Save Muscle Information–Unfiltered EEG Signal Helps Distinguish Sleep Stages

Liu, Gi Ren; Lustenberger, Caroline; Lo, Yu Lun; Liu, Wen‐Te; Sheu, Yuan Chung; Wu, Hau‐Tieng

doi:10.3390/s20072024

Cited by 7 publications

(5 citation statements)

References 33 publications

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“…In contrast, while the spectral slope performed better than beta power for classifying REM versus NREM ( p = 10 −15 , 73.7% versus 66.8%), it was worse for classifying wake versus NREM ( p < 10 −43 , 75.6% versus 87.2%). Although higher frequency EEG activity may - as others have suggested 33 - be an informative (and often overlooked) feature for distinguishing REM from wake, potentially driven by the EMG content of the high frequency EEG, we did not find evidence that the spectral slope per se is an optimal parameterization for this particular goal. Indeed, here beta or gamma band power alone performed similarly (equivalent results were observed for relative power).…”

Section: Resultscontrasting

confidence: 84%

Sources of variation in the spectral slope of the sleep EEG

Kozhemiako

Mylonas

Pan

et al. 2021

Preprint

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Building on previous work linking changes in the electroencephalogram (EEG) spectral slope to arousal level, Lendner et al. (2021) reported that wake, non rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep exhibit progressively steeper 30-45 Hz slopes, interpreted in terms of increasing cortical inhibition. Here we sought to replicate Lendner et al.’s scalp EEG findings (based on 20 individuals) in a larger sample of 11,630 individuals from multiple cohorts in the National Sleep Research Resource (NSRR). In a final analytic sample of N = 10,255 distinct recordings, there was unambiguous statistical support for the hypothesis that, within individuals, the mean spectral slope grows steeper going from wake to NREM to REM sleep. We found that the choice of mastoid referencing scheme modulated the extent to which electromyogenic or electrocardiographic artifacts were likely to bias 30-45 Hz slope estimates, as well as other sources of technical, device-specific bias. Nonetheless, within individuals, slope estimates were relatively stable over time. Both cross-sectionally and longitudinal, slopes tended to become shallower with increasing age, particularly for REM sleep; males tended to show flatter slopes than females across all states. Although conceptually distinct, spectral slope did not predict sleep state substantially better than other summaries of the high frequency EEG power spectrum (>20 Hz, in this context) including beta band power, however. Finally, to more fully describe sources of variation in the spectral slope and its relationship to other sleep parameters, we quantified state-dependent differences in the variances (both within and between individuals) of spectral slope, power and interhemispheric coherence, as well as their covariances. In contrast to the common conception of the REM EEG as relatively wake-like (i.e. ‘paradoxical’ sleep), REM and wake were the most divergent states for multiple metrics, with NREM exhibiting intermediate profiles. Under a simplified modelling framework, changes in spectral slope could not, by themselves, fully account for the observed differences between states, if assuming a strict power law model. Although the spectral slope is an appealing, theoretically inspired parameterization of the sleep EEG, here we underscore some practical considerations that should be borne in mind when applying it in diverse datasets. Future work will be needed to fully characterize state-dependent changes in the aperiodic portions of the EEG power spectra, which appear to be consistent with, albeit not fully explained by, changes in the spectral slope.

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Section: Resultscontrasting

confidence: 84%

Sources of variation in the spectral slope of the sleep EEG

Kozhemiako

Mylonas

Pan

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In contrast, while the spectral slope performed better than beta power for classifying REM versus NREM (p = 10 -15 , 73.7% versus 66.8%), it was worse for classifying wake versus NREM (p < 10 -43 , 75.6% versus 87.2%). Although higher frequency EEG activity may -as others have suggested (Liu et al, 2020) -be an informative (and often overlooked) feature for distinguishing REM from wake, potentially driven by the EMG content of the high frequency EEG, we did not find evidence that the spectral slope per se is an optimal parameterization for this particular goal. Indeed, here beta alone performed similarly (see Figure 4-1 for results with all classic frequency bands).…”

Section: Potential Non-neural Confounders Of the Spectral Slopecontrasting

confidence: 84%

Sources of Variation in the Spectral Slope of the Sleep EEG

Kozhemiako

Mylonas

Pan

et al. 2022

eNeuro

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“…Toward the other end of this spectrum, during drowsiness, the infra-slow global dynamic may prolong the memory consolidation phase whereas hinder encodings. The SM-to-DMN waves have been found to occur more frequently during various sleep stages and be associated with learning-related features (i.e., the rapid eye movements and possibly Ponto-Geniculo-Occipital (PGO) waves) during rapid eye movement (REM) sleep (59). Though not directly focused on the cascade and waves, recent studies convergingly point out an essential role of infra-slow neural dynamics in learning and memory.…”

Section: Discussionmentioning

confidence: 99%

Propagating cortical waves coordinate sensory encoding and memory retrieval in the human brain

Yang,

Leopold,

Duyn

et al. 2024

Preprint

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Complex behavior entails a balance between taking in sensory information from the environment and utilizing previously learned internal information. Experiments in behaving mice have demonstrated that the brain continually alternates between outward and inward modes of cognition, switching its mode of operation every few seconds. Further, each state transition is marked by a stereotyped cascade of neuronal spiking that pervades most forebrain structures. Here we analyzed large fMRI datasets to demonstrate that a similar switching mechanism governs the operation of the human brain. We found that human brain activity was punctuated every several seconds by coherent, propagating waves emerging in the exteroceptive sensorimotor regions and terminating in the interoceptive default mode network. As in the mouse, the issuance of such events coincided with fluctuations in pupil size, indicating a tight relationship with arousal fluctuations, and this phenomenon occurred across behavioral states. Strikingly, concurrent measurement of human performance in a visual memory task indicated that each cycle of propagating fMRI waves sequentially promoted the encoding of semantic information and self-directed retrieval of memories. Together, these findings indicate that human cognitive performance is governed by autonomous switching between exteroceptive and interoceptive states. This apparently conserved feature of mammalian brain physiology bears directly on the integration of sensory and mnemonic information during everyday behavior.

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Save Muscle Information–Unfiltered EEG Signal Helps Distinguish Sleep Stages

Cited by 7 publications

References 33 publications

Sources of variation in the spectral slope of the sleep EEG

Sources of variation in the spectral slope of the sleep EEG

Sources of Variation in the Spectral Slope of the Sleep EEG

Propagating cortical waves coordinate sensory encoding and memory retrieval in the human brain

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