Wavelet Coherence and Its Application in Analyzing Auditory and Motor Task Event-Related Potentials

WU Jie，ZHANG Ning，YANG Zhuo，ZHANG Tao, Wavelet coherence and its application in analyzing auditory and motor task event-related potentials.

This paper has been accepted by ACTA BIOPYSICA SINICA，here is its abstract.

Related links:Tips about Wavelet , Wavelet Coherence Method,A Vivid Example to Show How Wavelet Coherence Works, Some Concepts about ERP signals ,Wavelet and EEG Signals. These links can help to gain further information about this paper.

Abstract:
Wavelet coherence method is applied in analyzing single trial of ERP (event-related potential). There are three groups of experiments: auditory single task, motor single task 1 and motor single task 2. Data from 12 participants is analyzed around 40 Hz by wavelet coherence method and the coherence values between prefrontal area and other areas in the brain are calculated. It is found that the coherence values in motor tasks are larger than those in auditory task and there are significantly differences. Furthermore, in different tasks, the distributions of the coherence values are obviously different, and the values are changing in particularly ways according the varying of the time. This analysis indicates that wavelet coherence method has its advantages in investigating short time EEG signals.

Brief descriptions:


The coherence values, around 40Hz between prefrontal area and other areas in the cerebral cortex, were measured. It was found that the coherence values in the MST (Motor Single Task) are larger than that in the AST (Auditory Single Task) with significant differences.Brain dealing with complicated tasks can have more information to process, and there should be more information communication between different areas of the brain. This can be denoted by coherence values.


Wavelet Coherence Values along the time axis. The colors in the images indicate the coherence values between prefrontal area and other areas in the cerebral cortex around 40Hz(the relationship between the color and the value is shown in the color-bar).large coherence values exactly locate in Auditory Cortex at temporal lobe in AST conditions, whilst in MST conditions, the big values are in motor cortex which is in parietal area.

The data show that the wavelet methods calculations of non-stationary signals, compared to the Fourier methods, can characterize the time-frequency features of neural mechanisms underlying cognitive control. Furthermore, wavelet approach can provide higher resolution in both temporal and spatial scales and can be applied in analyzing other physiological signals.

Main reference:
Lachaux, J.-P., et al., Estimating the time-course of coherence between single-trial brain signals: an introduction to wavelet coherence. Neurophysiol Clin., 2002. 32: p. 157-174.

Wavelet Packet Transform

The wavelet packet transform (WPT) represents a generalization of the wavelet methods and it has recently been applied to various science and engineering fields with great success. Here are some tips about WPT from Matlab tutorial.

From WT to WPT:

In wavelet analysis, a signal is split into an approximation and a detail. The approximation is then itself split into a second-level approximation and detail, and the process is repeated. For an n-level decomposition, there are n+1 possible ways to decompose or encode the signal. More basic information about wavelet can be got here.

In wavelet packet analysis, the details as well as the approximations can be split.


The wavelet decomposition tree is a part of this complete binary tree. For instance, wavelet packet analysis allows the signal S to be represented as A1 + AAD3 + DAD3 + DD2.

Here is a sample transform of an EEG signal in our lab. The original waveform was decomposed into five waveforms in WPT method, db5 mother wavelet was employed and 7 levels decomposition was taken. We can see DELTA, THETA, ALPHA, BETA, GAMMA waves in this figure.

Wavelet and EEG Signals

EEG signals are wildly studied to determine how patterns contained within them might reflect control by the nervous system. Generally, they are investigated with frequency analysis based on FFT. However, EEG data sets (including signals in ERP experiments) are non-stationary in both time and space. And FFT is not good at analyzing waveforms with this feature，see this article.

The main advantage of wavelet transform (WT) in the analysis of EEG signals is that it allows accurate decomposition of a neuro-electrical record into a set of component waveforms (detail functions). These detail functions can isolate all scales of waveform structure, from the largest to smallest pattern of variation in time and space that is available in the signal. Consequently WT provides flexible control over the resolution with which different activities and events contained in the neuroelctrical signal can be localized in time, space and scale. This leads to several important applications like(Samar, et al., 1999): (a) noise filtering and signal separation; (b) preprocessing neuroelectric data; (c) neuroelectric signal compressions; (d) spike and transients detections; (e) component and event detection; (f) time-scale and space-scale analysis of neuroelectric waveforms; among others(Rosso, et al., 2006).

Many details have been reviewed by Samar(Samar, et al., 1999).


Rosso, O.A., Martin, M.T., Figliola, A., Keller, K. and Plastino, A. (2006) EEG analysis using wavelet-based information tools, Journal of Neuroscience Methods, 153, 163-182.
Samar, V.J., Bopardikar, A., Rao, R. and Swartz, K. (1999) Wavelet analysis of neuroelectric waveforms: a conceptual tutorial, Brain Lang, 66, 7-60.

What is EEG?

Electroencephalography is the neurophysiologic measurement of the electrical activity of the brain by recording from electrodes placed on the scalp or, in special cases, subdurally or in the cerebral cortex. The resulting traces are known as an electroencephalogram (EEG) and represent a summation of post-synaptic potentials from a large number of neurons. These are sometimes called brainwaves, though this use is discouraged, because the brain does not broadcast electrical waves.The EEG is a brain function test, but in clinical use it is a "gross correlate of brain activity". Electrical currents are not measured, but rather voltage differences between different parts of the brain.

The basic knowledge above comes from Wikipedia. It can give you a concept: EEG is a kind of signal from brain.

There are some details about the EEG data in our lab in Nankai University.

Some of the EEG data come from brains of human beings, including epileptic EEG signals from patients in hospitals, EEG from healthy people and ERP (Event Related Potentials) raw data from cognization research centers. Most of these signals have been collected in a database, you can see the articles with the tag database for more introduction.

Some EEG data were obtained from rats, which were healthy or in ill condition (ischemia model or epilepsy model). Signals from animals may be much more easier for us to detect the things happening in the brain. We recorded the data from the hippocampus and also the cortex.

Obviously, our purpose of calculating these data is to investigate the brain and to find out different features in the nerve systems in different physiology conditions. The analysis methods will be discussed in other articles.

ERPDB: a Database of ERP Data for Neuroinformatics Research

Event-related potential (ERP) is the measurement of the brain's electrical activity in response to different types of events. These signals are useful for learning how information is processed in the human brain. Being a neuroinformatics laboratory, we deal with lots of ERP signals, which come from various research programs. Different programs have different experiment designs, different parameters settings, and different data formats. These could be big problems for our study. For this reason, arranging the data and constructing a database to store them is quite necessary.

ERPDB contains the information of the participants and the rearranged signals.The data have been transformed into more organized data, which are more convenient for further computation and analysis. Also, new data can be easily added into the database according to our standard. This database is available on the web.



Publications:
Qinghong Yan, Ning Zhang, Jie Wu. The Constitution of ERP Database Based on SQL Server2000. The 20th International CODATA Conference (CODATA2006 ABSTRACT，China，Beijing):2006, 308
QingHong Yan, Ning Zhang, Jie Wu, Tao Zhang. ERPDB: an integrated database of ERP datafor neuroinformatics research, Data science journal, 2007

An Epilepsy EEG Database

Research on Epilepsy EEG is a big part in our lab-- Biocomputation and Neuroinformatics Laboratory. By analyzing the EEG signals in rat models, we aim to find some way to predict seizures before they begin, which could be helpful for treatment of epilepsy. Some samples of human epilepsy EEG were also collected from hospitals and research centers. For the data formats are distinctive, there was some problems to analyze them together.

Being a preparation for a further investigation, an EEG database was constituted. EEG signals from healthy and epilepsy humans together with the annotations were collected and standardized in this database.

The website of this database is shown as below.



Publication: Zhang Ning,Wu Jie,Yang Zhuo,et al,Constitution of an EEG database based on SQL server. Chinese Chinese High Technology Letters,2006,16(12): 48-52.