Novel approaches to effectively reduce noise in data recorded from multi-trial physiology experim... more Novel approaches to effectively reduce noise in data recorded from multi-trial physiology experiments have been investigated using two-dimensional filtering methods, adaptive Wiener filtering and reduced update Kalman filtering. Test data based on signal and noise model consisting of different conditions of signal components mixed with noise have been considered with filtering effects evaluated using analysis of frequency coherence and of time-dependent coherence. Various situations that may affect the filtering results have been explored and reveal that Wiener and Kalman filtering can considerably improve the coherence values between two channels of multi-trial data and suppress uncorrelated components. We have extended our approach to experimental data: multi-electrode array (MEA) local field potential (LFPs) recordings from the inferotemporal cortex of sheep and LFP vs. electromyogram (LFP-EMG) recording data during resting tremor in Parkinson's disease patients. Finally general procedures for implementation of these filtering techniques are described.
Various time-frequency methods have been used to study the time-varying properties of non-station... more Various time-frequency methods have been used to study the time-varying properties of non-stationary neurophysiological signals. In the present study, a time-frequency coherence using continuous wavelet transform (CWT) together with its confidence intervals are proposed to evaluate the correlation between two non-stationary processes. A systematic comparison between approaches using CWT and short-time Fourier transform (STFT) is carried out. Simulated data are generated to test the performance of these methods when estimating time-frequency based coherence. Surprisingly and in contrast to the common belief, the coherence estimation based upon CWT does not always supersede STFT. We suggest that a combination of STFT and CWT would be most suitable for analysing non-stationary neural data. In both frequency and time domains, methods to test whether there are two coherent signals presented in recorded data are presented. Our approach is then applied to the electroencephalogram (EEG) and surface electromyogram (EMG) during wrist movements in healthy subjects and the local field potential (LFP) and surface EMG during resting tremor in patients with Parkinson's disease. A software package including all results presented in the current paper is available at
Novel approaches to effectively reduce noise in data recorded from multi-trial physiology experim... more Novel approaches to effectively reduce noise in data recorded from multi-trial physiology experiments have been investigated using two-dimensional filtering methods, adaptive Wiener filtering and reduced update Kalman filtering. Test data based on signal and noise model consisting of different conditions of signal components mixed with noise have been considered with filtering effects evaluated using analysis of frequency coherence and of time-dependent coherence. Various situations that may affect the filtering results have been explored and reveal that Wiener and Kalman filtering can considerably improve the coherence values between two channels of multi-trial data and suppress uncorrelated components. We have extended our approach to experimental data: multi-electrode array (MEA) local field potential (LFPs) recordings from the inferotemporal cortex of sheep and LFP vs. electromyogram (LFP-EMG) recording data during resting tremor in Parkinson's disease patients. Finally general procedures for implementation of these filtering techniques are described.
Various time-frequency methods have been used to study the time-varying properties of non-station... more Various time-frequency methods have been used to study the time-varying properties of non-stationary neurophysiological signals. In the present study, a time-frequency coherence using continuous wavelet transform (CWT) together with its confidence intervals are proposed to evaluate the correlation between two non-stationary processes. A systematic comparison between approaches using CWT and short-time Fourier transform (STFT) is carried out. Simulated data are generated to test the performance of these methods when estimating time-frequency based coherence. Surprisingly and in contrast to the common belief, the coherence estimation based upon CWT does not always supersede STFT. We suggest that a combination of STFT and CWT would be most suitable for analysing non-stationary neural data. In both frequency and time domains, methods to test whether there are two coherent signals presented in recorded data are presented. Our approach is then applied to the electroencephalogram (EEG) and surface electromyogram (EMG) during wrist movements in healthy subjects and the local field potential (LFP) and surface EMG during resting tremor in patients with Parkinson's disease. A software package including all results presented in the current paper is available at
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Papers by Yang Zhan