Ronald E. Barr

Adaptive digital notch filter design on the unit circle for the removal of powerline noise from biomedical signals

Investigates adaptive digital notch filters for the elimination of powerline noise from biomedical signals. Since the distribution of the frequency variation of the powerline noise may or may not be centered at 60 Hz. Three different adaptive digital notch filters are considered. For the first case, an adaptive FIR second-order digital notch filter is designed to track the center frequency variation. For the second case, the zeroes of an adaptive IIR second-order digital notch filter are fixed on the unit circle and the poles are adapted to find an optimum bandwidth to eliminate the noise to a pre-defined attenuation level. In the third case, both the poles and zeroes of the adaptive IIR second-order filter are adapted to track the center frequency variation within an optimum bandwidth. The adaptive process is considerably simplified by designing the notch filters by pole-zero placement on the unit circle using some suggested rules. A constrained least mean-squared algorithm is used for the adaptive process. To evaluate their performance, the three adaptive notch filters are applied to a powerline noise sample and to a noisy EEG as an illustration of a biomedical signal.<<ETX>>

Development and evaluation of a musculoskeletal model of the elbow joint complex

This paper describes the development and evaluation of a musculoskeletal model that represents human elbow flexion-extension and forearm pronation-supination. The length, velocity, and moment arm for each of the eight musculotendon actuators were based on skeletal anatomy and joint position. Musculotendon parameters were determined for each actuator and verified by comparing analytical moment-angle curves with experimental joint torque data. The parameters and skeletal geometry were also utilized in the musculoskeletal model for the analysis of ballistic (rapid-directed) elbow joint complex movements. The key objective was to develop a computational model, guided by parameterized optimal control, to investigate the relationship among patterns of muscle excitation, individual muscle forces, and to determine the effects of forearm and elbow position on the recruitment of individual muscles during a variety of ballistic movements. The model was partially verified using experimental kinematic, torque, and electromyographic data from volunteer subjects performing both isometric and ballistic elbow joint complex movements. This verification lends credibility to the time-varying muscle force predictions and the recruitment of muscles that contribute to both elbow flexion-extension and forearm pronation-supination.

Potential and current density distributions of cranial electrotherapy stimulation (CES) in a four-concentric-spheres model

Cranial electrotherapy stimulation (CES) has been successfully used for treatment of many psychiatric diseases. Its noninvasive nature is its major advantage over other forms of treatments such as drugs. It is postulated that the low electric current of CES causes the release of neurotransmitters. However, the current pathways have not been extensively investigated. In this paper, analytical and numerical methods are used to determine the distribution of potential and current density in a four zone concentric spheres model of the human head when excited by two electrodes diametrically opposite to each other. Because of the azimuthal symmetry, which is assumed in this study, a two-dimensional (2-D) finite difference approximation is derived in the spherical grid. The current density distribution is projected around the center of the model, where the thalamus is modeled as a concentric sphere. All dimensions and electrical properties of the model are adapted from clinical data. Results of this simulation indicate that, in contrast to previous beliefs, a small fraction of the CES current does reach the thalamic area and may facilitate the release of neurotransmitters.

Quantitative analysis of the electroencephalogram during cranial electrotherapy stimulation

OBJECTIVE Normal individuals were used to quantitate electroencephalographic (EEG) changes during concurrent administration of 0.5 and 100 Hz cranial electrotherapy stimulation (CES). METHODS Twelve normal, right-handed males were used in a randomized, double-blind crossover design study. A 3 amplifier system incorporating noise-cancellation was used to collect one channel of EEG (O1-Cz configuration) for 30 min. Either 0.5, 100 Hz, or sham CES treatment was administered for 20 min of each session. Statistical analyses were applied to time- and frequency-domain EEG variables. RESULTS Relative to sham control, 0.5 and 100 Hz CES caused the alpha band mean frequency to shift downward. Additionally, 100 Hz CES also caused a decrease of the alpha band median frequency and beta band power fraction. CONCLUSIONS Both 0.5 and 100 Hz CES provide frequency distribution shifts that suggest beneficial changes in mental state. However, compared to 0.5 Hz CES, 100 Hz CES effected a greater overall change. It is suggested that similar tests be performed on individuals with various behavioral and neurological disorders to determine if comparable EEG changes can be realized and correlated with beneficial effects of CES therapy.

Frequency-domain digital filtering techniques for the removal of powerline noise with application to the electrocardiogram

This paper presents two new local processing frequency-domain methods for the removal of powerline noise from electrophysiological signals. The first is based on an iterative division or a multiplication of a set of frequencies centered at 60 Hz. The second users a basic property of the natural logarithm to smooth the 60-Hz noise. Both methods are intended to reduce powerline noise without affecting the frequency spectrum of the signal in the regions surrounding 60 Hz. For illustration, these local processing methods are applied to artificial and real electrocardiographic (ECG) data and are compared to a fixed IIR notch digital filter which is designed by pole-zero placements on the unit circle. The performance of each method is measured by the error squared, which is the square of the difference between the original noise-free signal and the filtered noisy ECG. Finally, since the two methods are iterative processes, comparison of their rate of convergence to a predefined noise reduction level is considered.

Muscle activity in rapid multi-degree-of-freedom elbow movements: solutions from a musculoskeletal model.

Abstract. The activity of certain muscles that cross the elbow joint complex (EJC) are affected by forearm position and forearm movement during elbow flexion/extension. To investigate whether these changes are based on the musculoskeletal geometry of the joint, a three-dimensional musculotendinoskeletal computer model of the EJC was used to estimate individual muscle activity in multi-degree-of-freedom (df) rapid (ballistic) elbow movements. It is hypothesized that this model could reproduce the major features of elbow muscle activity during multi-df elbow movements using dynamic optimal control theory, given a minimum-time performance criterion. Results from the model are presented and verified with experimental kinematic and electromyographic data from movements that involved both one-df elbow flexion/extension and two-df flexion/extension with forearm pronation/supination. The model demonstrated how the activity of particular muscles is affected by both forearm position and movement, as measured in these experiments and as previously reported by others. These changes were most evident in the flexor muscles and least evident in the extensor muscles. The model also indicated that, for specific one- and two-df movements, activating a muscle that is antagonistic or noncontributory to the movement could reduce the movement time. The major features of muscle activity in multi-df elbow movements appear to be highly dependent on the joints musculoskeletal geometry and are not strictly based on neural influences or neuroanatomical substrates.

Peak-detection algorithm for EEG analysis

A peak-detection method is described for computer analysis of the the electroencephalogramme (EEG). The technique consists of measuring the amplitude and time interval between successive maxima (peaks) and minima (troughs) in the signal. A critical feature of the peak-detection algorithm is the inclusion of an amplitude threshold criterion which eliminates the registration of low-voltage activity riding on EEG waves. The peak-detection procedure permits the formulation of a variety of intra-band and inter-band EEG statistics which can be useful in on-line computer applications. The peak-detection algorithm has been successfully applied to a number of normal and clinical EEG recordings. Although no computer procedure for EEG analysis has yet been universally adopted, the peak-detection algorithm reported in this paper presents a standardised approach which can be used between EEG clinics.

An alpha modulation index for electroencephalographic studies using complex demodulation

An automated technique for measuring the relative amount of amplitude modulation of electroencephalographic (EEG) alpha activity is developed to increase the number of existing tools for differentiating the various types of alpha activity. EEG data collected from 12 normal males is used to characterize alpha modulation frequency characteristics. From these findings, a complex demodulation method is constructed to extract the amplitude modulation envelope of alpha activity from an epoch of EEG data while disregarding both continuous amplitude alpha activity and activity outside the alpha band. A threshold technique is then used to determine the relative amount of modulation contained within the data epoch. This metric is termed the alpha modulation index (AMI). Good correlation (R2=0.86) is found when automated scoring results are compared with manual scoring of physiologic EEG alpha modulation. The flexibility of this technique makes it easily adaptable to other EEG frequency bands and applications.

A Neural Network Approach to Electromyographic Signal Processing for a Motor Control Task

A hybrid modeling structure composed of a one degree of freedom computational musculoskeletal model and a feedforward multi-layer perceptron neural network was used to effectively map electromyography (EMG) from a human exercise trial to muscle activations in a physiologically feasible and accurate fashion. Several configurations of the complete hybrid system were used to map four muscle surface EMGs from a ballistic elbow flexion to normalized muscle activations, estimated individual muscle forces and torque about the joint. The net joint torque was used to train the neural portion of the hybrid system to minimize kinematic error. The model allowed the estimation of the nonobservable parameters: normalized muscle activations and forces which was used to penalize the learning system. With these parameters in the learning equation, our system produced muscle activations consistent with the classic triphasic response present in ballistic movements.

Building anatomical models from CT and MRI scans for orthopedic preoperative planning and custom implant construction

Improved scanning and rapid prototyping methods make it possible to create anatomically correct models for preoperative planning and actual implantation. Methodology and challenges creating these models are discussed.