No effect of cigarette smoking on electroencephalographic nonlinearity.

The Gaussian surrogate-date procedure was applied to the measurement of the effect of cigarette smoking on the dimensional complexity of normal, resting EEG. Evidence of significant nonlinearity in the EEG was obtained, replicating previous results. However, unlike EEG dimensional complexity, EEG nonlinearity (difference between original and surrogate data) was not affected by smoking. This indicates that, under resting conditions, smoking/nicotine may have a modulating effect on input from the reticular activating system, with such input having a global, linearizing effect on cortical dynamics. Nonlinear dynamics resulting from intracortical processes appear not to be affected.

Application of dimension estimation and surrogate data to the time evolution of EEG topographic variables.

Dimensional complexity (estimated correlation dimension) was measured for two topographic EEG time series: (a) the time evolution of global field power (GFP) and (b) the time evolution of sequential dissimilarity (SQD) for resting, eyes-closed and eyes-open data. Eyes-closed GFP and eyes-closed/open SQD all had an element of nonlinearity in their dynamics as evidenced by increased dimensional complexity associated with the phase-angle randomization, Gaussian surrogate-data procedure. However, none of the three gave any evidence of being deterministic chaotic processes. Eyes-open GFP dimensional complexity could not be distinguished from a linear-stochastic process.

Dimensional analysis of resting human EEG. II: Surrogate-data testing indicates nonlinearity but not low-dimensional chaos.

Surrogate-data testing has recently been proposed as one way to detect the presence of nonlinearity and low-dimensional chaos in experimental time series. Such testing involves estimating correlation dimension for both the original data and surrogate data from which nonlinearity has been removed. We applied such testing to the same resting, eyes-closed, and eyes-open electroencephalogram (EEG) data set that was originally analyzed using dimension estimation applied only to the original data (Pritchard & Duke, 1992). Two kinds of surrogate-data sets had higher estimated dimension and poorer saturation. This indicates that the normal resting human EEG is nonlinear and therefore not a linear-stochastic system. Because nearly complete saturation at some loci was not differently affected by the surrogate-data procedures, our results also indicate that the normal resting human EEG is high dimensional and does not represent low-dimensional chaos.

On the validity of estimating EEG correlation dimension from a spatial embedding.

We demonstrate by using simulations that spatial embedding of single-variable time series data does not reliably reconstruct state-space dynamics. Instead, correlation dimension estimated from spatially embedded data is largely a measure of linear cross-correlation in the data set. For actual electroencephalographic (EEG) data, we demonstrate a high negative correlation between spatial correlation dimension and the average amount of lag-zero cross-correlation between "nearest-neighbor" embedding channels (the greater the cross-correlation, the lower the dimension). We also show that the essential results obtained from spatially embedding EEG data are also obtained when one spatially embeds across a set of highly cross-correlated stochastic (second-order autoregressive) processes. Although, with appropriate surrogate data, correlation dimension estimated from spatially embedded data detects nonlinearity, its use is not recommended because correlation dimension estimated from temporally embedded data both reconstructs state-space dynamics and, with appropriate surrogate data, detects nonlinearity as well.

Effects of cigarette smoking on EEG spectral-band power, dimensional complexity, and nonlinearity during reaction-time task performance.

Electroencephalogram (EEG) and heart rate (HR) were recorded while individuals performed visual and auditory go/no-go reaction time (RT) tasks. Overnight-abstaining smokers smoked two types of cigarettes in a single morning session. The first type was smoked once and had a nicotine yield of 0.05 mg. Two cigarettes of the second type (1.1 mg) were smoked. Four recordings were made: presmoking, postsmoking 0.05 mg, and postsmoking each 1.1 mg. HR was increased only by the first 1.1-mg cigarette. Smoking both the 1.1-mg cigarettes decreased RT. Smoking the first 1.1-mg cigarette increased EEG power in the beta2 band. A flexible effect of smoking the first 1.1-mg cigarette on EEG dimensional complexity (DCx) was obtained at locus Cz. Specifically, DCx was (a) raised when the presmoking level was low, (b) not affected when the presmoking level was intermediate, and (c) lowered when the presmoking level was high. Surrogate-data testing indicated the presence of nonlinearity in the EEG data that was not affected by smoking. Decreased RT was associated with increased DCx in the visual task only.

Differentiation of alpha coma from awake alpha by nonlinear dynamics of electroencephalography.

The electroencephalogram, as a probe of scalp-recorded electrical activity arising from the human cortex, provides useful information because of its temporal and spatial organization. Recent developments in nonlinear dynamics suggest that an object can be constructed in an n-dimensional space out of a temporal sequence of data such as an EEG signal and that its organization is characterized by the dimensionality of the object (in this case, human brain activity). We have carried out an analysis of a set of alpha coma EEG patterns in comparison to the awake alpha EEG patterns of normal volunteers and patients. Alpha coma recorded from a single channel is visually indistinguishable from normal resting alpha due to its similar frequency spectrum (a broad-band spectrum with 1/f characteristics). Our results show that alpha coma dimensionality, however, differs from that of normal alpha in that it has a greater variability over different temporal segments of EEG. Single channel recordings in 7 patients with alpha coma were differentiable from those of 10 subjects with "normal" EEGs. Through dynamic analysis of the EEG, novel methods of signal extraction from EEG may become evident and applicable to clinical practice.