Thomas C. Ferree

Scalp electrode impedance, infection risk, and EEG data quality

OBJECTIVES Breaking the skin when applying scalp electroencephalographic (EEG) electrodes creates the risk of infection from blood-born pathogens such as HIV, Hepatitis-C, and Creutzfeldt-Jacob Disease. Modern engineering principles suggest that excellent EEG signals can be collected with high scalp impedance ( approximately 40 kOmega) without scalp abrasion. The present study was designed to evaluate the effect of electrode-scalp impedance on EEG data quality. METHODS The first section of the paper reviews electrophysiological recording with modern high input-impedance differential amplifiers and subject isolation, and explains how scalp-electrode impedance influences EEG signal amplitude and power line noise. The second section of the paper presents an experimental study of EEG data quality as a function of scalp-electrode impedance for the standard frequency bands in EEG and event-related potential (ERP) recordings and for 60 Hz noise. RESULTS There was no significant amplitude change in any EEG frequency bands as scalp-electrode impedance increased from less than 10 kOmega (abraded skin) to 40 kOmega (intact skin). 60 Hz was nearly independent of impedance mismatch, suggesting that capacitively coupled noise appearing differentially across mismatched electrode impedances did not contribute substantially to the observed 60 Hz noise levels. CONCLUSIONS With modern high input-impedance amplifiers and accurate digital filters for power line noise, high-quality EEG can be recorded without skin abrasion.

Scaling properties of fluctuations in the human electroencephalogram

The fluctuation properties of the human electroencephalogram time series are studied using detrended fluctuation analysis. For nearly all 128 channels in each of the 28 subjects studied, it is found that the standard deviation of the fluctuations exhibits scaling behaviors in two regions. Topographical plots of the scaling exponents reveal the spatial structure of the nonlinear electrical activities recorded on the scalp. Moment analyses are performed to summarize the global variability across channels. The correlation between the two scaling exponents in each channel is also examined. Two global measures are found that succinctly characterize the overall properties of the fluctuation behaviors of the brain dynamics for each subject. Together they distinguish the stroke subjects from the normal ones with 90% accuracy, suggesting the possibility that this analysis could lead to an effective diagnostic tool.

Computational Rules for Chemotaxis in the Nematode C. elegans

We derive a linear neural network model of the chemotaxis control circuit in the nematode Caenorhabditis elegans and demonstrate that this model is capable of producing nematodelike chemotaxis. By expanding the analytic solution for the network output in time-derivatives of the network input, we extract simple computational rules that reveal how the model network controls chemotaxis. Based on these rules we find that optimized linear networks typically control chemotaxis by computing the first time-derivative of the chemical concentration and modulating the body turning rate in response to this derivative. We argue that this is consistent with behavioral studies and a plausible mechanism for at least one component of chemotaxis in real nematodes.

Robust spatial navigation in a robot inspired by chemotaxis in caenorhabditis elegans

We report on the design and implementation of an autonomous robot that performs phototaxis under the control of a simulated neural network. The mechanical configuration of the robot and its neural network controller are patterned after those believed to produce chemotaxis in the nematode Caenorhabditis elegans. The network is first optimized to produce phototaxis in a simulated, nematode-like robot and then is tested on a real robot. We find that both the simulated and real robot perform reliably, making nearly identical trajectories for similar environments and similar starting conditions. Furthermore, their performance is robust to significant perturbations of the robots locomotion parameters. Finally, we discuss the implicit computational rule that this network uses to control phototaxis. This makes the results intuitive and improves our intuition about control of tactic behavior in two dimensions.

Power-law scaling in human EEG: relation to Fourier power spectrum

Abstract We discuss a method of analyzing spontaneous human EEG time series, which emphasizes scale-independent behavior. We use detrended fluctuation analysis to quantify the temporal fluctuations as a function of window width, and show how power-law scaling behavior is frequently manifest over two distinct temporal ranges. These ranges encompass time scales associated with meaningful aspects of cortical physiology. This paper shows a simple way of quantifying the existence of such scaling behavior, and determining the characteristic time scale which separates the two regions. By making a qualitative connection with the discrete Fourier transform, we show how the violation of scaling between the two regions is associated with the normal human alpha rhythm, but that the existence scale-independent behavior on either side of the alpha rhythm enables a succinct description of the complex dynamics not accessible in the Fourier power spectrum.

The spatial resolution of scalp EEG

Abstract The scalp electroencephalogram (EEG) exhibits spatiotemporal dynamics reflecting synchronous dendritic activity of cortical pyramidal neurons. Recent advances in EEG acquisition and electric head modeling are improving the spatial resolution of scalp EEG, but the skull remains an obstacle. We use lead field theory to quantify the spatial resolution of scalp EEG, contrasting two electrode spacings and two values of skull conductivity. We show that, without cortical constraints, 19-electrode EEG systems have optimal spatial resolution near 22– 37 cm 3 , while 129-electrode systems have 6– 8 cm 3 . These results emphasize the benefits of more electrodes, but also the need for methods of measuring local skull conductivity.

Chemotaxis control by linear recurrent networks

The nematode Caenorhabditis elegans provides an excellent opportunity to study biological computation. This is mainly because it has a very simple neuromuscular system, consisting of only 302 neurons and 95 muscle cells. Anatomical studies have revealed the morphology of every neuron and the location of nearly every electrical and chemical synapse[1], and it has recently become possible to make electrophysiological recordings from identified neurons in C. elegans[2].

Theoretical study of BOLD response to sinusoidal input

This is a theoretical study of a compelling model of blood oxygen level-dependent (BOLD) response dynamics, measured in functional magnetic resonance imaging (fMRI). The novelty of this study involves the way the model is driven sinusoidally, in order to avoid onset and offset transients that pose difficulties in data analysis and interpretation. The driving frequency ranges over the natural time scales of the hemodynamic response (0.01-1 Hz), which also corresponds to the period in typical boxcar stimulus designs. At low stimulus amplitude, the predicted BOLD response is quasi-linear. The amplitude exhibits a mild peak near the modulation frequency 0.1 Hz, and falls rapidly for higher frequencies. The phase lag relative to the stimulus is a monotonically increasing function of the modulation frequency. These findings illustrate the dynamical nature of the BOLD response, and could be used to optimize experimental designs that admit sinusoidal modulation. Higher stimulus amplitude elicits nonlinear behavior characterized by a double peak during the positive deflection of the BOLD response. This finding is particularly interesting, because similar double peaks are seen frequently in BOLD data.

THE GLOBAL EFFECTS OF STROKE ON THE HUMAN ELECTROENCEPHALOGRAM

The scaling properties of the fluctuations of EEG time series are used in an investigation of acute stroke in humans. We use detrended fluctuation analysis to characterize the fluctuations in 10-second time series in terms of two dimensionless scaling exponents. The statistics of these scaling exponents across 129 scalp sites define measures which may be used to distinguish normal subjects from those with acute cerebral ischemia. By their nature, these statistics emphasize the global properties of EEG dynamics. Simulation of a focal anomaly which accurately reproduces the mean scaling exponents for stroke subjects contradicts the data for the variances, which we take as evidence that the effect of stroke on EEG is global.

Inelastic nucleon contributions in (e,e{sup {prime}}) nuclear response functions

We estimate the contribution of inelastic nucleon excitations to the (e,e{sup {prime}}) inclusive cross section in the CEBAF kinematic range. Calculations are based upon parameterizations of the nucleon structure functions measured at SLAC. Nuclear binding effects are included in a vector-scalar field theory, and are assumed to have a minimal effect on the nucleon excitation spectrum. We find that for q{approx_lt}1 GeV the elastic and inelastic nucleon contributions to the nuclear response functions are comparable, and can be separated, but with roughly a factor of 2 uncertainty in the latter from the extrapolation from data. In contrast, for q{approx_gt}2 GeV this uncertainty is greatly reduced but the elastic nucleon contribution is heavily dominated by the inelastic nucleon background. {copyright} {ital 1997} {ital The American Physical Society}