Abstract
Due to their non-stationarity, ERP signals are difficult to study. The concept of cointegration might overcome this problem and allow for the study of the co-variability between whole ERP signals. In this context cointegration factor is defined as the ability of an ERP signal to co-vary with other ERP signals. The aim of the present study was to investigate whether the cointegration factor is dependent on different EMF conditions and gender, as well as the locations of the electrodes on the scalp. The findings revealed that women have a significantly higher cointegration factor than men, while all subjects have increased cointegration factors in the presence of EMF. The cointegration factor is location dependent, creating a distinct cluster of high coin- tegration capacity at the central and lateral electrodes of the scalp, in contrast to clusters of low cointegration capacity at the anterior and posterior electrodes There seem to be distinct similarities of the present findings with those from standard methodologies of the ERPs. In conclusion cointegration is a promising tool towards the study of functional interactions between different brain locations.
Highlights
The electroencephalogram (EEG) is a non-invasive technique, providing a millisecond by millisecond readout of the brain’s processing of information, and is relatively inexpensive to implement
In order to further qualify the effect of electromagnetic fields (EMF) and gender on Cointegration Factor (CF) for each electrode individually, the CFs of the 15 electrodes were subjected to MANOVA with EMF condition, gender and their interaction as independent factors
The cointegration factor was defined as the ability of Event-related potentials (ERPs) signals to co-vary in time
Summary
The electroencephalogram (EEG) is a non-invasive technique, providing a millisecond by millisecond readout of the brain’s processing of information, and is relatively inexpensive to implement. The event-related activity is small and is, difficult to view in the single trial. It is usually covered by the ongoing ‘spontaneous’ EEG. ERPs techniques overcome this initially poor signal to noise ratio by averaging across many trials, typically from about 15 to several hundred [1]. Both EEG and ERP signals are time series. Stationary EEG signals are successfully analyzed in the frequency domain using Fourier transformations [2]. Fourier transformations cannot be applied on the non-stationary ERP signals. There are a number of alternative approaches that overcome the issue of non-stationarity, such as windowed Fourier [4,5] and wavelet analysis [6,7]
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