On the stationarity and normality of the electroencephalographic data during sleep stages

Adaptable and Robust EEG Bad Channel Detection Using Local Outlier Factor (LOF)

Electroencephalogram (EEG) data are typically affected by artifacts. The detection and removal of bad channels (i.e., with poor signal-to-noise ratio) is a crucial initial step. EEG data acquired from different populations require different cleaning strategies due to the inherent differences in the data quality, the artifacts' nature, and the employed experimental paradigm. To deal with such differences, we propose a robust EEG bad channel detection method based on the Local Outlier Factor (LOF) algorithm. Unlike most existing bad channel detection algorithms that look for the global distribution of channels, LOF identifies bad channels relative to the local cluster of channels, which makes it adaptable to any kind of EEG. To test the performance and versatility of the proposed algorithm, we validated it on EEG acquired from three populations (newborns, infants, and adults) and using two experimental paradigms (event-related and frequency-tagging). We found that LOF can be applied to all kinds of EEG data after calibrating its main hyperparameter: the LOF threshold. We benchmarked the performance of our approach with the existing state-of-the-art (SoA) bad channel detection methods. We found that LOF outperforms all of them by improving the F1 Score, our chosen performance metric, by about 40% for newborns and infants and 87.5% for adults.

Multiple window time-frequency distribution and coherence of EEG using Slepian sequences and hermite functions

Multiple window (MW) time-frequency analysis (TFA) is a newly developed technique to estimate a time-varying spectrum for random nonstationary signals with low bias and variance. In this paper, we describe the application of MW-TFA techniques to electroencephalogram (EEG) and compare the results with those of the conventional spectrogram. We find that the MW-TFA provide us with not only low bias and variance time-frequency (TF) distribution for EEG but also TF coherence estimation between a single realization of EEG recorded from two sites. We also compare the performance of the MW-TFA using two sets of windows, Slepian sequences, and Hermite functions. If care is taken in matching the two windows, we find no noticeable difference in the resulting TF representations.

Nonlinear analysis of the hippocampal subfields of CA1 and the dentate gyrus

The paper discusses the use of nonlinear bispectral analysis in examining the hippocampal EEG collected at subfields of CA1 and the dentate gyrus during the vigilance state of REM sleep. The cross-bispectrum and its unique capabilities of detecting and quantifying quadratic nonlinear interactions occurring between these two hippocampal subfields are explained and demonstrated with simulation examples and EEG data. It was found in this study that quadratic nonlinear interactions exist between CA1 and the dentate gyrus in the 6-8 frequency band which dominates the theta (theta) rhythm observed in the hippocampal EEG during REM sleep. As a result, energy components around the frequency band of the second-order harmonics of theta rhythm are not totally spontaneous, but generated largely due to quadratic nonlinear interactions.

Bispectral analysis of the rat EEG during various vigilance states

Bispectra were computed to detect phase coupling in the cortical and hippocampal EEG of the rat during various vigilance states. For EEG's recorded from the hippocampus, significant phase coupling was obtained during REM sleep between the frequency components (6-8 Hz) associated with theta rhythm.