Jiaqing Yan

Using Permutation Entropy to Measure the Changes in EEG Signals During Absence Seizures

In this paper, we propose to use permutation entropy to explore whether the changes in electroencephalogram (EEG) data can effectively distinguish different phases in human absence epilepsy, i.e., the seizure-free, the pre-seizure and seizure phases. Permutation entropy is applied to analyze the EEG data from these three phases, each containing 100 19-channel EEG epochs of 2 s duration. The experimental results show the mean value of PE gradually decreases from the seizure-free to the seizure phase and provides evidence that these three different seizure phases in absence epilepsy can be effectively distinguished. Furthermore, our results strengthen the view that most frontal electrodes carry useful information and patterns that can help discriminate among different absence seizure phases.

Use of functional near-infrared spectroscopy to evaluate the effects of anodal transcranial direct current stimulation on brain connectivity in motor-related cortex

Abstract. Transcranial direct current stimulation (tDCS) is a noninvasive, safe and convenient neuro-modulatory technique in neurological rehabilitation, treatment, and other aspects of brain disorders. However, evaluating the effects of tDCS is still difficult. We aimed to evaluate the effects of tDCS using hemodynamic changes using functional near-infrared spectroscopy (fNIRS). Five healthy participants were employed and anodal tDCS was applied to the left motor-related cortex, with cathodes positioned on the right dorsolateral supraorbital area. fNIRS data were collected from the right motor-related area at the same time. Functional connectivity (FC) between intracortical regions was calculated between fNIRS channels using a minimum variance distortion-less response magnitude squared coherence (MVDR-MSC) method. The levels of Oxy-HbO change and the FC between channels during the prestimulation, stimulation, and poststimulation stages were compared. Results showed no significant level difference, but the FC measured by MVDR-MSC significantly decreased during tDCS compared with pre-tDCS and post-tDCS, although the FC difference between pre-tDCS and post-tDCS was not significant. We conclude that coherence calculated from resting state fNIRS may be a useful tool for evaluating the effects of anodal tDCS and optimizing parameters for tDCS application.

A comparison of different synchronization measures in electroencephalogram during propofol anesthesia

Abstract Electroencephalogram (EEG) synchronization is becoming an essential tool to describe neurophysiological mechanisms of communication between brain regions under general anesthesia. Different synchronization measures have their own properties to reflect the changes of EEG activities during different anesthetic states. However, the performance characteristics and the relations of different synchronization measures in evaluating synchronization changes during propofol-induced anesthesia are not fully elucidated. Two-channel EEG data from seven volunteers who had undergone a brief standardized propofol anesthesia were then adopted to calculate eight synchronization indexes. We computed the prediction probability (PK) of synchronization indexes with Bispectral Index (BIS) and propofol effect-site concentration (Ceff) to quantify the ability of the indexes to predict BIS and Ceff. Also, box plots and coefficient of variation were used to reflect the different synchronization changes and their robustness to noise in awake, unconscious and recovery states, and the Pearson correlation coefficient (R) was used for assessing the relationship among synchronization measures, BIS and Ceff. Permutation cross mutual information (PCMI) and determinism (DET) could predict BIS and follow Ceff better than nonlinear interdependence (NI), mutual information based on kernel estimation (KerMI) and cross correlation. Wavelet transform coherence (WTC) in α and β frequency bands followed BIS and Ceff better than that in other frequency bands. There was a significant decrease in unconscious state and a significant increase in recovery state for PCMI and NI, while the trends were opposite for KerMI, DET and WTC. Phase synchronization based on phase locking value (PSPLV) in δ, θ, α and γ1 frequency bands dropped significantly in unconscious state, whereas it had no significant synchronization in recovery state. Moreover, PCMI, NI, DET correlated closely with each other and they had a better robustness to noise and higher correlation with BIS and Ceff than other synchronization indexes. Propofol caused EEG synchronization changes during the anesthetic period. Different synchronization measures had individual properties in evaluating synchronization changes in different anesthetic states, which might be related to various forms of neural activities and neurophysiological mechanisms under general anesthesia.

Towards adaptive synchronization measurement of large-scale non-stationary non-linear data

Synchronization measurement of non-stationary nonlinear data is an ongoing problem in the study of complex systems, e.g., neuroscience. Existing methods are largely based on Fourier transform and wavelet transform, and there is a lack of methods capable of (1) measuring the synchronization strength of multivariate data by adapting to non-stationary, non-linear dynamics, and (2) meeting the needs of sophisticated scientific or engineering applications. This study proposes an approach that measures the synchronization strength of bivariate non-stationary nonlinear data against phase differences. The approach (briefed as AD-PDSA) relies on adaptive algorithms for data decomposition. A parallelized approach was also developed with general-purpose computing on the graphics processing unit (GPGPU), which largely improved the scalability of data processing, namely, GAD-PDSA. We developed a model on the basis of GAD-PDSA to verify its effectiveness in analyzing multi-channel, event-related potential (ERP) recordings against Daubechies (DB) wavelet with reference to the Morlet wavelet transform (MWT). GAD-PDSA was applied to an EEG dataset obtained from epilepsy patients, and the synchronization analysis manifested an effective indicator of epileptic focus localization. We designed an approach to measure the synchronization strength of non-stationary nonlinear data against phase differences.We demonstrated that the synchronization analysis was an effective indicator of an epileptic focus location.We developed a parallelized approach with general-purpose computing on the graphics processing unit (GPGPU), and it largely improved the scalability of data processing.

Empirical mode decomposition-based motion artifact correction method for functional near-infrared spectroscopy

Abstract. Functional near-infrared spectroscopy (fNIRS) is a promising technique for monitoring brain activity. However, it is sensitive to motion artifacts. Many methods have been developed for motion correction, such as spline interpolation, wavelet filtering, and kurtosis-based wavelet filtering. We propose a motion correction method based on empirical mode decomposition (EMD), which is applied to segments of data identified as having motion artifacts. The EMD method is adaptive, data-driven, and well suited for nonstationary data. To test the performance of the proposed EMD method and to compare it with other motion correction methods, we used simulated hemodynamic responses added to real resting-state fNIRS data. The EMD method reduced mean squared error in 79% of channels and increased signal-to-noise ratio in 78% of channels. Moreover, it produced the highest Pearson’s correlation coefficient between the recovered signal and the original signal, significantly better than the comparison methods (p<0.01, paired t-test). These results indicate that the proposed EMD method is a first choice method for motion artifact correction in fNIRS.

High throughput wavelet coherence analysis of neural series

The real-time estimation of coherence amongst neural signals from different brain areas is a critical issue in understanding brain functions. The wavelet coherence based on Monte Carlo method (MC-WTC) is effective in measuring the time-frequency coherence of neural signals, but it generates large intermediate data and could not be applied in real-time neural signal analysis. We develop a parallelised MC-WTC method with general-purpose computing on the graphics processing unit (GPGPU), namely G-MC-WTC, which speeds up the calculations using the CUDA toolkit. Simulation data showed that it can improve the runtime performance by almost 200 times. This method has been applied to a visual-auditory EEG data and to obtain the coherence information between different brain areas in real time. The result revealed a coherence difference in θ band at left temporal lobe. This method may become a useful tool for studying the cooperation mechanisms of brain regions in cognitive processes.

A Robust Coherence-Based Brain Connectivity Method with an Application to EEG Recordings

Electroencephalography (EEG) is important for epileptic loci identification. Recently, brain connection of EEG has been proved to be an efficient approach to identify epileptic loci. Magnitude square coherence (MSC) is one of the traditional methods to calculate the coherence index. In this study, we propose a robust wavelet spectrum coherence (CWSC) algorithm to calculate the brain connection from EEG recordings, and apply it for epileptic loci identification. The coherence index during ictal and inter-ictal stage for one patient with temporal lobe epilepsy was used to demonstrate the performance of the proposed method. This case study showed that the proposed method had better anti-noise capability. In comparison with the MSC approach, it could be more suitable for localization of epileptic loci with noisy EEG signals.