Dimensional complexity and spectral properties of the human sleep EEG

Shen, Yan; Olbrich, Eckehard; Achermann, Peter; Meier, P. F.

doi:10.1016/s1388-2457(02)00338-3

Cited by 110 publications

(85 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The DA, as an expansion of the correlation dimension, is a measure of the dimensionality (and thus the complexity) of the space occupied by a set of points; the coordinates of each point correspond to a series of signal voltage values (SI Methods) (21)(22)(23). The DA quantifies the amount of correlated information within a signal, which depends on the number of frequencies constituting this signal and on their phase relationships.…”

Section: Resultsmentioning

confidence: 99%

“…who was blind to clinical data and positions of cortical recording sites. Sleep scoring was based on analysis of the cortical activity on 3-16 intracortical contacts per subject To characterize cerebral activity, we used a nonlinear time series analysis and considered the coefficient of DA (22), based on and derived from the dimensional complexity approach (SI Methods) (23,47). The nonlinear approach has been applied to EEG signals in several domains, mainly in sleep research where it has been validated against conventional spectral measures (references are given in SI Methods).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Thalamic deactivation at sleep onset precedes that of the cerebral cortex in humans

Magnin

Rey

Bastuji

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

195

147

View full text Add to dashboard Cite

Thalamic and cortical activities are assumed to be time-locked throughout all vigilance states. Using simultaneous intracortical and intrathalamic recordings, we demonstrate here that the thalamic deactivation occurring at sleep onset most often precedes that of the cortex by several minutes, whereas reactivation of both structures during awakening is synchronized. Delays between thalamus and cortex deactivations can vary from one subject to another when a similar cortical region is considered. In addition, heterogeneity in activity levels throughout the cortical mantle is larger than previously thought during the descent into sleep. Thus, asynchronous thalamocortical deactivation while falling asleep probably explains the production of hypnagogic hallucinations by a still-activated cortex and the common self-overestimation of the time needed to fall asleep.A bundant electrophysiological and functional imaging data have revealed that sleep-related brain activity is not the result of a global deactivation of cerebral structures but rather is a multifocal process associated with local changes in brain activities (1-10). Examples of such functional heterogeneities are, among others, the fronto-occipital gradient in cortical activity during sleep (1, 2), the preponderant fronto-parietal localization of sleep spindles (3, 4), and interhemispheric imbalanced activity (5, 6). So far, very few studies have addressed the time course of these regional differences during transitions between vigilance states (11)(12)(13)(14), and most of these studies were based on scalp recordings performed during stable periods of wakefulness or sleep. Although reports that favor some asynchrony of sleep-onset activity between the different cortical areas are accumulating, there still is a firm belief that thalamic and cortical activities are tightly coupled, at both the cellular and integrative level, during wakefulness and sleep (15)(16)(17)(18). Recent intracranial data in humans, however, indicate that, during both paradoxical (rapid eye movement) sleep and sleep stage 2, thalamic and cortical activities may alternate periods of coupling and decoupling (19,20). In this context, the question is whether the dynamics of the neuronal deactivation that characterizes the transition from wakefulness to sleep is identical in thalamus and cortex, or, conversely, whether transient decoupling may occur at this transition time that would suggest different sleep-onset timing in these two structures. The opportunity to record thalamic and cortical activities simultaneously in epileptic patients chronically implanted with intracerebral electrodes allowed us to address this issue. In contrast to the generally accepted view that thalamic and cortical activities are tightly locked along the different vigilance states, we found that the thalamic activity most often decreased to sleep levels several minutes before the cortical activity started to abate. This finding suggests that the cortex remains neurophysiologically awake but decoupled from thalamic i...

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Thalamic deactivation at sleep onset precedes that of the cerebral cortex in humans

Magnin

Rey

Bastuji

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

195

147

View full text Add to dashboard Cite

show abstract

“…Data were stored on a PC with a resolution of 128 Hz. For details of the recording and signal conditioning see [6]. Sleep stages were scored visually according to the criteria of Rechtschaffen and Kales [5].…”

Section: Methodsmentioning

confidence: 99%

“…In certain cases it might be possible to transform a linear model with time dependent parameters into a stationary nonlinear model by increasing the model order m. For time series of human sleep EEG, the surrogate data analysis revealed evidences for a rejection of the null hypothesis of a linear stochastic process (see e.g. [2,4,6]). These analyzes were performed on segments with a length larger than 10s (16 s in [4] and 20.5, 41, 82 and 164 s in [2]).…”

Section: Introductionmentioning

confidence: 99%

Dynamics of human sleep EEG

2003

Self Cite

View full text Add to dashboard Cite

Several investigators of EEG time series reported a rejection of the null hypothesis of linear stochastic dynamics for epochs longer than 10 s. We examine whether this rejection is related to nonlinearity or to nonstationarity. Our approach is a combination of autoregressive modeling and surrogate data testing. It is shown that the fraction of subsegments, for which the null hypothesis has to be rejected increases with the length of the subsegments and can be related to fluctuations of the AR-coefficients on time scales in the range from 2-30 s.

show abstract

“…(8) Select a sample from training set. (9) Decompose the sample to L using W. (10) Calculate wavelet coefficients in the corresponding best basis B. (11) Calculate Max, Min, Mean and Standard deviation of the wavelet coefficients to form a 4-dimension feature.…”

Section: B Best Basis-based Wavelet Packet Entropymentioning

confidence: 99%

A New Improved Feature Extraction Method in Memory EEG Data

Luo¹,

Li²,

Zhang³

2015

Proceedings of the 2015 Joint International Mechanical, Electronic and Information Technology Conference

View full text Add to dashboard Cite

Abstract. various papers and conferences about EEG data can be found at present. There are various feature extraction methods reviewed oversimplified in section one, such as zerocrossing, low zero-crossing rates, coherence analysis, subspace methods, the mean absolute amplitude, standard variance, kurtosis and so on. The feature extraction methods such as self-produced mother wavelet feature extraction method, best basis-based wavelet packet entropy feature extraction, empirical mode decomposition and non-linear feature extraction using correlation dimension and Hurst exponent are detailed introduced in section two. Those feature extraction methods are complex and limited, which often used in some specific fields. In this paper, a new feature extraction is proposed named incremental value, which considers the changes in brain waves. Next LDA and classification tree are used to analyze the results of feature extraction and to predict with unequal memory error compared with the feature extraction methods, such as mean absolute amplitude, standard variance and kurtosis. The method that we proposed is concise and accurate than other methods.

show abstract

Dimensional complexity and spectral properties of the human sleep EEG

Cited by 110 publications

References 34 publications

Thalamic deactivation at sleep onset precedes that of the cerebral cortex in humans

Thalamic deactivation at sleep onset precedes that of the cerebral cortex in humans

Dynamics of human sleep EEG

A New Improved Feature Extraction Method in Memory EEG Data

Contact Info

Product

Resources

About