Oscillatory states in the Electroencephalogram (EEG) reflect the rhythmic synchronous
activation in large networks of neurons. Time-frequency methods quantify the spectral
content of the EEG as a function of time. As such, they are well suited as
tools for the study of spontaneous and induced changes in oscillatory states. We
have used time-frequency techniques to analyze the flow of activity patterns between
two strongly connected brain structures: the entorhinal cortex and the hippocampus,
which are believed to be involved in information storage.
EEG was recorded simultaneously from the entorhinal cortex and the hippocampus
of behaving rats. During the recording, low-intensity trains of electrical
pulses at frequencies between 1 and 40 Hz were applied to the olfactory (piriform)
cortex. The piriform cortex projects to the entorhinal cortex, which then passes
the signal on to the hippocampus. Several time-frequency methods, including the
short-time Fourier transform (STFT), Wigner-Ville distribution (WVD) and multiple
window (MW) time-frequency analysis (TFA), were used to analyse EEG signals.
To monitor the signal transmission between the entorhinal cortex and hippocampus,
the time-frequency coherence functions were used. The analysed results showed that
stimulation-related power in both sites peaked near 15 Hz, but the coherence between
the EEG signals recorded from these two sites increased monotonically with
stimulation frequency.
Among the time-frequency methods used, the STFT provided time-frequency
distributions not only without cross-terms which were present in the WVD, but also
with higher resolutions in both time and frequency than the MW-TFA. The STFT
seems to be the most suitable time-frequency method to study the stimulation-induced
signals presented in this thesis. The MW-TFA, which gives low bias and low variance
estimations of the time-frequency distribution when only one realization of data is
given, is suitable for stochastic and nonstationary signals such as spontaneous EEG.
We also compared the performance of the MW-TFA using two different window functions:
Slepian sequences and Hermite functions. By carefully matching the two window
functions, we found no noticeable difference in time-frequency plane between
them. / Thesis / Master of Engineering (ME)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/22604 |
| Date | 10 1900 |
| Creators | Xu, Yan |
| Contributors | Haykin, Simon, Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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