eric R. Ziegel, William H. Press, Brian P. Flannery et al. · Technometrics · 1987 · 4.6K citations