P. E. Rapp

Re-examination of the evidence for low-dimensional, nonlinear structure in the human electroencephalogram.

We have re-examined single channel EEG data obtained from normal human subjects. In the original analysis, calculation of the correlation dimension with the Grassberger-Procaccia algorithm produced results consistent with and interpretation of low-dimensional behavior. The re-examination suggests that the previous indication of low-dimensional structure was an artifact of autocorrelation in the oversampled signal. Calculations with a variant of the Grassberger-Procaccia algorithm modified to be less sensitive to autocorrelations, and comparison with linear gaussian surrogate data, indicate that these data may be more appropriately modeled by linearly filtered noise. Discriminant analysis further indicates that the correlation dimension is a poor discriminator for distinguishing between EEGs recorded at rest and during periods of cognitive activity. It remains possible that the application of other nonlinear measures or the examination of multichannel EEG data may resolve structures not found in these calculations.

Dynamics of brain electrical activity

SummaryIn addition to providing important theoretical insights into chaotic deterministic systems, dynamical systems theory has provided techniques for analyzing experimental data. These methods have been applied to a variety of physical and chemical systems. More recently, biological applications have become important. In this paper, we report applications of one of these techniques, estimation of a signals correlation dimension, to the characterization of human electroencephalographic (EEG) signals and event-related brain potentials (ERPs). These calculations demonstrate that the magnitude of the technical difficulties encountered when attempting to estimate dimensions from noisy biological signals are substantial. However, these results also suggest that this procedure can provide a partial characterization of changes in cerebral electrical activity associated with changes in cognitive behavior that complements classical analytic procedures.

Dynamics of spontaneous neural activity in the simian motor cortex: The dimension of chaotic neurons

Abstract Single unit recordings of the spontaneous activity of neurons in the precentral and postcentral gyri of the squirrel monkey cortex were obtained and the dimensions of the corresponding attractors were calculated. Two distinct population of neurons including a group of low dimensional neurons were identified.

Phase-randomized surrogates can produce spurious identifications of non-random structure

Abstract Calculations with surrogate variants of original data are used to validate results obtained in dynamical analysis. Three classes of surrogates are now in use: random-shuffle surrogates, random-phase surrogates and Gaussian-scaled random-phase surrogates. In this paper we present an example based on a natural source of random numbers (radioactive decay) in which random-shuffle and Gaussian-scaled random-phase surrogates both correctly identify the random nature of the data while random-phase surrogates give a dramatic, and totally spurious, identification of non-random structure. The application of random-phase surrogates by themselves, without confirmatory calculations using Gaussian-scaled random-phase surrogates, is becoming increasingly common. The results presented here argue against this practice. The first step in the application of symbolic analysis to dynamical data is the partitioning of the data into a finite symbol alphabet. These results also show that appropriately constructed surrogates can be used as a protection against spurious results caused by defective partitioning.

Oscillations in calcium-cyclic AMP control loops form the basis of pacemaker activity and other high frequency biological rhythms

Abstract In this paper theoretical and experimental evidence is presented which indicates that oscillations in internal calcium and cyclic AMP concentrations due to an instability in their common control loops are possible and indeed may be widespread. Further, it is demonstrated that fluctuations in various cellular properties, in particular membrane potential, are a direct consequence of these second messenger oscillations. Given the central importance of calcium and cyclic AMP to the regulation of metabolism, these oscillations would influence most metabolic processes especially rhythmic behaviour. We propose that these oscillations form the basis of several biological rhythms including, potential oscillations in cardiac pacemaker cells, neurones and insulin secreting β-cells, the minute rhythm in smooth muscle, cyclic AMP pulses in Dictyostelium , rhythmical cytoplasmic streaming in Physarum and transepitheliel potential oscillations in Calliphora salivary gland. This model makes possible an explanation of the frequency and amplitude effects of hormones.

Experimental Studies of Chaotic Neural Behavior: Cellular Activity and Electroencephalographic Signals

Deterministic systems can display a form of highly irregular, quasirandom behavior called chaos. Even though the observed behavior is very complex, the systems which generate it can be very simple. Thus, in at least some instances, irregular biological systems may obey a simple, potentially discoverable, deterministic dynamical law. These systems can undergo reversible transitions to and from chaotic dynamics in response to small changes in parameter values. As a long term goal, this form of analysis may suggest more effective responses to disordered behavior in physiological control systems.

Frequency encoded biochemical regulation is more accurate than amplitude dependent control

Abstract It is generally recognised in engineering that encoding information in a frequency provides resistance to degradation by noise and an enhanced precision of control. This paper demonstrates how the same arguments can be applied to biochemical control networks. It shows that the conversion of an analogue demand signal to an oscillation is stable against corruption by noise in the input and even against corruption by certain internal chaotic motions. The paper also argues that intracellular transmission of frequency encoded information is robust against noise. These arguments are proposed as a partial explanation of why so many biological regulatory systems are periodic.

Are there alternatives to traditional polygraph procedures

The most commonly used method for detecting deception is based on the assumption that lies given by a person in response to critical questions posed during a polygraph examination will elicit an identifiable pattern of autonomic reactivity. Critics of this method argue that a polygraph examination cannot detect lying because lying does not produce a distinct physiological response. They assert that the possession of information only the guilty person would be expected to have can be revealed in a polygraph examination, however, by the pattern of autonomic arousal presentation of this information elicits in a person who possesses it. In this article, the position is taken that the dependence of both procedures on autonomic measures diminishes their effectiveness and inhibits the development of alternatives. A few studies are reviewed that suggest that measures of brain electrical activity can be used to infer the possession of information in persons attempting to conceal it

Traumatic Brain Injury Detection Using Electrophysiological Methods

Measuring neuronal activity with electrophysiological methods may be useful in detecting neurological dysfunctions, such as mild traumatic brain injury (mTBI). This approach may be particularly valuable for rapid detection in at-risk populations including military service members and athletes. Electrophysiological methods, such as quantitative electroencephalography (qEEG) and recording event-related potentials (ERPs) may be promising; however, the field is nascent and significant controversy exists on the efficacy and accuracy of the approaches as diagnostic tools. For example, the specific measures derived from an electroencephalogram (EEG) that are most suitable as markers of dysfunction have not been clearly established. A study was conducted to summarize and evaluate the statistical rigor of evidence on the overall utility of qEEG as an mTBI detection tool. The analysis evaluated qEEG measures/parameters that may be most suitable as fieldable diagnostic tools, identified other types of EEG measures and analysis methods of promise, recommended specific measures and analysis methods for further development as mTBI detection tools, identified research gaps in the field, and recommended future research and development thrust areas. The qEEG study group formed the following conclusions: (1) Individual qEEG measures provide limited diagnostic utility for mTBI. However, many measures can be important features of qEEG discriminant functions, which do show significant promise as mTBI detection tools. (2) ERPs offer utility in mTBI detection. In fact, evidence indicates that ERPs can identify abnormalities in cases where EEGs alone are non-disclosing. (3) The standard mathematical procedures used in the characterization of mTBI EEGs should be expanded to incorporate newer methods of analysis including non-linear dynamical analysis, complexity measures, analysis of causal interactions, graph theory, and information dynamics. (4) Reports of high specificity in qEEG evaluations of TBI must be interpreted with care. High specificities have been reported in carefully constructed clinical studies in which healthy controls were compared against a carefully selected TBI population. The published literature indicates, however, that similar abnormalities in qEEG measures are observed in other neuropsychiatric disorders. While it may be possible to distinguish a clinical patient from a healthy control participant with this technology, these measures are unlikely to discriminate between, for example, major depressive disorder, bipolar disorder, or TBI. The specificities observed in these clinical studies may well be lost in real world clinical practice. (5) The absence of specificity does not preclude clinical utility. The possibility of use as a longitudinal measure of treatment response remains. However, efficacy as a longitudinal clinical measure does require acceptable test–retest reliability. To date, very few test–retest reliability studies have been published with qEEG data obtained from TBI patients or from healthy controls. This is a particular concern because high variability is a known characteristic of the injured central nervous system.

The algorithmic complexity of multichannel EEGs is sensitive to changes in behavior

Symbolic measures of complexity provide a quantitative characterization of the sequential structure of symbol sequences. Promising results from the application of these methods to the analysis of electroencephalographic (EEG) and event-related brain potential (ERP) activity have been reported. Symbolic measures used thus far have two limitations, however. First, because the value of complexity increases with the length of the message, it is difficult to compare signals of different epoch lengths. Second, these symbolic measures do not generalize easily to the multichannel case. We address these issues in studies in which both single and multichannel EEGs were analyzed using measures of signal complexity and algorithmic redundancy, the latter being defined as a sequence-sensitive generalization of Shannons redundancy. Using a binary partition of EEG activity about the median, redundancy was shown to be insensitive to the size of the data set while being sensitive to changes in the subjects behavioral state (eyes open vs. eyes closed). The covariance complexity, calculated from the singular value spectrum of a multichannel signal, was also found to be sensitive to changes in behavioral state. Statistical separations between the eyes open and eyes closed conditions were found to decrease following removal of the 8- to 12-Hz content in the EEG, but still remained statistically significant. Use of symbolic measures in multivariate signal classification is described.