Shahjahan Shahid

Application of higher order statistics techniques to EMG signals to characterize the motor unit action potential

The electromyographic (EMG) signal provides information about the performance of muscles and nerves. At any instant, the shape of the muscle signal, motor unit action potential (MUAP), is constant unless there is movement of the position of the electrode or biochemical changes in the muscle due to changes in contraction level. The rate of neuron pulses, whose exact times of occurrence are random in nature, is related to the time duration and force of a muscle contraction. The EMG signal can be modeled as the output signal of a filtered impulse process where the neuron firing pulses are assumed to be the input of a system whose transfer function is the motor unit action potential. Representing the neuron pulses as a point process with random times of occurrence, the higher order statistics based system reconstruction algorithm can be applied to the EMG signal to characterize the motor unit action potential. In this paper, we report results from applying a cepstrum of bispectrum based system reconstruction algorithm to real wired-EMG (wEMG) and surface-EMG (sEMG) signals to estimate the appearance of MUAPs in the Rectus Femoris and Vastus Lateralis muscles while the muscles are at rest and in six other contraction positions. It is observed that the appearance of MUAPs estimated from any EMG (wEMG or sEMG) signal clearly shows evidence of motor unit recruitment and crosstalk, if any, due to activity in neighboring muscles. It is also found that the shape of MUAPs remains the same on loading.

A New Spike Detection Algorithm for Extracellular Neural Recordings

Signals from extracellular electrodes in neural systems record voltages resulting from activity in many neurons. Detecting action potentials (spikes) in a small number of specific (target) neurons is difficult because many neurons, both near and more distant, contribute to the signal at the electrode. We consider some nearby neurons as target neurons (providing a signal) and all the other contributions to the signal as noise. A new algorithm for spike detection has been developed: this applies a cepstrum of bispectrum (CoB) estimated inverse filter to provide blind equalization. This technique is based on higher order statistics, and seeks to find a sequence of event times or delta sequence. We show that the CoB-based technique can achieve a 98% hit rate on an extracellular signal containing three spike trains at up to 0 dB SNR. Threshold setting for this technique is discussed, and we show the application of the technique to some real signals. We compare performance with four established techniques and report that the CoB-based algorithm performs best.

Cepstrum of bispectrum-A new approach to blind system reconstruction

In this paper, an improved approach to blind deconvolution of LTI systems incorporating phase unwrapping is presented. The method can recover a noise-free estimate of the logarithm of the system transfer function which enables reconstruction of the system. The algorithm is fast due to simple computation and accurate as it includes phase unwrapping. The proposed method is compared via simulation with other methods, selected as representative of both bispectrum- and bicepstrum-based techniques. In general, it performs as well as or much better than the other methods considered. The proposed method is also shown to perform well under low signal-to-noise ratios.

Cepstrum of bispectrum spike detection on extracellular signals with concurrent intracellular signals

The new Cepstrum of Bispectrum based spike detection technique (cob) has shown excellent performance on simulated extracellular signals. However with real extracellular signal cob sometimes does not perform as well as we demand. In this research, we propose iterative application of the cob technique which improves the spike detection capability. We assess the performance of iterative cob on 3 types of real extracellular signal whose ground truth was estimated from concurrent intracellular signals from the same target neuron. It is observed that the iterative cob detects a higher number of target neuron’s spikes from the extracellular signal even if the average quality (SNR signal to noise ratio) of the extracellular recording is nearly 0dB. This technique does not make much difference if the SNR of extracellular signal is less than 0 dB. Discussion and Conclusion